Applied Machine Learning Research Scientist

Responsibilities

• Apply post-training techniques, including RLVR, RLHF, and GRPO, to improve model performance.
• Build and maintain evaluation pipelines to measure model performance across tasks and domains.
• Debug issues across the ML stack, including data pipelines, training jobs, model outputs, and mixed- or lower-precision computation.
• Collaborate with researchers to translate ML ideas into efficient, scalable implementations.
• Design, implement, and scale ML pipelines across all stages of LLM development, including pretraining, fine-tuning, and alignment.
• Work with large datasets, including dataset generation, filtering, and synthetic data approaches.
• Optimize training and inference workflows for performance, efficiency, and reliability.
• Contribute high-quality, maintainable code to shared ML infrastructure.

Requirements

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

Nice-to-Haves

• Experience with large language models, including training, fine-tuning, and evaluation.
• Familiarity with reinforcement learning concepts.
• Experience with distributed training frameworks such as FSDP and Megatron.
• Experience working with large-scale datasets and data pipelines.
• Experience debugging or optimizing ML systems for performance.
• Contributions to meaningful codebases, projects, or open-source systems.

Benefits

• Opportunity to build a breakthrough AI platform beyond the constraints of GPUs.
• Opportunities to publish and open-source cutting-edge AI research.
• Work on one of the fastest AI supercomputers in the world.
• Startup vitality with job stability.
• A non-corporate work culture that respects individual beliefs.