Image Scientist Career Path Guide

An Image Scientist specializes in the acquisition, processing, analysis, and interpretation of digital images across various domains such as healthcare, remote sensing, manufacturing, and multimedia. They blend expertise in computer vision, image processing algorithms, and domain-specific knowledge to extract meaningful information from images, optimize image quality, and develop innovative solutions leveraging visual data.

15%

growth rate

$105,000

median salary

remote-friendly

📈 Market Demand

Low

High

The demand for Image Scientists remains high, fueled by expanding AI applications across healthcare, manufacturing, autonomous vehicles, and environmental monitoring. As organizations seek to leverage visual data for competitive advantage, expertise in image analysis and computer vision is increasingly vital, making this a growing and dynamic career path.

🇺🇸 Annual Salary (US, USD)

70,000—140,000

Median: $105,000

Entry-Level: $80,500
Mid-Level: $105,000
Senior-Level: $129,500

Top 10% of earners in this field can expect salaries starting from $140,000+ per year, especially with specialized skills in high-demand areas.

Core Functions of the Image Scientist Role

Image Scientists play a crucial role in the rapidly evolving intersection of computer vision, artificial intelligence, and data science. Their work revolves around extracting, enhancing, and interpreting visual information from diverse image data sources, encompassing everything from medical MRI scans and satellite imagery to product inspection cameras and cultural heritage digitization.

Their expertise covers both theoretical and practical aspects of imaging technologies. They develop and adapt sophisticated algorithms to improve image clarity, identify subtle patterns, or transform raw data into actionable insights. This requires a deep understanding of signal processing methods, statistical modeling, and the physics of image acquisition systems.

Collaboration is a significant part of this role, as Image Scientists often work with engineers, domain experts, and software developers to tailor image processing pipelines to specific industry needs. For instance, in medicine, the emphasis may be on accurate tumor detection and segmentation, while in remote sensing, it focuses on analyzing ecological changes or urban developments.

The role demands continuous learning given the accelerated progress in machine learning techniques, computational imaging, and sensor technology. Image Scientists are called upon not only to process images but also to innovate new methodologies that push the boundaries of what is possible with visual data. Whether it is automating quality control on a production line or enhancing forensic image analysis, their work drives efficiency, accuracy, and new discoveries.

Key Responsibilities
Develop and implement image processing algorithms for enhancement, segmentation, registration, and classification.
Analyze large datasets of images to extract quantitative and qualitative information relevant to the project goals.
Collaborate with interdisciplinary teams to translate domain-specific requirements into imaging solutions.
Validate and optimize imaging workflows to ensure robustness, accuracy, and scalability.
Research emerging image acquisition technologies and integrate novel sensor data when appropriate.
Design experiments to characterize imaging systems and calibrate sensor outputs.
Write clean, documented code to support reproducibility and hand-off within multidisciplinary teams.
Evaluate machine learning and deep learning methods applied to image analysis challenges.
Publish research findings or technical reports to communicate advancements internally or externally.
Develop visualization tools and dashboards to present image-derived insights to non-technical stakeholders.
Troubleshoot hardware or software issues related to image capture and processing.
Maintain awareness of regulatory and ethical considerations around image data, especially in sensitive sectors like healthcare.
Train junior scientists and interns on core imaging concepts and industry best practices.
Participate in patent filing or intellectual property development based on imaging innovations.
Contribute to proposal writing to secure funding for cutting-edge image science initiatives.

Work Setting

Image Scientists typically work in laboratory or office settings, often within research institutions, healthcare organizations, technology companies, or government agencies. Their environment is highly collaborative, involving regular interaction with software engineers, data scientists, medical professionals, and electrical engineers. Workspaces are generally equipped with high-performance computing resources, specialized software, and sometimes interface hardware such as advanced imaging sensors or microscopes. Depending on the industry, the job may also require occasional fieldwork to collect real-world imaging data, especially in environmental or remote sensing applications. The role necessitates a balance between solitary deep-focus time spent coding or analyzing images and dynamic team meetings discussing project milestones and innovations.

