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Temporary Machine Learning Engineer Jobs in Oklahoma

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

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Temporary Machine Learning Engineer information

What is the difference between Temporary Machine Learning Engineer vs Data Scientist?

AspectTemporary Machine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related fields; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentProject-based, often contract roles in tech or finance companiesResearch and analysis-focused, in tech, finance, or healthcare sectors
Employer UsageUsed for short-term ML projects, model deployment, or prototypingUsed for data analysis, insights, and predictive modeling

Temporary Machine Learning Engineers focus on implementing and deploying ML models on a short-term basis, often within project deadlines. Data Scientists analyze data to generate insights and develop models but may have a broader scope. Both roles require strong technical skills, but their primary functions differ in scope and application.

What are the most commonly searched types of Machine Learning Engineer jobs in Oklahoma?

The most popular types of Machine Learning Engineer jobs in Oklahoma are:

What are popular job titles related to Temporary Machine Learning Engineer jobs in Oklahoma?

For Temporary Machine Learning Engineer jobs in Oklahoma, the most frequently searched job titles are:

What job categories do people searching Temporary Machine Learning Engineer jobs in Oklahoma look for?

The top searched job categories for Temporary Machine Learning Engineer jobs in Oklahoma are:

What cities in Oklahoma are hiring for Temporary Machine Learning Engineer jobs?

Cities in Oklahoma with the most Temporary Machine Learning Engineer job openings:

Infographic showing various Temporary Machine Learning Engineer job openings in Oklahoma as of July 2026, with employment types broken down into 81% Full Time, and 19% Contract. Highlights an 91% In-person, and 9% Remote job distribution.

Applied Machine Learning Engineer

Oklahoma City, OK โ€ข On-site

ANAUTICS INC
Guided Missile and Space Vehicle Manufacturingย โ€ขย 11 - 50 employees

Other

Posted 8 days ago


Job description

Take models from notebooks to production systems that serve real operational workflows.

Impact
  • Convert data science ideas into reliable model services.
  • Build feedback loops that improve model performance over time.
  • Help teams use AI where it creates measurable operational value.
What you'll do
  • Implement training, inference, feature, evaluation, and monitoring pipelines.
  • Work with data scientists to harden models for production use.
  • Build APIs, batch jobs, and workflows around ML capabilities.
  • Improve model reliability, latency, cost, and explainability.
What we're looking for
  • 4+ years of ML engineering or applied machine learning experience.
  • Strong Python and production software fundamentals.
  • Experience deploying models or ML-powered services.
  • U.S. Citizenship is required.
  • Must be capable of obtaining and maintaining a Top Secret security clearance.
  • Comfort working with ambiguous mission, data, and engineering problems.
  • Strong written communication and ability to explain technical decisions to technical and non-technical teams.
Preferred experience
  • AWS experience is a plus, especially secure cloud, GovCloud, serverless, data, or AI services.
  • AI, machine learning, LLM, agentic workflow, or decision intelligence experience is a plus.
  • Experience in defense, government, manufacturing, aerospace, logistics, or other mission-critical environments is a plus.
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