Overview Atlassian is looking for a Senior Machine Learning Engineer to join our Search & Intelligence organization. Our team builds the intelligent experiences, agentic systems, models, evaluation frameworks, and data p…
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.
389
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$61–$155/hr
Typical pay range
$206k
Median, full-time
5
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Open machine learning engineer roles
12 shown of 389 · sorted by freshness
are dedicated to building innovative tools and technologies that drive the future of work. We are looking for a Principal Machine Learning Engineer to join our Search & Intelligence organization. The team builds Atlassia…
advanced modeling and rapid innovations that accelerate how teams work, discover, and create.We’re seeking a Principal Machine Learning Systems Engineer (P60) to lead technical directions of GenAI Products & Knowledge In…
$238k - $326k
authorized, and audited at scale. We are a diverse team of engineers, product managers, and designers who are bringing Okta’s... ...Access Policies sub-team under Okta Secures AI as a Principal Machine Learning (ML) Engi…
across the platform. Partner with Security and Infrastructure Engineering to integrate safety systems into core platform infrastructure.... ...insurance. Relocation assistance to San Francisco. Learning and growth opport…
Job Description Senior Machine Learning Engineer, Artificial Intelligence (AI) Required, Work From Home As the Senior Machine Learning Engineer, you are an independent owner of critical Machine Learning (ML) subsystems i…
Job Description Staff Machine Learning Engineer, Artificial Intelligence (AI) Required, Work From Home As the Staff Machine Learning Engineer, you own the execution layer of intelligence. You translate research direction…
Job Description Principal Machine Learning Engineer, Artificial Intelligence (AI) Required, Work From Home As a Principal Machine Learning Engineer, you are a deep technical authority responsible for designing and evolvi…
About the Role As a Machine Learning Engineer at Clera, you'll design, build, and deploy ML systems that power our core product in a fast-moving startup environment. You'll own the full ML lifecycle—from problem definiti…
$228.96k - $315.36k
London, and Amsterdam. The Fraud Data team at Plaid builds the machine learning systems that power Plaid’s fraud detection products,... ...from evolving fraud threats. As a Senior Machine Learning Engineer on Plaid's Fra…
the Advanced Technologies Group (ATG), Pinterest’s advanced machine learning team. ATG’s goal is to keep Pinterest at the forefront of machine... ...technology to the product in collaboration with product engineering tea…
$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…
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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.
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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
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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.
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Resume tips that move the needle
For machine learning engineers specifically — generic advice costs you here
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Center bullets on production systems: models served, request volume, latency, and the business metric they moved.
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Show software engineering credentials explicitly — testing, CI, code review — since MLE hiring filters hardest on engineering rigor.
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Name the MLOps tooling you have run (MLflow, SageMaker, Kubeflow, vector databases) as these are common screens.
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Include LLM work with specifics — fine-tuning, RAG, evaluation — if you have it; vague 'GenAI experience' claims read poorly.
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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
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Senior ML Engineer
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Staff ML Engineer
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ML Platform Lead
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