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Engineering & IT · Oklahoma City, OK
Machine Learning Engineer jobs in Oklahoma City, OK
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
67
Open roles today
$38–$99/hr
Typical pay range
$131k
Median, full-time
1
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Open machine learning engineer roles
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Job Description Job Description The YouVersion Data Engineer will develop and build data pipelines and ETL (Extract, Transform, Load... ....Church is to uphold and represent the beliefs of Life.Church. Learn more about w…
to 5% * Medical/Dental/Vision Insurance after 30 days * Competitive Pay * Career Development * Welcome to Love's: The Data Engineer II designs, builds, and supports scalable data solutions that advance Love’s enterprise…
$104k - $208k
prefer prior experience as a Software Developer, Coder, Software Engineer, or Programmer. We can only offer this opportunity to... ...JavaScript Kotlin Python React Swift TypeScript Machine Learning More: We are DataAnno…
Job Description Job Description FSB has joined Colliers Engineering & Design. Read about it here. FSB is a full-service Architecture and Engineering firm in Oklahoma City, OK / OKC, built on over 80 years of design excel…
Data Engineer Location: Onsite in OKC – 5 days/week Employment Type: Direct Hire Work Authorization: Must be authorized to work in the U.S. now and in the future without sponsorship We’re partnering with a client on a di…
$110k - $120k
Job Description — Field Service Engineer II (LINAC) | Direct Hire (W2) Position: Field Service Engineer II (FSE II) – Linear Accelerators... ...discussed during interview). Engineers typically support 2–3 machines on ave…
dynamics. As part of Analytics team, this role develops advanced machine learning and analytical solutions that shape customer strategy and... ...reporting and analytical workflows Work with data engineering to build sca…
Job Description Job Description The YouVersion Senior Data Engineer is primarily responsible for shaping, implementing, and maintaining... ...instincts around testing, monitoring, and data correctness. Learning Orientati…
$45 - $55 per hour
Position Overview We are seeking a highly skilled Field Service Engineer to install, commission, maintain, and troubleshoot industrial material handling and process equipment at customer manufacturing facilities across N…
$35 - $50 per hour
FIELD SERVICE ENGINEER Seeking a Field Service Engineer who thrives in fast-paced industrial environments and is comfortable traveling... .... MUST HAVE EXPERIENCE WORKING ON DRYERS, BLENDERS, EXTRUSION MACHINES, AND CON…
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What machine learning engineers earn in Oklahoma City
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $38–$53 | $80k–$111k |
| Mid level | $53–$73 | $111k–$151k |
| Senior | $71–$99 | $147k–$205k |
Adjusted for the Oklahoma City 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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