for the FOX Network’s national content. JOB DESCRIPTION KTTV Fox 11 is seeking a versatile, hands-on Transmitter and Studio Engineer to support both our primary broadcast transmitter facility atop Mt. Wilson and our West…
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.
251
Open roles today
$53–$135/hr
Typical pay range
$180k
Median, full-time
5
Fresh in this list
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01
Open machine learning engineer roles
12 shown of 251 · sorted by freshness
$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…
$130k - $160k
ABOUT YOU We're seeking a Lead AI/ML Engineer to architect and scale our data infrastructure supporting personalization, churn prediction... ...with Data Science, ML, and Backend teams to productionize machine learning f…
$35 - $40 per hour
and maintenance, including lighting controls, energy management systems and breaker boxes. Responsible for overall mechanical, engineering and plumbing (MEP) systems, including troubleshooting, repair and escalation as n…
$120k - $140k
Airport, The Marvin Group consists of Marvin Engineering (MEC), Marvin Test Solutions (MTS) and... ...of facility equipment including CNC machines (Mazak, Okuma, DMG), process ovens, paint... ...eligibility of all newly…
$148.7k - $199.4k
audiences experience sports, entertainment & news. Product Engineering is a unified team responsible for the engineering of Disney... ...personalization and live sports experiences. As a Machine Learning Engineer, you wi…
$229.2k - $319.5k
become more varied and our experiences become more dynamic, machine learning is an increasingly important part of how we help players discover... ...fair, compelling matches. As a Staff Machine Learning Engineer embedded…
$148.7k - $199.4k
audiences experience sports, entertainment & news. Product Engineering is a unified team responsible for the engineering of Disney... ...responsible for building robust data pipelines and advanced machine learning platfo…
**Job Title:** Senior AI/ML Engineer **Job Type:** Full-time / Long-term Contract **Experience Required:** 7+ Years **Location:... ...solutions. You will work on real-world AI projects involving machine learning models,…
Support Services is seeking a highly experienced Senior AI/ML Engineer to design, develop, and deploy advanced AI solutions. This... ...~7+ years of Microsoft Azure experience , including Azure Machine Learning ~7+ years…
$245k - $272k
success. Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy . AI is a... ...technical standards, drive architectural decisions, and mentor engineers across the organi…
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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.
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.
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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.
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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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