in 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 community, but around the world. We believe building engineering is more than system…
Engineering & IT · Los Angeles, CA
Machine Learning Engineer jobs in Los Angeles, CA
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
218
Open roles today
$53–$135/hr
Typical pay range
$180k
Median, full-time
11
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Open machine learning engineer roles
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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... ...Highways, and Joint Development construction contracts. Project Field Engineers (…
join us on our mission of providing humankind access to the galaxy beyond our planet. About the RoleApex is seeking a Propulsion Engineer with experience in structures and hardware integration to design the propulsion mo…
us on our mission of providing humankind access to the galaxy beyond our planet. About the RoleApex is seeking a Senior Propulsion Engineer with deep expertise in structures and hardware integration to design the propuls…
player experience at a massive scale. As a Sr. Principal ML Engineer within the Central Product team, you will define and drive... ..., fair, and personalized through responsible, scalable machine learning systems. Respo…
$100k - $200k
Introduction Since 1973, East West Bank has served as a pathway to success. With over 110 locations across the U.S. and Asia, we are the premier financial bridge between the East and West. Our teams of experienced, multi…
$132.26k - $155.6k
every stage of your career. Try new things, learn new skills and discover what you excel... ...such as statistics, computer science, engineering or applied mathematics, or equivalent work... .../statistics, predictive mo…
connectivity layer across the AM fleet — PC-based machine control systems, PLC-based subsystems,... ...flow them down to OEMs and to internal engineering; verify what is delivered against them;... ...from the U.S. Depart…
management using Databricks Unity Catalog for ML governance.• Design and manage Databricks Feature Store for consistent feature engineering across training and inference pipelines.Generative AI & LLM Operations• Architec…
$157k - $235k
our audiences and our employees – and aim to leave a positive mark on culture. ML Platform Lead Engineer, Training & Inference Organization: Applied Machine Learning Group (AMLG) → ML Platform Level: Lead / Senior Lead (…
$156.8k - $235.2k
moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture. Lead Machine Learning Operations Engineer Personalization & Recommendation Systems Overview We’re hiring a Lea…
ML engineers at Riot own the full lifecycle of machine learning in production — from problem framing through model design, deployment, and operation — building systems that serve players at global scale. They work across…
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What machine learning engineers earn in Los Angeles
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $53–$73 | $110k–$152k |
| Mid level | $73–$100 | $152k–$207k |
| Senior | $97–$135 | $201k–$281k |
Adjusted for the Los Angeles 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
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ML Platform Lead
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