$27 - $32 per hour
MedTech is seeking Mechanical Field Service Engineers to support a nationwide medical device... ...hydraulic assemblies on dialysis machines. Complete required rinse, flush, testing... ...outcomes and population health w…
Engineering & IT · Kansas City, MO
Machine Learning Engineer jobs in Kansas City, MO
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
182
Open roles today
$41–$104/hr
Typical pay range
$139k
Median, full-time
2
Fresh in this list
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01
Open machine learning engineer roles
9 shown of 182 · sorted by freshness
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…
Summary In this role, the Gas Engineer I position will assist the Engineering Manager and Field Operations and is responsible for designing... ...improvements. Essential Characteristics Ability to learn industry standard…
Field Service Engineer Here at Siemens, we take pride in enabling sustainable progress through technology. We do this through empowering... ...technology with purpose adding real value for customers. Learn more about Sie…
$80k - $120k
Plumbing Engineer Location: Kansas City, MO, 64112 Country: United States Salary: $80000-$120000 Start Date: Description: Job Description We are seeking a Plumbing Engineer to assist project teams in preparing plumbing s…
employees are dedicated to customers' pursuit of discovery and resolution to global challenges. Territory: This Field Service Engineer will primarily support customers in the Kansas City, MO metropolitan area , with addi…
$75k - $77k
Field Service Engineer I Location: Kansas City, MO Salary: $75,000 - $77,000 per year Who are we? Established in 1975, Shimadzu Scientific Instruments is one of the largest suppliers of analytical instrumentation, physic…
Description Position at Samtec, Inc Founded in 1976, Samtec is a privately held, $950 million global manufacturer of a broad line of electronic interconnect solutions, including High-Speed Board-to-Board, High-Speed Cabl…
$17 - $19 per hour
Job description: Job Description Job Title: Maintenance Engineer Department: Engineering Reports To: Chief Engineer FLSA... ...electronic units and systems.* 6. Repairs or adjusts equipment, machines, or defective compon…
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What machine learning engineers earn in Kansas City
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 Kansas City 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
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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.
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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