Engineering & IT · Omaha, NE

Machine Learning Engineer jobs in Omaha, NE

Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.

72
Open roles today
$40–$102/hr
Typical pay range
$136k
Median, full-time
3
Fresh in this list

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01

Open machine learning engineer roles

11 shown of 72 · sorted by freshness

Field Service Engineer - AV, USA

Evertz Microsystems Limited · Omaha, NE · Full-time

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…

Posted 6d ago
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$120.4k - $188.13k

grow, and succeed – today and into tomorrow. As an Electrical Engineer in our Advanced Manufacturing group, you’ll contribute to... ...Analytical and problem-solving skills Forward thinking, eager to learn best practices…

Posted 1w ago
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Data Scientist - AI Systems

Mutual of Omaha · Omaha, NE · Full-time
$110k - $130k

Stack Logic Integration: Ensure seamless flow of data between the backend, the GenAI system, and the UI by collaborating with cloud engineers, data engineers, and UI/UX developers as needed to deliver cohesive end-to-end…

Posted 1w ago
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Campus Facilities Engineer

Archdiocese of Omaha · Omaha, NE · Full-time

to: Business & Finance Director Principal Duties and Responsibilities St. Robert Bellarmine is hiring a Campus Facilities Engineer (CFE) to ensure the safe, efficient operation of our facilities and campus by maintaining…

Posted 1w ago
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Senior Proccess Engineer

Bluestem Biosciences · Omaha, NE · Full-time

S. competitiveness and securing our nation's supply chains - while reinvesting in agricultural America. Bluestem Biosciences has engineered a breakthrough in American manufacturing. Our proprietary biomanufacturing proce…

Posted 1w ago
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$25 - $35 per hour

HIRING - M aintenance engineer - APPLY SOON!!!! - PAY - $25-35 - CAN START ASAP !!!!! We are seeking a skilled Maintenance Engineer... ...company, the global leader in workforce and business solutions. To learn more, vis…

Posted 2w ago
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Resident Storage Engineer

MetroSys · Omaha, NE · Temporary

Position Overview MetroSys is seeking an experienced Resident Storage Engineer to provide onsite operational and engineering support for a large-scale enterprise storage environment during a strategic migration from NetA…

Posted 3w ago
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Plant Engineer - Full Time - Nights

Omaha Nursing And Rehabilitation Center · Omaha, NE

fields preferred. 1 year previous experience in hospital plant operations preferred. License/Certifications ~ Third Grade Engineers License required at assigned facilities Skills/Knowledge/Abilities Knowledge of mechanic…

Posted 5mo ago
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Professional Engineer - 2820.17

Wade Trim · Omaha, NE

Rotation Program, Mentor Program, Sustainability Program, and Wellness Program. Position Description: We are looking for a Senior Engineer to join our Water Team in Omaha. The candidate must have a bachelor's degree in c…

Posted 11mo ago
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Engineer Summer Intern - #2810.17

Wade Trim · Omaha, NE

Rotation Program, Mentor Program, Sustainability Program, and Wellness Program. Position Description: We are looking for an Engineer Summer Intern to join our Water Resources Team to improve infrastructure in our Omaha o…

Posted 11mo ago
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02

What machine learning engineers earn in Omaha

Hourly first — that's how the offer arrives

ExperienceHourlyAnnual, full-time
Entry level $40–$55 $83k–$115k
Mid level $55–$75 $115k–$156k
Senior $73–$102 $152k–$212k

Adjusted for the Omaha 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.

06

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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