AI role · Open offer

Machine Learning Engineer — GenAI platforms

Meta

Full-time
Senior
Generative AI
Hybrid
1 Hacker Way, Menlo Park, CA 94025, United States · Hybrid
Menlo Park, United States
USD 180k–250k + RSU

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

4 skills2 tools

At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: 1 Hacker Way, Menlo Park, CA 94025, United States · Hybrid
Compensation: USD 180k–250k + RSU
Experience level: Senior
Team: Generative AI
Benefits & perks

About this role

What the team is looking for and how this position fits.

Meta is hiring a Senior Machine Learning Engineer — GenAI platforms on the Generative AI team (Hybrid · Menlo Park, United States). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Meta scales generative AI across social and messaging products from Menlo Park.

Machine Learning Engineer: GenAI platforms, serving reliability, and quality loops for US-scale traffic.

Missions

Improve training/serving pipelines for generative features.
Own latency, cost, and quality regressions with clear SLOs.

Stack & environment

Python, PyTorch, Kubernetes, Docker.

What we look for

Large-scale ML systems; Bay Area hybrid readiness.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
Machine Learning

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Production ML with clear latency/quality tradeoffs.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, Machine Learning, MLOps, and Kubernetes.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Menlo Park, United States)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: USD 180k–250k + RSU, benefits in At a glance) and your expectations align.

Apply

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

Production ML with clear latency/quality tradeoffs.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

Suggested

AI talent you may also like

Profiles ranked by overlap with this role's skills and tools—handy if you're hiring for a team or comparing backup candidates.

No close matches in the directory yet—try with your own filters.

Similar open roles

Other listings that share skills or tools with this one—useful if you want comparable stacks or backup options.

Machine Learning Engineer — GenAI platforms

AI talent you may also like

Similar open roles

Staff ML Platform Engineer — inference at scale

Research Engineer — InstaDeep careers Tunis

ML Engineer — OpenAI ecosystem (Toronto)

MLOps Engineer — model CI & monitoring

ML Engineer — fine-tuning & adaptation