AI Engineer (REMOTE)

Role overview Build and evaluate machine learning systems as a hands-on individual contributor, owning work from knowledge capture and data preparation through model training and reporting. The work combines reinforcement learning, language model fine-tuning, data analysis, and evaluation, with close input from domain experts. The source describes roughly half the role as reinforcement learning and model training, with the remaining effort across knowledge engineering, analysis, and evaluation.

Responsibilities - Work with subject matter experts to capture specialized knowledge in structured, usable forms. - Assess source material and datasets for quality, consistency, and coverage; investigate and resolve gaps. - Create and validate training and evaluation datasets using automated and human review. - Design reinforcement learning environments, reward functions, and verifiers; train or fine-tune language models using supervised and reinforcement learning approaches. - Run reproducible experiments, establish baselines, compare methods, and investigate issues such as unstable training, reward hacking, or data leakage. - Build evaluation metrics and benchmarks, document findings, and communicate results to technical and non-technical audiences.

Requirements - Practical reinforcement learning and machine learning engineering experience. - Ability to manage experiments from design through implementation, training, evaluation, and reporting. - Strong data analysis and knowledge engineering skills, including dataset quality review. - Experience designing model evaluations and communicating what results mean. - Ability to work closely with domain experts and build a working understanding of their subject matter. - Careful handling of sensitive information, following HIPAA and internal data policies and using de-identified or synthetic data where possible.