Applied Machine Learning Research Scientist

What you'll do

• Design, implement, and scale LLM pipelines for pretraining, fine-tuning, and alignment, including post-training techniques to improve model performance.
• Build evaluation pipelines; debug data, training, model-output, and precision issues; optimize training and inference workflows.
• Collaborate with researchers to implement ML ideas efficiently and contribute maintainable code to shared infrastructure.

What they ask for

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 4+ years of experience working with machine learning systems, including internships, research, or industry experience.
• Strong programming skills in Python and experience with ML frameworks such as PyTorch.
• Solid understanding of ML fundamentals; familiarity with deep learning architectures, particularly transformers; ability to understand modern ML papers and implement key ideas.

Nice to have

• Experience with LLMs, reinforcement learning, distributed training frameworks (e.g., FSDP, Megatron), or large-scale datasets and data pipelines.
• Experience debugging or optimizing ML systems, or contributions to meaningful codebases, projects, or open-source systems.

Tools

Python, PyTorch, FSDP, Megatron, RLVR, RLHF, GRPO

AI focus

Practical LLM development, including pretraining, fine-tuning, reinforcement learning-based post-training, evaluation, and optimization of training and inference.