Senior AI Engineer (REMOTE)

About the role

Position Summary:

The Senior AI Engineer is a hands-on individual contributor who builds, trains, and evaluates machine learning models. The role combines knowledge engineering and data analysis with applied reinforcement learning, and owns work end to end, from data and experiment design through training, evaluation, and reporting.

The ideal candidate brings practical reinforcement learning experience, strong engineering discipline, and the ability to collaborate closely with domain experts and communicate results to technical and non-technical audiences.

Key Responsibilities - Work with domain experts to capture specialized knowledge and represent it in structured, usable form.

- Analyze source material and datasets for quality, consistency, and coverage, and resolve issues with the appropriate stakeholders.

- Perform exploratory and statistical analysis to inform data, modeling, and evaluation decisions.

- Create and curate training and evaluation datasets, using both automated and human validation.

- Design reinforcement learning environments, reward functions, and verifiers.

- Train and fine-tune language models using supervised and reinforcement learning methods such as PPO, DPO, and GRPO.

- Establish baselines, run ablations, and diagnose training issues such as reward hacking, instability, and data leakage.

- Follow the research literature and bring promising techniques into practice.

- Manage experiments rigorously, including versioned configurations, reproducible runs, experiment tracking, and compute budgeting.

- Design and extend evaluation frameworks, metrics, and benchmarks.

- Compare models and approaches through structured experiments and A/B testing.

- Document results, failures, and next steps in clear technical write-ups.

- Present findings to engineers, domain experts, and business leaders in language each audience can act on.

Requirements & Qualifications - Bachelor’s degree in computer science, machine learning, statistics, or a related field; MS or PhD preferred. Equivalent hands-on experience will be considered.

- 3+ years of hands-on experience training or fine-tuning language models.

- Demonstrated reinforcement learning experience, including designing an environment and reward function end to end and training a policy in it.

- Working knowledge of policy optimization methods for language models, such as PPO, DPO, or GRPO, including their failure modes.

- Strong Python and PyTorch skills, with fluency in the Hugging Face ecosystem and parameter-efficient fine-tuning techniques.

- Experience building and evaluating LLM-based agents or multi-turn systems.

- Experience designing evaluations for language models, including held-out sets and automated grading.

- Ability to analyze complex, detailed documentation and translate it into precise, structured, testable form.