$220k - $320k
planet-scale hosting. We are a well-funded ten-person team of engineers who work in-person in downtown San Francisco on difficult, high... ...You don't need to tick every box. Curiosity and the ability to learn quickly m…
Engineering & IT · San Francisco, CA
Machine Learning Engineer jobs in San Francisco, CA
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
355
Open roles today
$61–$155/hr
Typical pay range
$206k
Median, full-time
2
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01
Open machine learning engineer roles
12 shown of 355 · sorted by freshness
$249.12k - $367.92k
monitoring. As part of the team, you’ll work at the intersection of machine learning infrastructure, applied AI, and distributed systems, helping... ...innovation across Plaid. As a Staff Machine Learning Engineer, you w…
$42.18 - $74.64 per hour
Title: TV Maintenance Engineer Schedule Format - Location: On-site - San Francisco Pay Information: Hourly Rate: $42.18 - $74.64... ...from Engineering computers. ~ Very strong technical aptitude to learn technology quic…
$187.9k - $252k
Entertainment and ESPN Product & Technology is a global organization of engineers, product developers, designers, technologists, data... ...and identity. The Core ML team is an applied science and machine learning engine…
environments without traditional infrastructure limitations. As a Senior ML Infrastructure Engineer, the successful candidate will help build and scale Kubernetes-based machine learning platforms supporting large-scale t…
$250k
Our client, a venture-backed AI Startup, is hiring a talented ML/AI Research Engineer to join their team in San Francisco. The successful candidate will act as the key link between cutting-edge ML research and secure, sc…
$275k - $305k
Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy . AI is a... ...the company. You will lead a broad organization spanning Machine Learning Engineering, ML Platform,…
$245k - $272k
success. Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy . AI is a... ...technical standards, drive architectural decisions, and mentor engineers across the organi…
About the internship Stripe's Applied ML, Data Science, Risk, and Payments organizations are excited to offer PhD machine learning engineering internships for the summer of 2026. This is an exceptional opportunity to con…
research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward... ...you’d have: Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a re…
$200k - $245k
workplace, both physically and virtually. Learn more about our Total Rewards philosophy... ...AI/ML team and seasoned leaders in Engineering, Product, Design, Data Science, Marketing... ...the unified service platform st…
$208k - $260k
areas relating to: robotics, computer vision, embodied AI, sim-to-real, imitation learning, reinforcement learning, and vision language actions models ~ PhD or equivalent experience in Machine Learning or Robotics ~ A tr…
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What machine learning engineers earn in San Francisco
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $61–$84 | $126k–$175k |
| Mid level | $84–$114 | $175k–$238k |
| Senior | $111–$155 | $231k–$322k |
Adjusted for the San Francisco market from national ranges.
03
What employers ask for
The skills these listings keep naming
Python and software engineeringPyTorch or TensorFlowML fundamentals and evaluationModel serving and APIsMLOps (tracking, registries, CI)Docker and KubernetesData pipelines and feature storesLLM fine-tuning and RAG (a plus)Monitoring and drift detection
04
Interview questions worth rehearsing
With the thing the interviewer is actually listening for
Walk me through taking a model from prototype to production.
Cover data pipelines, training reproducibility, serving, and monitoring. Emphasize that the model is a small part of the system — that framing is the job.
How do you monitor a model in production?
Discuss input drift, prediction distributions, delayed labels, and business metrics — plus what triggers retraining. Mention that silent degradation is the default failure mode.
How would you reduce inference latency or cost for a large model?
Options include distillation, quantization, batching, caching, and smaller models. Frame it as measuring first, then choosing the cheapest acceptable quality tradeoff.
Tell me about a time a model failed in production. What happened?
A real story about skew, drift, or a data bug — with detection and prevention — is far more convincing than claiming smooth deployments.
How do you evaluate a model beyond accuracy?
Talk about the metric matching the business cost of errors, slicing by segment, and offline-online gaps. Naming a case where accuracy misled is a strong touch.
When would you fine-tune an LLM versus use retrieval or prompting?
Start cheap: prompting, then RAG for knowledge, fine-tuning for behavior and format. Cost and maintenance burden should drive the answer.
How do you make training reproducible?
Version code, data, and config; track experiments; pin environments. This is engineering discipline applied to ML, which is exactly the role.
05
Resume tips that move the needle
For machine learning engineers specifically — generic advice costs you here
01
Center bullets on production systems: models served, request volume, latency, and the business metric they moved.
02
Show software engineering credentials explicitly — testing, CI, code review — since MLE hiring filters hardest on engineering rigor.
03
Name the MLOps tooling you have run (MLflow, SageMaker, Kubeflow, vector databases) as these are common screens.
04
Include LLM work with specifics — fine-tuning, RAG, evaluation — if you have it; vague 'GenAI experience' claims read poorly.
05
Distinguish your role on shared projects: built the serving layer, owned the pipeline, or trained the model.
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Where this role goes
Typical progression
01
ML Engineer
02
Senior ML Engineer
03
Staff ML Engineer
04
ML Platform Lead
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