Job Summary: The Sr. Electrical Engineer (R&D) is responsible for collaborating on or leading the electrical design, testing, and certification of new low and medium voltage power distribution products. This role works c…
Engineering & IT · Sacramento, CA
Machine Learning Engineer jobs in Sacramento, CA
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
77
Open roles today
$47–$119/hr
Typical pay range
$159k
Median, full-time
12
Fresh in this list
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01
Open machine learning engineer roles
12 shown of 77 · sorted by freshness
$150k - $190k
industrial power solutions for essential infrastructure nationwide. Headquartered in Sacramento, CA, our teams collaborate closely across engineering, manufacturing, and operations to build reliable, high‑quality systems…
our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your... ...centers to modernizing transmission systems, our industry-recognized engineers an…
$110.3k - $165.5k
OpportunityWe are currently engaged as the Owner’s Consulting Engineer in several hydro projects that include complex scopes ranging from... ...primary focus will be on mechanical engineering for rotating machines, speci…
$93.6k - $135.7k
clients and the communities they serve.Join a team that’s naturally committed to the environment.Your OpportunityThe role of a civil engineer is to work on smaller projects, and/or as a team member of a larger project un…
$110.3k - $165.5k
firms while building our clean energy future.Your OpportunityOur US work group has an opportunity for a Senior Transmission Line Engineer; this individual will sit in any of Stantec’s US offices. Project requirements wil…
$145.6k - $218.5k
projects that will benefit future generations. Grow with us, H2O+U.Stantec's Water practice is seeking a highly motivated Geotechnical Engineer to join our successful water team in our Sacramento, California office. Prim…
$121.3k - $182.1k
top design firms while building our clean energy future.Your OpportunityStantec is seeking Intermediate and Senior Civil/Structural Engineers to support our Substation Physical Electrical Teams. This position is ideal fo…
$145.6k - $218.5k
lead, and do the most consequential work of their careers.Your OpportunityStantec is seeking an accomplished US Transmission Line Engineering Manager to lead and advance our transmission line engineering and design pract…
our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your... ...centers to modernizing transmission systems, our industry-recognized engineers an…
our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your... ...global team is looking for success-oriented Hydraulic Structures Engineers to wor…
our ability to change the world for the better. Read further to learn how you could help make great things possible not only in your... ...LI-MO1QualificationsRequired QualificationsBachelor's degree in Engineering 7 yea…
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What machine learning engineers earn in Sacramento
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $47–$65 | $97k–$135k |
| Mid level | $65–$88 | $135k–$184k |
| Senior | $86–$119 | $178k–$248k |
Adjusted for the Sacramento 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
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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
02
Senior ML Engineer
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Staff ML Engineer
04
ML Platform Lead
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