$94k - $134k
per year Requirements: We are looking for proven experience as an ML Engineer, Data Engineer, or Software Engineer, with a strong emphasis on deploying, monitoring, and scaling machine learning systems in production. We…
Engineering & IT · Denver, CO
Machine Learning Engineer jobs in Denver, CO
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
377
Open roles today
$48–$122/hr
Typical pay range
$162k
Median, full-time
11
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Open machine learning engineer roles
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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…
$70 - $93 per hour
Job Title: Machine Learning Engineer Clearance Required: TS/SCI Location: Centennial, CO | Hybrid About Us Grey Matters Defense Solutions, LLC is a specialized firm in software development, data analytics, and advanced r…
$89.8k - $229.3k
position requires a strong command of statistical techniques machine learning algorithms, and big data technologies. The ideal candidate will... ...modeling as a Scientist, Consultant, Architect, DBA, or Engineer 7+ year…
$82.1k - $172.4k
position is for a Senior Computer Vision AI/ML Engineer looking for new and challenging problems... ..., optimizing, and deploying deep learning CV models that provide analysts with actionable... ...government security c…
$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…
$159.2k - $215.3k
business outcomes. You will apply your broad expertise across machine learning, statistical modeling, and experimentation to tackle complex... ...clear and backed by data.About the teamOur team designs and engineers high…
$65 - $75 per hour
DescriptionKforce has a client that is seeking a Data Infrastructure Engineer in Greenwood Village, CO.Summary:We're looking for a Data... ...* Kafka or other streaming technologies* Databricks* MLflow/ machine learning…
complex, cross-functional initiatives across analytics, data engineering, product, technology and client teams to ensure high-quality... ...oversight and quality assurance for statistical analyses, machine learning model…
ourselves: What is our impact on the world?We believe building engineering is more than systems and structures, it’s about powering progress... ...ability to change the world for the better. Read further to learn how you…
our impact on the world?Watch Our Story:' We believe building engineering is more than systems and structures, it’s about powering progress... ...ability to change the world for the better. Read further to learn how you…
$275k - $305k
Offer amounts are determined by role, level, and location. Learn more about our [Total Rewards philosophy]( AI is a... ...the company. You will lead a broad organization spanning Machine Learning Engineering, ML Platform…
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What machine learning engineers earn in Denver
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $48–$66 | $99k–$138k |
| Mid level | $66–$90 | $138k–$187k |
| Senior | $88–$122 | $182k–$253k |
Adjusted for the Denver 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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