Senior AI Engineer

This is a fully remote position, open to applicants in Tennessee.

📋 Description

• Develop, train, and assess machine learning models.

• Gather specialized knowledge from subject matter experts and convert it into structured, usable formats.

• Evaluate source materials and datasets for quality, consistency, and coverage, addressing issues with stakeholders.

• Conduct exploratory and statistical analyses to guide data, modeling, and evaluation choices.

• Generate and curate training and evaluation datasets through both automated and manual validation methods.

• Create reinforcement learning environments, design reward functions, and establish verifiers.

• Train and refine language models using both supervised and reinforcement learning techniques, including PPO, DPO, and GRPO.

• Set baselines, execute ablation studies, and troubleshoot reward hacking, instability, and data leakage.

• Keep abreast of research literature and implement promising methodologies.

• Manage reproducible experiments with version-controlled configurations, experiment tracking, and compute budgeting.

• Develop and enhance evaluation frameworks, metrics, and benchmarks.

• Compare models and methodologies through structured experiments and A/B testing.

• Document results, setbacks, and subsequent actions in technical reports.

• Share findings with engineers, domain experts, and business leaders.

• Manage experiments from start to finish as a fully engaged individual contributor.

• Work collaboratively with domain experts and adjust data, training, and evaluation strategies as knowledge progresses.

⛳️ Requirements

• Bachelor’s degree in computer science, machine learning, statistics, or a related discipline; MS or PhD preferred.

• Equivalent practical experience will be taken into account.

• Over 3 years of hands-on experience in training or fine-tuning language models.

• Proven experience in reinforcement learning, including the end-to-end design of environments and reward functions, as well as training a policy within them.

• Proficient understanding of policy optimization methods for language models, such as PPO, DPO, or GRPO, along with their potential failure modes.

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

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

• Familiarity with designing evaluations for language models, encompassing held-out sets and automated grading.

• Ability to interpret complex, detailed documentation and convert it into clear, structured, testable formats.

• Proficient technical writing skills, capable of succinctly explaining experiments and their outcomes.

• Comfortable collaborating directly with domain experts who may not have a background in machine learning.

• Experience with healthcare data or within another highly regulated industry is strongly preferred.

• Preferred experience in training models to generate structured or schema-constrained outputs.

• Highly preferred experience with a wide array of reinforcement learning algorithms and advanced reward design.

• Experience with knowledge representation techniques, such as knowledge graphs or ontologies, is highly preferred.

• Preferred experience in generating and validating synthetic data for model training.

• Publications or contributions to open-source projects in reinforcement learning, language model fine-tuning, or model evaluation are highly preferred.

• Preferred experience in handling sensitive data, including protected health information (PHI) under HIPAA regulations.

• Must maintain timely and regular attendance.

• An equivalent combination of education and experience will be considered.

• Ability to satisfactorily perform each essential duty; reasonable accommodations may be made for qualified individuals with disabilities.

• Commitment to EnableComp’s Core Values, Vision, and Mission.

• Adherence to HIPAA and EnableComp data policies.

🏝️ Benefits

• Comprehensive health insurance options.

• Competitive salary and performance-based bonuses.

• Opportunities for professional development and continuing education.

• Flexible work hours and remote work options.

• Supportive work environment that values innovation and collaboration.