[All remote jobs](https://jobicy.com/jobs.md)[![Doximity logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/e71c16d6-221.jpg)](https://jobicy.com/company/doximity.md)[Doximity](https://jobicy.com/company/doximity.md) # Staff Applied AI Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/doximity.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 170k–248k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished19 Oct 2026Apply before18 Nov 2026Listing views74Application actions6Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Doximity is hiring a Staff Applied AI Engineer to build, tune, and evaluate AI products serving medical professionals. The role combines large-scale machine learning, LLM fine-tuning, statistical experimentation, analytics, and production-quality Python and SQL work. The engineer will partner with product managers, analysts, and developers to lead projects from data ingestion through recommendations. Success requires deep technical expertise in modern ML and the ability to communicate data-driven insights clearly to product and business stakeholders. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a senior, high-impact applied AI position requiring expertise in advanced ML methods, LLM alignment and fine-tuning, distributed processing, experimentation, and scalable data systems. The role also demands technical leadership across the full lifecycle of data and product initiatives. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$209,000US market range (per year)$190k–$280k0$308k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses total target compensation of $170,000–$248,000 USD, inclusive of salary and equity. The annual period is inferred from the full-time US professional-role context and the stated compensation magnitude, as the posting does not explicitly specify a cadence. A reasonable estimated US annual market range for a staff-level applied AI engineer is $190,000–$280,000 USD. ## Core skills Skills and capabilities most closely associated with this opportunity. [Applied machine learning](https://jobicy.com/jobs?search_keywords=Applied%20machine%20learning.md)[Large language models](https://jobicy.com/jobs?search_keywords=Large%20language%20models.md)[LLM fine-tuning](https://jobicy.com/jobs?search_keywords=LLM%20fine-tuning.md)[Supervised fine-tuning](https://jobicy.com/jobs?search_keywords=Supervised%20fine-tuning.md)[Direct Preference Optimization](https://jobicy.com/jobs?search_keywords=Direct%20Preference%20Optimization.md)[Reinforcement learning](https://jobicy.com/jobs?search_keywords=Reinforcement%20learning.md)[PyTorch](https://jobicy.com/jobs?search_keywords=PyTorch.md)[TensorFlow](https://jobicy.com/jobs?search_keywords=TensorFlow.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Advanced SQL](https://jobicy.com/jobs?search_keywords=Advanced%20SQL.md)[Experimental design](https://jobicy.com/jobs?search_keywords=Experimental%20design.md)[Statistical analysis](https://jobicy.com/jobs?search_keywords=Statistical%20analysis.md)[Distributed data processing](https://jobicy.com/jobs?search_keywords=Distributed%20data%20processing.md)[Data visualization](https://jobicy.com/jobs?search_keywords=Data%20visualization.md)[Model evaluation](https://jobicy.com/jobs?search_keywords=Model%20evaluation.md) Sample interview questionsHow would you evaluate whether an LLM-based feature improves the experience of medical professionals?I would define user-centered success metrics such as task completion, time saved, clinician satisfaction, and safe-answer rates. I would combine offline evaluation using clinically relevant test sets with staged online experiments, monitor quality and safety guardrails, and analyze outcomes by user segment to identify unintended effects. Describe your approach to fine-tuning a large language model for a specialized domain. I begin by defining the target tasks, risk profile, and evaluation criteria, then curate high-quality, privacy-appropriate domain data. I typically establish a supervised fine-tuning baseline, evaluate it against representative holdout sets, and use preference optimization or reinforcement fine-tuning only where it demonstrably improves desired behavior. Throughout the process, I version data and models, track experiments, and test for reliability, bias, and failure modes. How do you design an experiment when user behavior and business objectives may conflict? I start by identifying primary, guardrail, and long-term metrics. The primary metric measures the intended product benefit, while guardrails protect user trust, quality, safety, and downstream outcomes. I predefine decision thresholds, power calculations, segmentation plans, and stopping rules so that the result supports a balanced, evidence-based decision. Tell us about a time you turned a complex analysis into a recommendation for nontechnical stakeholders. I would frame the analysis around the decision rather than the methodology, explain the key result in plain language, and use focused visualizations to show magnitude and uncertainty. I would clearly state assumptions, trade-offs, and recommended next steps, then provide technical detail separately for stakeholders who need it. How would you optimize a machine-learning workflow that must train on high-volume distributed data? I would profile the full pipeline to identify bottlenecks in data extraction, feature generation, network transfer, and training. I would use partitioned datasets, efficient distributed processing, reproducible feature pipelines, and scalable training infrastructure, while validating that performance optimizations do not change data semantics or model quality. Monitoring cost, latency, and training reliability would be part of the production design. Opportunity details ## About this role. Doximity is transforming the healthcare industry. Join our mission to help every physician be more productive and provide better care for their patients. As medicine’s largest network in the United States, there’s an elevated level of responsibility in everything we do. We don’t take that responsibility lightly and are committed to building diverse teams with an inclusive culture that can