Machine Learning, Recommendation Systems (Ads Infra and Ranking)

Meta; Bellevue (WA); On-site; USD 154,000 - 217,000; Full time

Job summary

Meta's Ads organization is seeking a senior ML engineer to own a significant area of the ads ranking stack end to end, including model architecture and production rollout. You will collaborate with cross-functional teams to drive performance, security, and privacy while shipping high-quality AI systems at scale.

Ideal candidates bring 6+ years of programming or 3+ years with a PhD, hands-on ML experience, and a track record of building recommender systems, with strong Python/Java/C++ and

Qualifications

• 6+ years of programming experience in a relevant language or 3+ years with a PhD.
• 1+ years of hands-on experience in ML, recommendation systems, LLMs, or AI.
• Experience with scripting languages such as Python, Javascript, or Hack.
• Experience with developing ML models at scale from inception to business impact.
• Knowledge of C/C++ and Java, or scripting languages like Python, Perl, PHP, or shells.
• Proven experience setting technical direction for a team and cross-functional partnerships.
• Experience designing or deploying recommendation systems.
• Experience delivering high-quality, reliable work.

Responsibilities

• Own a significant area of the ads ranking stack end to end.
• Lead architecture work in deep learning for recommendations.
• Define evaluation methodology and guardrails.
• Drive value-model and auction-adjacent modeling work.
• Lead GPU-scale training and serving efficiency work.
• Set technical direction for a team and lead partnerships.
• Improve engineering quality through code reviews and testing.
• Own reliability and responsible-AI practices for a major ranking surface.
• Mentor engineers and participate in recruiting.

Skills

ML experience

Recommendation systems

Scripting languages

C/C++/Java knowledge

Tech direction & leadership

Code reviews & testing

Reliability & high quality

Cross-functional partnerships

Education

PhD

Tools

Python

Java

C/C++

CUDA

Triton

Javascript

Hack

Perl

PHP

Shell scripts

Job description

Meta's Ads organization builds the systems that help businesses of every size reach the people most likely to care about what they offer. At our core, we're builders who care about shipping real value. Within Ads, the Infra and Ranking organization owns the technical foundation that makes this work at Meta's scale: the ranking models that decide which ad is right for each moment; the retrieval and serving platforms that evaluate enormous candidate sets in milliseconds; and the underlying data, training, and inference infrastructure. All of this is done while maximizing compute efficiency, ensuring reliability and privacy, and using AI to speed up development. Our state-of-the-art research in deep learning architectures, sequence and representation learning, and generative recommenders continuously pushes the boundaries of personalization accuracy. Our innovations in distributed GPU training, model/system co-design, and large-scale low-latency serving let us deploy multi-billion-parameter models that learn continuously from real-time behavior - under strict efficiency, reliability, and privacy constraints. These advances in large-scale ML optimization, ads ranking quality, and personalization infrastructure are crucial to our revenue goals and to innovation across the Meta family of apps.

Machine Learning, Recommendation Systems (Ads Infra and Ranking) Responsibilities:

• Own a significant area of the ads ranking stack end to end - model architecture, features, training data, evaluation, and production rollout.
• Lead architecture work in deep learning for recommendations (sequence models, multi-task and multi-objective learning, transfer learning, generative recommenders).
• Define evaluation methodology and guardrails - offline/online correlation, calibration, holdouts, long-term and incrementality effects.
• Drive value-model and auction-adjacent modeling work in partnership with auction, bidding, and pacing teams.
• Lead GPU-scale training and serving efficiency work, including model/system co-design and inference optimization.
• Set technical direction for a team, drive consensus, and lead successful cross-functional partnerships.
• Raise engineering and modeling quality through code and design review, testing, staged rollouts, and monitoring.
• Own reliability and responsible-AI practices for a major ranking surface.
• Contribute to team and organizational growth through mentoring engineers, participating in recruiting and interviews, and identifying opportunities to improve team workflows.

Minimum Qualifications:

• 6+ years of programming experience in a relevant programming language OR 3+ years of experience + PhD
• 1+ years of hands-on experience in one or more of the following areas: machine learning, recommendation systems, llms or artificial intelligence
• Experience with scripting languages such as Python, Javascript or Hack
• Experience with developing machine learning models at scale from inception to business impact
• Knowledge developing and debugging in C/C++ and Java, or experience with scripting languages such as Python, Perl, PHP, and/or shell scripts
• Track record of setting technical direction for a team, driving consensus and successful cross-functional partnerships
• Proven experience designing, building, or deploying recommendation systems (e.g., collaborative filtering, content-based, hybrid approaches, personalization at scale)
• Experience building and shipping high quality work and achieving high reliability
• Experience improving quality through thoughtful code reviews, appropriate testing, proper rollout, monitoring, and proactive changes

Preferred Qualifications:

• Direct experience with ads ranking or monetization - auction dynamics, bidding, pacing, value modeling, or advertiser-facing optimization
• Experience with distributed training and large-scale low-latency model serving
• Experience with sequence modeling, multi-task/multi-objective optimization, and transfer learning for recommendations
• Experience with GPU scaling, model/system co-design, CUDA, Triton, or inference optimization
• Publications in top-tier conferences/journals, patents, or open-source contributions in the recommendations or LLM space
• Experience with privacy-preserving or aggregated measurement and delayed-conversion modeling

About Meta:

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today - beyond the constraints of screens, the limits of distance, and even the rules of physics.

Meta is proud to be an Equal Employment Opportunity and Afffirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.

Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at [e-mail].

$154,003/year to $217,000/year + bonus + equity + benefits

Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.