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. In the role of Electrical Engineer we'll count on y…
Engineering & IT · Tucson, AZ
Machine Learning Engineer jobs in Tucson, AZ
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
63
Open roles today
$41–$104/hr
Typical pay range
$139k
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... ...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... ...community, but around the world. In the role of Senior Resident Engineer, we'll c…
DescriptionAbout UsAt HDR, we specialize in engineering, architecture, environmental and construction services. While we are most well-known for adding beauty and structure to communities through high-performance buildin…
$68.9k - $131.1k
the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today’s mission and stay ahead of... ...Trust, Respect, Accountability, Collaboration, and Innovation. Learn More…
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…
test solutions while being accountable for product qualityWhy TI? Engineer your future. We empower our employees to truly own their career... ...possible for semiconductors to go into electronics everywhere. Learn more a…
$86.8k - $165.2k
the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today’s mission and stay ahead of... ...Collaboration, and Innovation.Relocation assistance available! Learn More…
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. HDR is looking for a Trenchless Engineer to join…
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. In the role of Senior Structural Engineer - Buil…
$68.9k - $131.1k
to explore the world of aerospace and defense? Do you want to learn from and collaborate with some of the greatest minds in the industry... ..., the Effector RF Design Department is seeking talented engineers with the sk…
them get to space — our world class Space Systems team is empowering some of the boldest and most ambitious space missions. MACHINE LEARNING ENGINEER II Rocket Lab Optical Systems solves mission-critical space domain and…
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What machine learning engineers earn in Tucson
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $41–$57 | $85k–$118k |
| Mid level | $57–$77 | $118k–$160k |
| Senior | $75–$104 | $155k–$216k |
Adjusted for the Tucson 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
04
ML Platform Lead
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