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

Requirements

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.
Hands-on experiences implementing and scaling the full post-training pipeline for language models including supervised fine tuning and reinforcement learning.
Previous experiences designing and building evaluation frameworks and benchmarks that accurately measure model capability improvements and alignment quality.
Modeling distillation quantization experience

Benefits

Health insurance
Retirement plans
Paid time off

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonC++machine learning engineeringdata sciencereinforcement learningmodel trainingevaluation metricsmodel fine tuningdistributed trainingmodel distillation

Certifications

Bachelor's degreeMaster's degreePhD