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Meta Platforms · Menlo Park, CA

# Distinguished Software Engineer, Recommendation Systems (AI/ML)

Menlo Park, CA Software Engineering 

Listed on Meta Platforms’s official careers site. Listed as of November 1, 2026. Check that it is still open and apply there.
[Apply on Meta Platforms’s careers site →](https://www.metacareers.com/jobs/2138780437002628/)

[OpenNash Jobs](https://jobs.opennash.com/) / [Meta Platforms](https://jobs.opennash.com/companies/meta-platforms/) / Distinguished Software Engineer, Recommendation Systems (AI/ML)

## Job description

Meta is seeking a Distinguished Software Engineer with deep machine learning expertise to drive transformative advances in Meta's AI-powered products. In this role, you will operate at the intersection of foundational ML research and large-scale user products, shaping the technical direction of applied AI, machine learning modeling, and infrastructure across the organization. You will identify and solve the hardest user problems, and leverage AI-native approaches to unlock step-change improvements in Meta's recommendation products.

## Responsibilities

### The role

Top applied AI/ML expert in a Recommendation Product
- Work on the hardest applied AI problem in the product
- Set a 2–3 year north-star architecture for the product Apply AI-native workflows and tooling as a force multiplier to accelerate AI/ML advancement in the product and expand the scope of what engineering teams can deliver
- Define new metrics and data-driven decision-making principles for long-term ML projects, connecting operational metrics to organization-level business outcomes Proactively identify systemic reliability, privacy, and integrity risks in ML systems and build robust technical safeguards, partnering with compliance and policy teams to ensure responsible AI deployment
- Mentor engineers across the organization on applied AI/ML, debugging complex product problems, and building production-grade AI systems, establishing yourself as a sought-after technical coach and technical leader

## Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience
- 8+ years of experience in applied AI/ML with a track record of delivering measurable product outcomes Experience defining technical strategy and gaining organizational alignment across multiple engineering teams and cross-functional stakeholders Experience communicating complex ML system designs and trade-offs in writing to both technical and non-technical audiences, including executive leadership Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience building AI-native developer tooling or automation that measurably accelerates ML experimentation and production improvement cycles Experience applying ML to multiple product domains such as ranking and recommendation, generative AI, computer vision, or natural language processing
- Track record of industry-recognized contributions to machine learning applications, such as publications and open-source projects
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, harness engineering, agent orchestration) and staying current with emerging AI technologies

## At a glance
- Posted by the employer: Not stated
- First seen by OpenNash: Oct 25, 2026
- Last checked: Nov 1, 2026
- Listed for: 7 days
- Location: Menlo Park, CA[Apply on Meta Platforms’s careers site →](https://www.metacareers.com/jobs/2138780437002628/)

## Hiring at Meta Platforms
- Open jobs: 1,310
- New in the last 7 days: After two weekly collections
- Tracked since: Jul 11, 2026

[All Meta Platforms jobs](https://jobs.opennash.com/companies/meta-platforms/) · [Weekly hiring report](/reports/hiring/)

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OpenNash lists jobs from employers’ own careers sites and does not handle applications.
Dates come from the employer where stated; “first seen” and “last checked” are our own observations.

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