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…
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.
87
Open roles today
$40–$102/hr
Typical pay range
$136k
Median, full-time
11
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Open machine learning engineer roles
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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... ...resilient communities and quality of life. We bring together planners, engineers,…
$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…
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. In the role of Data Engineer II, we'll c…
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 Analytics Engineer II, we'll count o…
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…
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 systems…
$118.2k
Agency: U.S. Army Corps of EngineersDepartment: Department of the ArmySub agency: Omaha District, Engineering Division - Dam and Levee Safety BranchSalary: Starting at $118,204 Per year (GS 13)Dates: Open 09/22/2026 to 1…
Company DescriptionCRB is a leading provider of sustainable engineering, architecture, construction and consulting solutions to the global life sciences and food and beverage industries. Our more than 1,100 employees pro…
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…
$25 - $35 per hour
more than 18,000 clients each year. Aerotek is an Allegis Group company, the global leader in workforce and business solutions. To learn more, visit: Aerotek.com | 1-888-AEROTEK. Aerotek offers weekly pay, employee benef…
02
What machine learning engineers earn in Omaha
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, 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.
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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
01
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
02
Senior ML Engineer
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Staff ML Engineer
04
ML Platform Lead
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