AI/ML Research Engineer, LLM Post-Training & Evaluation

Role Overview

• Lead or co-lead technically complex ML engineering projects from initial customer discussions through implementation and delivery
• Design, build, and improve LLM training and post-training pipelines, including data ingestion, preprocessing, fine-tuning, evaluation, and experiment tracking
• Implement and optimize evaluation systems for LLMs and multimodal models, including offline benchmarks and task-specific test harnesses
• Integrate human-in-the-loop and AI-augmented evaluation signals into model development workflows
• Build robust infrastructure and tooling for reproducible experimentation, metrics logging, and regression monitoring
• Diagnose model behavior and pipeline failures, including data issues, training instability, metric inconsistencies, and evaluation drift
• Collaborate with Language Data Scientists and Applied Research Scientists to translate evaluation frameworks into executable systems
• Work closely with customer technical stakeholders to understand goals, constraints, and success criteria; propose and implement technically sound solutions
• Contribute to internal research and platform development, including benchmark frameworks, evaluation tooling, and post-training workflow improvements
• Contribute to best practices and standards for LLM training, evaluation, and quality assurance across projects
• Mentor junior engineers and contribute to technical design reviews, documentation, and engineering rigor across the team

Requirements

• BS/MS/PhD in Computer Science, Machine Learning, AI, Applied Mathematics, or related quantitative technical field (MS/PhD preferred)
• 2-3 years of relevant industry or research engineering experience in ML/AI systems
• Hands-on experience with LLM training / fine-tuning / post-training, including at least one of: supervised fine-tuning (SFT) preference optimization (e.g., DPO or related methods) RLHF / RLAIF-style workflows task
• or domain-adaptation of foundation models
• Strong programming skills in Python and experience building production-quality ML code
• Experience with modern ML frameworks (e.g., PyTorch, JAX, TensorFlow) and model libraries/tooling (e.g., Hugging Face ecosystem, vLLM, distributed training stacks)
• Experience designing and implementing evaluation pipelines for LLM/ML systems, including metrics computation, dataset handling, and experiment comparisons
• Strong understanding of data pipelines and ML systems engineering, including reproducibility, observability, and debugging
• Experience with large-scale distributed ML systems and performance optimization for training/evaluation workloads (GPU/accelerator environments preferred)
• Experience with large-scale data processing and workflow orchestration in support of model training/evaluation
• Ability to collaborate directly with technical stakeholders including research scientists, ML engineers, data engineers, and customer technical leads
• Strong written and verbal communication skills, including the ability to explain complex technical tradeoffs to both technical and non-technical audiences.

Tech Stack

• Python
• PyTorch
• Tensorflow

Benefits

• Health insurance
• Retirement plans