Tech Stack

MATLAB
Python (with libraries such as OpenCV, scikit-image, TensorFlow, PyTorch)
ImageJ/Fiji
NIH Image
TensorFlow and PyTorch for deep learning
ITK (Insight Segmentation and Registration Toolkit)
Cloud computing platforms (AWS, Google Cloud, Azure)
DICOM standards and associated PACS systems
GPU computing frameworks (CUDA, OpenCL)
R (for statistical analysis)
OpenCV
Advanced microscopy software (e.g., Zeiss ZEN, Nikon NIS-Elements)
GIS software (e.g., QGIS, ArcGIS) for geospatial image analysis
Version control tools like Git
Continuous integration/continuous deployment (CI/CD) tools
Docker/Singularity for virtualization
Jupyter Notebooks
SQL and NoSQL databases for image metadata management
Quality assurance tools like Image Quality Assessment (IQA) toolkits

Skills and Qualifications

Education Level

Aspiring Image Scientists typically require a strong academic background in disciplines such as electrical engineering, computer science, physics, biomedical engineering, or applied mathematics. A bachelor's degree is the minimum entry point; however, a master's degree or Ph.D. is usually preferred or required for advanced research roles given the technical complexity and specialized knowledge involved.

Coursework should focus on signal processing, computer vision, machine learning, algorithms, statistical analysis, and software engineering. Hands-on experience with image acquisition devices and practical programming skills are valuable assets. Interdisciplinary coursework related to the specific application area (such as biology for medical imaging or geosciences for remote sensing) enhances an applicant's suitability.

Additional certifications or training in specialized software libraries, data science, or deep learning further strengthen one’s profile. Professional development often includes continuous learning of emerging models and computational techniques, given the fast pace of innovation in this field.

Tech Skills

Image Processing Algorithms Development
Computer Vision Techniques
Machine Learning and Deep Learning
Mathematical Modeling and Statistics
Programming Languages: Python, MATLAB, C++
Signal and Sensor Data Analysis
Medical Image Analysis (DICOM, PACS)
Geospatial Image Interpretation (GIS tools)
3D Image Reconstruction
Data Visualization
High-performance Computing (GPU Utilization)
Software Development and Version Control
Image Quality Assessment Methods
Calibration and Experimental Design
Cloud Computing and Storage Solutions

Soft Abilities

Critical Thinking
Problem Solving
Collaborative Teamwork
Clear Communication
Adaptability to Emerging Technologies
Attention to Detail
Time Management
Project Planning
Creativity in Algorithm Design
Continuous Learning Mindset

Path to Image Scientist

Becoming an Image Scientist starts with securing a strong foundational education in a STEM field, typically electrical engineering, computer science, applied physics, or bioengineering. During undergraduate studies, aspiring candidates should focus on coursework that includes digital signal processing, algorithms, probability, statistics, and machine learning. Complementing theoretical knowledge with programming skills in Python, MATLAB, or C++ is essential.

Building hands-on experience through internships, research assistant positions, or projects centered on image processing or computer vision will provide practical skills highly valued by employers. Engaging in open-source communities or contributing to image analysis projects can demonstrate initiative and technical capabilities.

Pursuing graduate education at the master’s or doctoral level allows deeper specialization and research opportunities in areas like medical imaging, remote sensing, or computational photography. Graduate studies often involve publishing papers, presenting findings at conferences, and working on multidisciplinary teams, which strengthens communication and leadership skills.

Networking with professionals in academia and industry through conferences, workshops, and professional organizations such as IEEE’s Signal Processing or Computer Vision Society can open doors to valuable mentorships and job opportunities.

Staying current with emerging trends by taking additional courses in deep learning frameworks, cloud computing, and specialized imaging hardware is critical. Certification programs in AI, data science, or specific tools like TensorFlow can enhance employability.

Entry-level roles may include positions as research assistants, imaging analysts, or software developers with an imaging focus, progressing into dedicated Image Scientist roles with experience. Continuous learning and adapting to new technological advancements define success and career longevity in this rapidly evolving field.

Required Education

The educational path toward becoming an Image Scientist typically begins with a bachelor's degree in relevant fields such as electrical engineering, computer science, biomedical engineering, or physics, where students acquire foundational knowledge in algorithms, digital signal processing, and programming.

Graduate education is nearly essential for most Image Scientist positions. Master's or Ph.D. programs allow students to focus on specialized courses in image processing, computer vision, machine learning, statistical modeling, and sensor technology. Top research institutions often provide opportunities to work with state-of-the-art imaging systems or participate in cutting-edge projects.

