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Engineering & IT · Phoenix, AZ
Machine Learning Engineer jobs in Phoenix, AZ
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
250
Open roles today
$44–$113/hr
Typical pay range
$150k
Median, full-time
6
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01
Open machine learning engineer roles
11 shown of 250 · sorted by freshness
$100k - $150k
Machine Learning Research 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…
$100k - $150k
ML Performance Engineer - Remote Bright Vision Technologies is a technology consulting and software development company delivering... ...in Python and C++. Hands-on experience optimizing deep learning workloads on modern…
$100k - $150k
ML Infrastructure Engineer - Remote Bright Vision Technologies is a technology consulting and software development company delivering... ...resume to ****@*****.*** or contact us at (***) ***-****. Learn more about Brigh…
$100k - $150k
ML Security Engineer- Remote Bright Vision Technologies is a technology consulting and software development company delivering... ...response capabilities specifically tailored to AI and machine learning systems. The rol…
$100k - $150k
MLOps Engineer - Remote Bright Vision Technologies is a technology consulting and software development company delivering... ...performance, highly reliable inference platforms for serving large machine learning models i…
Kforce has a client that is seeking a Machine Learning Operations (MLOps) Engineer (Snowflake) in Phoenix, AZ. Summary: We are seeking a Senior MLOps Engineer to help design and build an enterprise-scale machine learning…
$28 - $32 per hour
that hold similar values, which is why we do not put limits on learning, development, industry, and personal growth. Start your path... ...including various test meters, hand and power tools. Able to read engineering dra…
Los Angeles, CA; Concord, CA; Salt Lake City, UT. As a Field Engineer , you will be working in one of these market sectors:... ...Field Engineers at Harder perform the following daily tasks: Learn, follow, and promote Ha…
Job Description Job Description Role Description This is a contract, on-site role for a Python AI/ML Senior Engineer, based in Phoenix, AZ. The responsibilities include designing, developing, and deploying robust AI/ML m…
$35 - $50 per hour
FIELD SERVICE ENGINEER Seeking a Field Service Engineer who thrives in fast-paced industrial environments and is comfortable traveling... .... MUST HAVE EXPERIENCE WORKING ON DRYERS, BLENDERS, EXTRUSION MACHINES, AND CON…
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What machine learning engineers earn in Phoenix
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $44–$62 | $92k–$128k |
| Mid level | $62–$83 | $128k–$173k |
| Senior | $81–$113 | $168k–$235k |
Adjusted for the Phoenix 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
01
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
03
Staff ML Engineer
04
ML Platform Lead
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