About this role

Design and maintain post‑training pipelines, from data ingestion through deployment
Develop reinforcement learning environments, reward models, and evaluation signals
Debug, optimize, and scale distributed training workloads
Design and execute research experiments and ablation studies
Develop benchmarks and evaluation metrics for model capability and alignment

Bachelor's degree (B.S. or B.A.) in Computer Science, Electrical Engineering, Mathematics, Statistics, or related STEM field
3+ years of experience in machine learning engineering, data science, ML research or modeling fine tuning
Programming: Python/C++ as the primary development language for ML research and engineering
Core ML fundamentals: LLM architectures, optimization, and model training fine tuning evaluation techniques.
Masters or PhD degrees are preferred (preferred qualification)
Hands-on experience implementing and scaling the full post-training pipeline for language models including supervised fine tuning and reinforcement learning (preferred qualification)
Previous experiences designing and building evaluation frameworks and benchmarks that measure model capability improvements and alignment quality (preferred qualification)
Modeling distillation quantization experience (preferred qualification)

Applied Machine Learning Engineer, LLMs, RL

Key skills