Some universities offer dedicated computer vision or image science degrees, which combine interdisciplinary coursework and research. Elective courses in related fields such as neuroscience, medical imaging, or remote sensing can tailor education toward specific industries.

Professional training and certifications complement formal education. Online platforms like Coursera, edX, and Udacity offer courses on deep learning for computer vision, using TensorFlow, PyTorch, and OpenCV. Certifications in cloud platforms (AWS, Google Cloud) or programming languages also hold value.

Industry workshops and hands-on bootcamps focused on data science or AI for imaging solutions provide practical skills and networking opportunities.

Continuous professional development is critical due to the rapid emergence of new methodologies and hardware in imaging technologies. Attending conferences, reading journals such as IEEE Transactions on Image Processing or Journal of Imaging Science, and collaborating on open-source projects helps Image Scientists remain at the leading edge.

Organizations may also provide specialized internal training on proprietary imaging platforms and regulatory standards, especially in healthcare or manufacturing sectors where compliance is critical.

Career Path Tiers

Junior Image Scientist

Experience: 0-2 years

Entering the field as a Junior Image Scientist involves supporting senior colleagues by performing routine image preprocessing, data annotation, and basic algorithm testing. At this stage, professionals focus on building foundational technical skills like programming in Python and MATLAB, understanding image acquisition methods, and learning to work with established workflows. Expectations include assisting with documentation, contributing to data curation, and gradually taking ownership of small project components under supervision. This role is critical for gaining hands-on experience, understanding pipeline integration, and developing problem-solving abilities.

Mid-Level Image Scientist

Experience: 3-5 years

Mid-Level Image Scientists take on more complex responsibilities such as designing and implementing algorithms for image segmentation, classification, or registration independently. They critically evaluate existing methods and experiment with machine learning or deep learning techniques to optimize performance. Collaboration with cross-functional teams increases to translate domain needs into technical specifications. Mentorship of junior staff and contribution to publications or patents may begin. Mid-level professionals are expected to have strong coding, statistical analysis, and visualization skills, enabling significant impact on project outcomes.

Senior Image Scientist

Experience: 6-10 years

Senior Image Scientists lead research initiatives, architect end-to-end imaging solutions, and provide strategic direction on technological choices. They supervise teams, guide experimental design, and address highly complex imaging challenges by leveraging advanced mathematical models and AI techniques. Responsibilities expand to include interfacing with external collaborators, managing project timelines, and securing funding through grants or corporate partnerships. Senior professionals champion innovation, publish influential research, and help define long-term vision for image science within their organization.

Lead Image Scientist / Principal Scientist

Experience: 10+ years

At the pinnacle, Lead or Principal Image Scientists influence both scientific progress and business strategy. Their work shapes how imaging technologies integrate into products or research agendas. They represent their organizations at international conferences, lead intellectual property development, and mentor across multiple teams. Leadership in defining industry standards, driving collaboration across departments, and directing major investments in imaging infrastructure falls under their purview. These senior figures combine deep technical expertise with exceptional communication and management capabilities.

Global Outlook

The field of image science is thriving worldwide, with significant opportunities emerging across North America, Europe, and Asia-Pacific regions. The United States and Canada boast numerous roles in healthcare imaging, autonomous vehicles, and tech startups that focus on computer vision. Europe offers strong prospects particularly in research institutions within Germany, the Netherlands, and the UK, supported by robust government funding for AI and imaging technologies.

In Asia, countries like China, Japan, and South Korea lead in semiconductor imaging, manufacturing quality control, and smart city applications. These regions heavily invest in integrating AI with imaging to drive industrial automation and public safety.

Emerging markets in India, Brazil, and Eastern Europe are progressively increasing demand for image scientists to build localized solutions in agriculture monitoring, telemedicine, and consumer electronics.

Global collaboration is commonplace, with professionals participating in international consortia, open-source projects, and multi-center clinical trials. Fluency in English and familiarity with international data privacy standards significantly enhance employability. Remote and hybrid working arrangements broaden access to international roles, although specific projects may require local presence to interact with hardware or imaging facilities.

Understanding region-specific applications and regulatory environments empowers image scientists to successfully navigate global career landscapes and contribute to groundbreaking advancements that transcend borders.

