$140k - $293k
experience with a bachelors degree in Computer Science, Computer Engineering, Electrical Engineering, or Mathematics; or 6 years of... ...a degree. We need strong expertise in Python and modern machine learning framework…
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.
379
Open roles today
$48–$122/hr
Typical pay range
$162k
Median, full-time
4
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01
Open machine learning engineer roles
12 shown of 379 · sorted by freshness
$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…
$245k - $321k
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 organ…
Qualifications: College or University education in Electrical, Electronic, or Computer disciplines Passion for technology and learning new software and hardware products Hands-on experience with IP Networking, server har…
the position. While a foundational understanding of data center engineering principles, critical infrastructure systems, and operational... ...law. We use artificial intelligence in our hiring process. Learn more here .…
Class of Q1' 2026 supporting the Data Center Critical Facilities Engineer. The training will be on the cutting-edge of technology in a... ..., or HVAC and skilled Mechanical trades? Or desire to learn a new skill or trad…
$77.4k - $135.4k
this role, you will design, build, and deploy scalable AI and machine learning solutions that drive business impact. You will contribute... ...relevant innovations into team solutions. Collaborate with engineers, data sc…
$28.85 - $43.27 per hour
Act as a subject expert and resource for training How will you get here? This position is ideal for an experienced service engineer wanting to be involved with a team with varied strengths and exposed to a constant varie…
$82k - $108k
Description: The CLPE Maintenance Engineer II will work with the Engineering Maintenance Team to provide technical expertise in distribution... ...lifecycle,” we mean it—every step, every phase, every part. Learn more ab…
$45 - $55 per hour
Conception to Reality Inc currently seeking a Senior Field Engineer to join our team. Who we are: Conception to Reality, Inc. (CtR) offers project management, engineering, and construction management expertise to large-s…
the globe. We are looking for a Director, Applied AI & ML Engineering to be part of revolutionizing these industries. The Director... ..., and built. This role will drive the integration of AI and machine learning across…
$200k - $245k
workplace, both physically and virtually. Learn more about our Total Rewards philosophy... ...AI/ML team and seasoned leaders in Engineering, Product, Design, Data Science, Marketing... ...the unified service platform st…
02
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.
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.
05
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.
04
Include LLM work with specifics — fine-tuning, RAG, evaluation — if you have it; vague 'GenAI experience' claims read poorly.
05
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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