with compiler design for CPU, GPU, vector engines, or ML accelerators. We prefer... ...PyTorch, OpenXLA, StableHLO, JAX, TVM, deep learning models, and algorithms. We prefer experience... ...Hardware Support Java LLVM Ma…
Engineering & IT · San Jose, CA
Machine Learning Engineer jobs in San Jose, CA
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
391
Open roles today
$60–$152/hr
Typical pay range
$204k
Median, full-time
7
Fresh in this list
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01
Open machine learning engineer roles
11 shown of 391 · sorted by freshness
$161.3k - $274.2k
It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of... ...the future. You will play a major part in building AI and Machine Learning (ML) solutio…
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…
$158.9k - $178.1k
It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of... ...that deliver real customer impact. Job Description The Machine Learning Engineer design…
and ML inference. Turn advances in information retrieval and machine learning into production systems through rigorous evaluation,... ...multiple teams, simplify fragmented systems, mentor senior engineers, and align tec…
$197.3k - $225.1k
Overview Machine Learning Engineer 4 (IC) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delive…
an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges... ...Join us at Zscaler. Role We are looking for a Senior Machine Learning Engineer to…
$190.9k - $334.1k
It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of... ...to do in this role We are looking for a Senior Manager of Machine Learning Engineering…
$201.3k - $352.3k
It all started when engineer Fred Luddy wrote code that automated a tedious task for... ...experiences, and a culture of continuous learning. This is a zero-to-one incubation. We... ...Computer Science, Artificial Intell…
$176.1k - $308.2k
It all started when engineer Fred Luddy wrote code that automated a tedious task for... ...experiences, and a culture of continuous learning. This is a zero-to-one incubation. We... ...Computer Science, Artificial Intell…
$240.1k - $420.2k
It all started when engineer Fred Luddy wrote code that automated a tedious task for... ...experiences, and a culture of continuous learning. This is a zero-to-one incubation. We... ...Computer Science, Artificial Intell…
02
What machine learning engineers earn in San Jose
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $60–$83 | $124k–$172k |
| Mid level | $83–$113 | $172k–$235k |
| Senior | $110–$152 | $228k–$317k |
Adjusted for the San Jose 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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