Job Market Today

Role Challenges

The Image Scientist profession faces several challenges today, not least the rapid evolution and complexity of imaging technologies that demand continuous skill development. Managing the sheer volume and variability of image data—often from heterogeneous sources—requires sophisticated processing pipelines and robust validation protocols. Ensuring reproducibility, accuracy, and interpretability in AI-driven image analysis remains a critical hurdle, especially when deployed in sensitive fields such as healthcare diagnostics or autonomous driving. Integration of new deep learning models often comes with high computational costs, necessitating specialized hardware and expertise in optimization. Ethical and privacy issues related to image data collection and sharing require vigilance and compliance, particularly with regulations like GDPR or HIPAA. Furthermore, bridging the gap between pure research advancements and scalable industrial applications demands strong interdisciplinary communication and adaptability. Despite these challenges, the field offers exciting problem-solving opportunities alongside significant pressure for innovation under tight timelines.

Growth Paths

Opportunities in image science have expanded dramatically with the proliferation of AI, improved sensor technology, and growing reliance on visual data analysis. Fields such as medical imaging, autonomous systems, remote sensing, and augmented reality depend heavily on skilled Image Scientists to develop intelligent image interpretation methods. Investment in precision medicine fuels demand for accurate medical image analytics, while manufacturing industries increasingly leverage visual inspection systems powered by image science. Startups and tech giants alike are investing in developing novel imaging applications from smart cameras for retail analytics to satellite imagery interpretation for climate monitoring. Cloud-based imaging services and edge computing create fresh avenues for innovation and deployment. Expanding into interdisciplinary collaborations with genetics, robotics, or materials science can broaden career pathways. Up-skilling in cutting-edge techniques like self-supervised learning, 3D imaging, and hyperspectral analysis unlocks even more prospects. In essence, the growth landscape encourages continuous learning and exploration in applied research tied to impactful real-world challenges.

Industry Trends

Current industry trends emphasize AI-driven computer vision developments that automate and enhance image analysis accuracy. Explainable AI models are gaining momentum to build trust and transparency in high-stakes applications. Multimodal data fusion, combining images with other sensor or textual data, is revolutionizing holistic understanding in domains like autonomous driving and medical diagnosis. Edge computing and on-device processing reduce latency and privacy risks, pushing innovations in lightweight image models optimized for mobile or embedded systems. Advances in computational photography and 3D imaging, including LiDAR and volumetric capture, enrich data complexity and utility. Cross-industry collaboration with genomics, neuroscience, and environmental science fosters novel applications. Open-source frameworks and democratized datasets accelerate global research and product development, while the focus on sustainability motivates development of efficient, resource-conscious imaging solutions. The picture for Image Scientists is one where staying abreast of AI, hardware advances, and domain applications will determine success.

A Day in the Life

Morning (9:00 AM - 12:00 PM)

Focus: Data Curation & Algorithm Development

Clean and preprocess incoming image datasets through normalization and enhancement.
Implement or modify image segmentation and classification algorithms.
Conduct initial exploratory data analysis to identify image quality issues.
Review and analyze results from previous algorithm runs to guide parameter tuning.

Early Afternoon (12:00 PM - 3:00 PM)

Focus: Collaboration & Experimentation

Meet with interdisciplinary teams to discuss project requirements and challenges.
Set up experiments to validate new imaging techniques or sensor calibrations.
Review literature and recent advances to identify opportunities for algorithmic improvements.
Prepare presentations or technical notes summarizing findings for stakeholders.

Late Afternoon (3:00 PM - 6:00 PM)

Focus: Model Optimization & Documentation

Optimize machine learning models for accuracy, speed, or computational efficiency.
Document code and methodologies to ensure reproducibility.
Update internal repositories and communicate with software engineers regarding integration.
Plan next steps based on data interpretations and project milestones.

Work-Life Balance & Stress

Stress Level: Moderate

Balance Rating: Good

Work-life balance for Image Scientists is generally good, with many employing flexible hours or remote work options depending on organizational culture. The profession can become intense when nearing project deadlines or during critical research phases, creating moderate stress levels. However, structured project management and collaborative work environments help to mitigate this. Continuous learning demands and adapting to rapid technology changes are ongoing pressures but often balanced by stimulating intellectual challenges and autonomy in problem-solving.

Skill Map

This map outlines the core competencies and areas for growth in this profession, showing how foundational skills lead to specialized expertise.

