1

Weekend Machine Learning Jobs in Tulsa, OK (NOW HIRING)

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Machine Operator

Tulsa, OK · On-site

$18 - $24/hr

Willingness to work flexible hours, including early morning, nights, and weekends Benefits Overview ... If you are interested in learning the status of your application, please note you will be contacted ...

Machine Operator

Tulsa, OK · On-site

$15.50 - $18.50/hr

Willingness to work flexible hours, including early morning, nights, and weekends Benefits Overview ... If you are interested in learning the status of your application, please note you will be contacted ...

Summary We are seeking talented individuals to join our Data Science team, developing machine learning solutions and analytics capabilities that power our Intelligence Platform for casino clients.

Summary We are seeking talented individuals to join our Data Science team, developing machine learning solutions and analytics capabilities that power our Intelligence Platform for casino clients.

Works with large data sets in a hybrid cloud environment across multiple source systems, machine learning, artificial intelligence and other advanced statistical methods * Work with other internal ...

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 ...

next page

Showing results 1-20

Weekend Machine Learning information

What are the most commonly searched types of Machine Learning jobs in Tulsa, OK? The most popular types of Machine Learning jobs in Tulsa, OK are:
What are popular job titles related to Weekend Machine Learning jobs in Tulsa, OK? For Weekend Machine Learning jobs in Tulsa, OK, the most frequently searched job titles are:
What job categories do people searching Weekend Machine Learning jobs in Tulsa, OK look for? The top searched job categories for Weekend Machine Learning jobs in Tulsa, OK are:

Machine Learning Engineer

Bespoke Labs

Broken Arrow, OK • On-site

Full-time

Posted 22 days ago


Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

Must-Have Skills

3+ years of ML engineering experience — model training, fine-tuning, or post-training pipelines in research or production

Strong Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination