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Temporary Machine Learning Engineer Jobs in Tulsa, OK

Summary We're looking for a Machine Learning Engineer to design, deploy, and operate production ML systems on Amazon Web Services. You'll own the full lifecycle in a real-world, high-stakes ...

Summary We're looking for a Machine Learning Engineer to design, deploy, and operate production ML systems on Amazon Web Services. You'll own the full lifecycle in a real-world, high-stakes ...

Summary We're looking for a Machine Learning Engineer to design, deploy, and operate production ML systems on Amazon Web Services. You'll own the full lifecycle in a real-world, high-stakes ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Junior PL/SQL Developer

Tulsa, OK

$58.10K - $75.40K/yr

Currently, We are looking for entry-level software programmers, Java Full stack developers, Python/Java developers, Data analysts/ Data Scientists, Machine Learning engineers. Who Should Apply Recent ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

... for machine learning pipelines, feature engineering, and model lifecycle management - Implements model monitoring, performance validation, traceability, and reproducibility of AI artifacts ...

... for machine learning pipelines, feature engineering, and model lifecycle management - Implements model monitoring, performance validation, traceability, and reproducibility of AI artifacts ...

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Temporary Machine Learning Engineer information

See Tulsa, OK salary details

$28.8K

$117.6K

$176.7K

How much do temporary machine learning engineer jobs pay per year?

As of May 30, 2026, the average yearly pay for temporary machine learning engineer in Tulsa, OK is $117,614.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,700.00 and $141,600.00 per year, depending on experience, location, and employer.

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 Tulsa, OK? The most popular types of Machine Learning Engineer jobs in Tulsa, OK are:
What job categories do people searching Temporary Machine Learning Engineer jobs in Tulsa, OK look for? The top searched job categories for Temporary Machine Learning Engineer jobs in Tulsa, OK are:
Infographic showing various Temporary Machine Learning Engineer job openings in Tulsa, OK as of May 2026, with employment types broken down into 1% Internship, 32% Full Time, 57% Part Time, 5% Temporary, and 5% Contract. Highlights an 97% Physical, and 3% Remote job distribution, with an average salary of $117,614 per year, or $56.5 per hour.
Machine Learning Engineer (AWS)

Machine Learning Engineer (AWS)

CCT

Tulsa, OK

Other

Posted 28 days ago


Job description

Summary
 
We're looking for a Machine Learning Engineer to design, deploy, and operate production ML systems on Amazon Web Services. You'll own the full lifecycle in a real-world, high-stakes environment - from training and packaging through deployment, monitoring, retraining, security, and cost control.
 
This role sits at the intersection of ML engineering and MLOps and is core to CCT's analytics strategy. You'll partner closely with data scientists, engineers, and product stakeholders to turn complex time-series and transactional data into reliable, observable, and cost-effective ML services that our customers can trust.
 
You'll thrive here if you naturally dig into why models behave the way they do, enjoy tracing issues to their root cause, and like collaborating across disciplines to ship robust systems that are built to last.
What You'll Do
  • Build and maintain reproducible model training workflows on AWS (SageMaker, S3, Glue, etc.), making retraining, rollback, and experimentation routine rather than heroic.
  • Deploy and operate real-time and batch inference services with full CI/CD pipelines, versioning, and safe rollout strategies (canary, shadow, A/B) so changes are deliberate and observable.
  • Instrument production models for performance, data drift, latency, and errors - and automate retraining triggers when models drift out of tolerance.
  • Maintain model lineage, auditability, and traceability to meet the compliance, governance, and reporting needs of the regulated gaming industry.
  • Enforce least-privilege IAM, encryption, and secure data access patterns across the entire ML platform.
  • Treat cost as a first-class engineering metric - right-size infrastructure, balance batch vs. real-time workloads, and continually reduce platform spend without sacrificing reliability.
  • Collaborate with engineers, data scientists, and product teams to translate business problems into ML solutions, communicate tradeoffs clearly, and iterate based on feedback.
  • Continuously explore new AWS services, ML frameworks, and deployment patterns to improve reliability, observability, and developer velocity on the ML platform.
Requirements
  • 3+ years of experience in machine learning engineering, MLOps, or a closely related discipline.
  • Hands-on experience with AWS ML and data services - SageMaker (training, endpoints, pipelines), S3, Lambda, Step Functions, CloudWatch, MWAA (Apache Airflow).
  • Experience working with time series data, including feature engineering, seasonality handling, and temporal train/test splits.
  • Strong Python skills and familiarity with common ML frameworks (scikit-learn, PyTorch, XGBoost, or equivalent).
  • Experience building and maintaining CI/CD pipelines for ML systems.
  • Demonstrated ability to monitor and debug production ML systems - latency, drift, errors, and data quality - and drive issues to root cause.
  • Comfort with SQL and working with structured data at scale.
  • Able to work collaboratively across teams, assume positive intent, and communicate clearly with both technical and non-technical stakeholders.
  • Track record of self-directed learning and technical growth in areas like AWS, ML frameworks, or deployment patterns.
Nice to Have
  • Experience in a regulated industry (gaming, finance, healthcare) where auditability, explainability, and compliance are first-class concerns.
  • Familiarity with feature stores, model registries, or ML metadata tools (e.g., MLflow, SageMaker Model Registry).
  • Experience with infrastructure-as-code (Terraform, CDK, or CloudFormation).
  • Exposure to data drift detection libraries or custom drift monitoring implementations.
Success Looks Like
  • Production models run reliably with clear, measurable business impact for casino operators.
  • Failures are observable, recoverable, and explainable - with logs, metrics, and traces that tell the full story.
  • ML systems scale predictably with usage and data volume, without runaway cost.
  • The ML platform becomes a trusted, well-understood part of CCT's product ecosystem - for both internal teams and external customers.

About CCT 

CCT is the creator of Casino Insight, the award-winning platform trusted by more than 350 casinos worldwide to automate cage operations, revenue audits, and operational analysis. Since 2012, Casino Insight has helped casinos replace manual work with streamlined workflows, improving accuracy, compliance, and profitability. 

Headquartered in Tulsa, Oklahoma, CCT integrates seamlessly with leading casino management, hospitality, and financial systems-delivering measurable ROI and empowering teams to work smarter at every level. 

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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