Foundational Skills

Critical basics every Image Scientist must master to effectively work with image data and algorithms.

Digital Image Processing
Programming in Python and MATLAB
Statistical Data Analysis
Understanding Image Acquisition and Sensors

Advanced Technical Skills

Specialized capabilities focused on modern AI techniques and domain-specific image challenges.

Deep Learning for Computer Vision
3D Image Reconstruction and Analysis
Medical Image Processing (DICOM and PACS)
Hyperspectral and Multimodal Imaging
Sensor Calibration and Experimental Design

Professional & Software Skills

Skills essential for thriving in professional environments and ensuring collaboration and productivity.

Version Control with Git
Cloud Computing Platforms (AWS, Google Cloud)
Data Visualization Tools (Matplotlib, Plotly)
Strong Written and Verbal Communication
Project Management and Team Collaboration
Critical Thinking and Problem Solving

Pros & Cons for Image Scientist

✅ Pros

Exposure to cutting-edge technologies in AI, machine learning, and imaging sensors.
Opportunities to contribute to impactful applications such as medical diagnostics and environmental monitoring.
Interdisciplinary work that fosters collaboration and learning across fields.
Strong job market demand and competitive salary prospects.
Engaging problem-solving work that combines theory and practical implementation.
Flexibility in work environments including remote options in many organizations.

❌ Cons

Requires continuous education due to rapidly evolving technologies.
Can involve significant pressure to produce accurate results, particularly in healthcare applications.
Handling large, complex datasets may require intensive computational resources.
Ethical and privacy considerations add layers of complexity and responsibility.
Difficulties translating research prototypes into scalable industry solutions.
Sometimes necessitates specialized hardware access or fieldwork in challenging environments.

Common Mistakes of Beginners

Overlooking the importance of preprocessing leading to poor image quality and inaccurate results.
Relying too heavily on out-of-the-box machine learning models without tuning to dataset specifics.
Neglecting to validate algorithms on diverse and representative datasets.
Underestimating the computational requirements of advanced image analysis methods.
Failing to document code and methodologies, causing reproducibility issues.
Ignoring domain-specific knowledge which often guides effective feature extraction.
Poor communication with interdisciplinary teams, leading to misaligned project goals.
Attempting to develop overly complex algorithms before mastering fundamental principles.

Contextual Advice

Focus on mastering core image processing concepts before diving into deep learning.
Build a diverse portfolio of projects demonstrating skills across different imaging domains.
Engage with cross-disciplinary teams early to grasp real-world application contexts.
Continually update skills through courses and attending industry conferences.
Develop proficiency in both software and hardware aspects of imaging systems.
Prioritize writing clean, well-documented code to facilitate collaboration.
Stay informed about ethical standards and data privacy regulations relevant to imaging.
Seek mentorship and contribute to open-source projects to broaden experience and network.

Examples and Case Studies

Enhancing Tumor Detection in MRI with Deep Learning

A research team developed a convolutional neural network-based algorithm to improve the accuracy of tumor segmentation in brain MRI scans. Leveraging publicly available datasets combined with proprietary clinical images, the team optimized preprocessing steps for noise reduction and normalization. Their method increased lesion detection sensitivity by 15% compared to traditional techniques and achieved faster processing times, facilitating more timely diagnosis and treatment planning.

Key Takeaway: The case underscores the importance of combining domain expertise with tailored image preprocessing to fully leverage modern deep learning architectures for impactful healthcare applications.

Satellite Imagery Analysis for Deforestation Monitoring

An environmental consortium deployed an image scientist to craft a workflow for analyzing multi-temporal satellite images to detect illegal logging activities in the Amazon rainforest. Using cloud-based GIS tools and machine learning classifiers, the project identified subtle vegetation changes with high accuracy. Integration with local authorities enabled rapid response, helping to preserve critical ecosystems.

Key Takeaway: This demonstrates the power of image science in global environmental protection and highlights the need for scalable cloud computing solutions combined with geospatial analytics.

Automated Visual Inspection in Semiconductor Manufacturing

A leading semiconductor manufacturer implemented an image scientist-led project to automate wafer defect detection. Using hyperspectral imaging coupled with AI models, the team designed real-time inspection systems that reduced human error and increased throughput by 30%. Robust calibration protocols and continuous algorithm retraining were key to adapting to manufacturing variances.

