$71.3k - $140.6k
Position Summary Our Deloitte AI & Engineering team works to transform technology platforms, drive innovation, and help make... ...request handlingExperience with DockerExperience supporting machine learning workflowsExp…
Engineering & IT · Salt Lake City, UT
Machine Learning Engineer jobs in Salt Lake City, UT
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
92
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$45–$114/hr
Typical pay range
$152k
Median, full-time
12
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Open machine learning engineer roles
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become part of a global team of over 50,000 planners, designers, engineers, scientists, digital innovators, program and construction... ...00 firm that had revenue of $16.1 billion in fiscal year 2025. Learn more at aeco…
trusted partner for the world's top brands, offering comprehensive engineering, supply chain, and manufacturing solutions. With 60 years of... ...production data and implement corrective actions to reduce machine- genera…
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 Mechanical Engineer (Pipe S…
stewardship. With a top-tier industry ranking, our team delivers comprehensive solutions that span waste planning, remediation, engineering and implementation. You’ll collaborate closely with clients and communities to d…
We are looking for a Data Engineer to help design and enhance data solutions that support reliable reporting and analytics in Salt Lake City, Utah. This role focuses on building scalable data pipelines, shaping well-stru…
difference 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.HDR Engineering is currently seeking an E…
difference 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.HDR Engineering is currently seeking a Me…
$74.2k - $129.8k
location*Amazon's Reliability Maintenance Engineering (RME) organization is responsible for... ...equipment information, performance metrics and machine histories and make recommendations for... ..., paid time off, and p…
$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…
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 Mechanical Engineer, we'll count on…
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…
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What machine learning engineers earn in Salt Lake City
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $45–$62 | $93k–$129k |
| Mid level | $62–$84 | $129k–$175k |
| Senior | $82–$114 | $170k–$237k |
Adjusted for the Salt Lake City 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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