make a direct impact on the healthcare system. One of Doximity’s[core values](https://work.doximity.com/) is stretching ourselves. Even if you don’t check off all the boxes below we encourage you to apply. Doximity is full of exceptional people who bring their own unique experiences to work everyday and make us all better for it! ### Visa sponsorship is not available for this role. About Us * Here are[some of the ways we bring](https://drive.google.com/open?id=1qimYh0mG3i1nTJe6jDCDepJt2i4o8MEB) value to doctors * Here is an[introduction](https://technology.doximity.com/technology-stack) to our tech stack * We use UNIX command-line interface and standard programming tools (vim/emacs, git, etc.) and have over 350 private repositories in Github containing our applications, forks of gems, our own internal gems, and[open-source projects](https://github.com/doximity) * We have worked as a distributed team for a long time;[we’re currently about 65% distributed](https://blog.brunomiranda.com/building-a-distributed-engineering-team-85d281b9b1c) * Find out more information on the[Doximity engineering blog](https://engineering.doximity.com/) * Our[company core values](https://work.doximity.com/) * Our[recruiting process](https://engineering.doximity.com/articles/engineering-recruitment-process-doximity) * Our[product development cycle](https://engineering.doximity.com/articles/mofo-driven-product-development) * Our[on-boarding & mentorship process](https://engineering.doximity.com/articles/software-engineering-on-boarding-at-doximity) Here’s How You Will Make an Impact * Leverage Doximity’s extensive datasets to optimize, tune and evaluate AI products for medical professionals on our platform. * Play a key role in creating both product and client-facing analytics. * Inform data team strategy by working with the product leaders and managers. Actively participate in execution and some planning of organizational data team strategy. * Collaborate with a team of product managers, analysts, and other developers to define and lead data projects from data ingestion to analysis to recommendations. About you * At least 5 years of professional experience as a Data Scientist or other related roles working with complex, high-volume datasets. * Advanced knowledge of statistical concepts — particularly exploratory data analysis, experimental design, and probability theory. * Deep understanding of modern machine learning techniques, including deep learning architectures, reinforcement learning, and large language model (LLM) fine-tuning methods such as Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Fine-Tuning (RFT). * Proven ability to design, train, and evaluate large-scale models using frameworks such as PyTorch or TensorFlow. * Advanced SQL proficiency — skilled at writing and optimizing complex queries across multiple tables and data relationships. * Advanced Python skills, including understanding of object-oriented programming, production-grade code design, and modern data science libraries. * Hands-on experience with distributed data processing tools and concepts for scalable analysis and model training. * Excellent data visualization and storytelling abilities, with the capacity to translate technical insights into clear, compelling narratives for stakeholders. * Curious, fast learner with a demonstrated passion for data, continuous learning, and staying current with advances in machine learning research. Compensation The US total compensation range for this full-time position is $170,000 – $248,000 (inclusive of salary + equity). Our ranges are determined by role and level. The range displayed on each job posting reflects the approximate total target compensation for the position across the US. Within the range, individual pay is determined by factors including relevant skills, experience, and education/training. Please note that the compensation listed does not include benefits. More on /Benefits/Perks Doximity is proud to offer industry-leading benefits to our full time employees. Some of our offerings include: * Medical, dental, vision offerings for you and your family * 401k with matching program * Employee stock purchase plan * Family planning support, Childcare FSA, and parental leave * Life, AD&D, and Disability * Generous time off, holidays and paid company trips * Wellness benefits…plus many more! More About Doximity… For the past decade, it’s been our mission to help every physician be more productive so they can provide better care for their patients. We believe that when doctors are connected, the healthcare system works better and patients benefit. Doximity enables our verified clinician members to collaborate with colleagues, stay up-to-date with the latest medical news and research, manage their careers, and conduct virtual patient visits. Today, Doximity is the leading digital platform for U.S. medical professionals, with over 80% of physicians, 50% of all nurse practitioners and physician assistants, and 90% of graduating medical students as members. Joining Doximity means being part of an incredibly talented and humble team passionate about improving inefficiencies in our $4.3 trillion U.S. healthcare system. We are a team of doers who solve problems everyday by treating obstacles like an adventure, and we love creating technology that has a real, meaningful impact on people’s lives. Doxers are committed to working towards a more equitable world both within and beyond our office walls. This starts by fostering an inclusive and diverse work environment where differences are valued and all employees are encouraged to bring their full, authentic selves to work daily. To learn more about our team, culture, and users, check out our[careers page](https://workat.doximity.com/),[company blog](https://blog.doximity.com/), and[engineering blog](https://engineering.doximity.com/). We’re growing fast, and there’s plenty of opportunity for you to make an impact—join us! For more information, visit[Doximity.com](http://www.doximity.com/). ____________________________________________ EEOC Statement Doximity is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. 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