About this role.
This is a senior-level Software Engineer (Technical Leadership) role at Meta, focusing on machine learning and large-scale prediction problems such as fraud detection, search ranking, and recommendations. The position requires driving technical direction, mentoring engineers, and collaborating with organizational leaders to build scalable systems. Candidates need 12+ years of programming experience and 8+ years in ML or related fields, with a proven track record of leading complex, cross-functional projects. The role offers significant autonomy and impact, working on some of the most massive social data problems on the web.
Role DNA
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Job Complexity
5/5Pace & Pressure
5/5Autonomy Level
5/5Communication Load
5/5Salary analysis
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Core skills
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Cover letter sample
Dear Hiring Manager,
I am excited to apply for the Software Engineer (Technical Leadership) position at Meta. With over 12 years of experience building large-scale machine learning systems and leading high-impact engineering teams, I am drawn to the opportunity to tackle the massive social data challenges you describe. My background in classification, optimization, and deep learning aligns perfectly with your needs in fraud detection, recommendation systems, and search ranking.
Throughout my career, I have driven multi-year technical roadmaps, mentored engineers across organizations, and collaborated with leadership to prioritize and deliver complex projects. I thrive in fast-paced environments where technical vision and cross-functional communication are essential. I am particularly excited about applying my expertise to Meta's unique data scale and impacting billions of users.
I look forward to the possibility of contributing to your team and driving innovative solutions that make a global impact. Thank you for your consideration.
Sincerely,
[Your Name]
Sample interview questions
I led a project to revamp our recommendation engine, which involved data science, backend, and product teams. I organized regular syncs, created a shared roadmap, and used architectural decision records to document trade-offs. By clearly communicating the vision and involving key stakeholders early, we maintained alignment and delivered on time.
I would start by profiling the data pipeline and identifying bottlenecks, then design a layered architecture with feature stores, model serving via distributed clusters, and caching layers. I'd leverage parallel processing with tools like Spark and optimize model inference using quantization or GPU acceleration. I'd also implement monitoring and A/B testing to ensure reliability and performance.
I regularly read research papers, attend conferences, and participate in internal tech forums. I evaluate new techniques through small pilot projects and benchmarks. For example, I introduced a transformer-based model for text classification after validating it improved accuracy by 15% compared to previous methods, then scaled it with proper guardrails.
In one initiative, I needed buy-in from multiple teams to adopt a new data pipeline. I built a prototype demonstrating clear benefits, presented it with concrete metrics, and actively listened to their concerns. I then incorporated their feedback and created a phased rollout plan, which gained their support and ensured smooth adoption.
I focus on identifying common components that can serve both. I work with product managers to understand immediate priorities, then design solutions that are extensible. For example, I might implement a feature that solves a current fraud detection issue but is also reusable for future spam detection, ensuring short-term wins ladder up to the long-term vision.
Meta is seeking a Software Engineer to join our engineering team. The ideal candidate will have industry experience working on a range of classification and optimization problems like payment fraud, click-through rate prediction, click-fraud detection, search ranking, text/sentiment classification, collaborative filtering/recommendation, or spam detection. The position will involve taking these skills and applying them to some of the most exciting and massive social data and prediction problems that exist on the web.ResponsibilitiesDrive the team’s goals & technical direction to pursue opportunities that make your larger organization more efficient.* Effectively communicate complex features & systems in detail.* Understand industry & company-wide trends to help assess & develop new technologies.* Partner & collaborate with organization leaders to help improve the level of performance of the team & organization.* Identify new opportunities for the larger organization & influence the appropriate people for staffing/prioritizing these new ideas.* Suggest, collect and synthesize requirements and create an effective feature roadmap.* Develop highly scalable classifiers and tools leveraging machine learning, data regression, and rules-based models.* Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU).QualificationsBachelor’s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.* Experience leading projects with industry-wide impact.* Experience communicating and working across functions to drive solutions.* Experience in mentoring/influencing engineers across organizations.* Proven track record of planning multi-year roadmap in which short-term projects ladder to the long-term vision.* Experience in driving large cross-functional/industry-wide engineering efforts.* 12+ years of experience in programming languages (Python, C++, or Java) with technical background.* 8+ years of experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining or deep learning based methods. Experience in shipping products to millions of customers or have started a new line of product.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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