Plumbing Project Engineer PE in City Heights, San Diego Currently looking for an experienced Plumbing Project Engineer PE to fill the open position in San Diego. The ideal candidate must be local and have stable work-his…
Engineering & IT · San Diego, CA
Machine Learning Engineer jobs in San Diego, CA
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
177
Open roles today
$51–$130/hr
Typical pay range
$174k
Median, full-time
3
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Open machine learning engineer roles
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Plumbing Project Engineer position in San Diego, CA We are a large mechanical contractor that specializes in large Mechanical projects in CA. Responsibilities for Plumbing Project Engineer: Assist Project Managers with o…
ISO 9001 standards to ensure aerospace conformity and risk mitigation. Core Responsibilities L ooking for the Quality Engineer with background on Analysis, problem solving (against jut managing quality processes, complia…
develop applied intelligence that brings AI into the real world. Our platform combines advanced machine learning, high-performance simulation, and modern software engineering to accelerate the design, validation, and dep…
$180k - $250k
Role: Senior Machine Learning Engineer Location: San Diego, CA (in-office) Salary Range: $180,000 - $250,000 / yr + stock options, 401k matching, and other benefits Role Overview: Seasats' vehicles operate in highly remo…
intelligent solutions across AI, data, analytics, and product engineering. Since 2000, we have partnered with some of the largest... ...edge AI research and production systems at the intersection of machine learning, com…
$216.1k - $378.2k
It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy... ...on the nature of their work and their assigned work location. Learn more here . To…
Description Responsibilities Own small to medium components of machine learning systems from technical designthrough implementation and... ...to plan Build and maintain data pipelines and feature engineering workflows to…
$140k - $195k
Machine Learning Engineer III The Marlin Alliance, Inc. | San Diego, CA | Hybrid | Clearance Required About The Marlin Alliance Incorporated in 2002, The Marlin Alliance is a digital transformation company dedicated to e…
$110k - $180k
The Marlin Alliance, Inc. is seeking a Senior Machine Learning Engineer to design, develop, and implement advanced machine learning models and algorithms in support of naval applications. This role requires deep technica…
$165k - $195k
The Marlin Alliance, Inc. is seekinga talented and experienced Senior Machine Learning Engineer to join our team. The successful candidate will be expected to design, develop, and implement advanced machine learning mode…
Staff Machine Learning Engineer Location: Remote Are you tired of being kept in a restricted creative box with limited autonomy to push boundaries and ideas to solve problems with ML? Or not seeing your work directly imp…
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What machine learning engineers earn in San Diego
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $51–$71 | $106k–$148k |
| Mid level | $71–$97 | $148k–$201k |
| Senior | $94–$130 | $195k–$271k |
Adjusted for the San Diego 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
04
ML Platform Lead
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