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Weekend Machine Learning Jobs in Indianapolis, IN

AI Software Engineer

Anderson, IN · On-site +1

$48.25 - $64/hr

If you want to know more about Big Data, artificial intelligence or machine learning and how they are changing the world, your place is here! We are an engineering and innovation company working in ...

Prepares and structures data for machine learning pipelines, feature engineering, and model lifecycle management * Implements model monitoring, performance validation, traceability, and ...

Prepares and structures data for machine learning pipelines, feature engineering, and model lifecycle management * Implements model monitoring, performance validation, traceability, and ...

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Weekend Machine Learning information

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Machine Learning Engineer

Bespoke Labs

Indianapolis, IN

Full-time

Posted 16 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