Key Takeaway: Industrial applications benefit greatly from the fusion of imaging technologies with AI to improve efficiency and quality control in high-precision environments.

Portfolio Tips

Crafting a standout portfolio is crucial for aspiring Image Scientists to showcase technical expertise and creativity. Begin by displaying a diverse range of projects that reflect your proficiency across core image processing techniques, algorithm development, and applied machine learning. Include detailed write-ups explaining your problem statements, methodologies, challenges faced, and results achieved.

Visuals matter immensely: provide clear annotated images, charts, or interactive dashboards that demonstrate your ability to interpret and communicate visual data effectively. When possible, include links to code repositories, Jupyter notebooks, or executable demos to highlight your programming and reproducibility skills.

Tailor projects to your desired industry niche, such as medical imaging, remote sensing, or industrial inspection, to demonstrate domain awareness. Participating in hackathons, contributing to open-source image libraries, or publishing articles on platforms like Medium or GitHub can further strengthen your profile.

Highlight any experience working with different sensor modalities, large-scale datasets, or computational optimizations to underscore versatility. Lastly, maintain a neat, intuitive portfolio layout, emphasizing concise explanations with technical depth and storytelling to engage recruiters and collaborators alike. Update regularly as you gain new skills and complete impactful projects.

Job Outlook & Related Roles

Growth Rate: 15%
Status: Growing much faster than average
Source: U.S. Bureau of Labor Statistics and industry reports

Related Roles

Frequently Asked Questions

What is the difference between an Image Scientist and a Computer Vision Engineer?

While the terms overlap, Image Scientists often focus more heavily on researching, developing, and optimizing raw image processing and analysis algorithms, with a strong emphasis on understanding imaging physics and signal processing. Computer Vision Engineers typically focus on implementing these algorithms into software products or systems, emphasizing engineering, deployment, and performance optimization. Image Scientists tend to work in research or specialized environments, whereas Computer Vision Engineers work in software development and product teams.

Is a Ph.D. necessary to become an Image Scientist?

A Ph.D. is not strictly necessary but is highly advantageous for advanced research roles, leadership positions, or academic careers. Many industry positions at entry and mid-level require at least a master's degree combined with practical experience. Ultimately, employers value demonstrated expertise, problem-solving ability, and relevant skills, which can sometimes be gained through professional experience and focused training without a doctorate.

Which programming languages are most important for Image Scientists?

Python is the most widely used language due to its extensive libraries for image processing and machine learning such as OpenCV, scikit-image, TensorFlow, and PyTorch. MATLAB remains popular for prototyping algorithms quickly, especially in research. C++ is valuable when performance optimization is critical. Familiarity with scripting, version control (Git), and cloud computing tools also enhances productivity.

What industries employ Image Scientists the most?

Healthcare and biomedical imaging, remote sensing and geospatial analysis, manufacturing quality control, security and surveillance, autonomous vehicles, consumer electronics, and environmental sciences are among the largest employers. Tech companies developing AI-driven visual products also actively hire image scientists.

Can Image Scientists work remotely?

Many aspects of image processing and algorithm development can be done remotely, especially those focused on software and data analysis. However, roles requiring interaction with specialized imaging hardware or frequent on-site experiments may require physical presence. Increasing cloud infrastructure adoption also expands remote work possibilities.

How important is domain knowledge for an Image Scientist?

Domain knowledge is very important because it guides meaningful feature extraction, helps understand image context, and shapes appropriate algorithm selections. For instance, understanding biological structures benefits medical image analysis, while knowledge of geographical data supports remote sensing projects. Interdisciplinary communication often requires a respectful understanding of field-specific challenges.

What are some emerging technologies relevant to Image Scientists?

Emerging trends include explainable AI for building transparent models, edge computing for on-device processing, hyperspectral and multispectral imaging, computational photography advancements, 3D volumetric imaging, and integration of imaging with other sensor modalities like LiDAR and radar. Proficiency in these areas enhances innovation potential.

How can beginner Image Scientists gain practical experience?

Beginners can work on open datasets available from platforms like Kaggle, participate in coding competitions focused on computer vision, contribute to open-source image processing libraries, undertake internships in imaging-focused labs or companies, and develop personal projects reflecting real-world applications. Sharing code and write-ups online builds visibility.