$80k - $100k
Machine Learning Data Engineer – Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fant…
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.
419
Open roles today
$60–$152/hr
Typical pay range
$204k
Median, full-time
3
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01
Open machine learning engineer roles
12 shown of 419 · sorted by freshness
$138.9k - $256.5k
Role Number: 200550889 Imagine what you could do as an applied machine learning scientist here. At Apple, novel machine learning ideas have... ...your opportunity to be part of an incredible research and engineering team…
$105k - $143k
Machine Learning Infrastructure Engineer – Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This…
$19 - $65 per hour
directly usable for planning models. Support Reinforcement Learning: Create the infrastructure necessary for planning models to undergo... ...Strong foundation in deep learning, computer vision, and machine learning. Pro…
$19 - $65 per hour
using recursive and multi-step losses. Area of work Deep Learning Models, Planning, Prediction Responsibilities Multimodal... ...trajectories for ambiguous scenarios. Loss Function Engineering: Formulate and experiment w…
Job Description Job Description Job Description: We are looking for a Machine Learning Engineer to join our core research and development team, focused on recovering accurate 3D human body and hand motion from egocentric…
$70k - $170k
Back Machine Learning Engineer PayPal USD 70K-170K Full Time San Jose, CA On-site At PayPal (NASDAQ: PYPL), we believe that every person has the right to participate fully in the global economy. Our mission is to revolut…
$148.91k - $252k
to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity. With this mission, we are looking for passionated machine learning engineers of all levels who will deve…
$223.61k - $268.33k
products OKX, OKX Wallet, OKLink and more. About The Opportunity Building machine learning systems for risk at a global crypto exchange is fundamentally different from conventional ML engineering. The data spans on-chain…
$160k - $200k
teams. As a Senior ML Infrastructure Engineer at Plus, you will design scalable architectures... ...integrated with state-of-the-art deep learning frameworks like PyTorch or TensorFlow.... ...the boundaries of what's pos…
$130k - $220k
drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams. We’re looking for a machine learning engineer to train and deploy the latest generation of ML-based planning algorith…
$175k - $296k
electric vehicles with technology and data, shaping the mobility experience of the future. We are looking for a full-time Machine Learning Engineer, with deep knowledge and strong enthusiasm towards establishing a state-…
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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.
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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
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.
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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
04
ML Platform Lead
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