AI Research Scientist

About the Role

Were looking for an AI Research Scientist who blends frontier research curiosity with engineering discipline. Youll work at the core of our research efforts, training state-of-the-art models and contributing to training infrastructure.

This role is ideal for someone who thrives in high-performance environments, understands the nuances of training large models, and is obsessed with fast experimentation and applied usability.

You are a great fit if you are

1. Intellectually Curious
2. Productively Combative
   

Responsibilities

• Ask meaningful questions
• Conduct AI experiments
• Scrutinize results and iterate
  
• Adapt AI towards solving everyday problems, improving the lives of Menlo's users
• Learn and stay on SOTA

What you'll do

• Generate new ideas, implement experiments, debug training runs, and accelerate iteration
  
• Build and maintain modular, high-quality training codebases
• Develop and maintain efficient data loading pipelines and training utilities
• Ensure training jobs can scale across multiple GPUs and nodes (e.g., with DDP, NCCL)
• Optimize model training for performance, stability, and hardware utilization
• Maintain long-term code health: write clean, testable and reproducible code
• Contribute to open source dependencies

You Should Have

• Deep expertise in training codebases, e.g. PyTorch, or equivalent
• Proven experience training deep learning models in real-world research or production settings
• Strong engineering skills in Python (and optionally C++ for performance-critical components)
• Experience working with large datasets, complex pipelines, and real-world debugging
• Understanding of training dynamics: what goes wrong, and how to fix it
• Familiarity with job launchers, logging tools (e.g., Weights & Biases, TensorBoard), and checkpointing systems
• A mindset of engineering rigor applied to research readable code, thoughtful design, and reproducibility

Bonus Points

• Experience with TorchScript, ONNX, or custom inference runtimes
• Open Source contributions to PyTorch or ML tooling
• Experience working on transformer models, diffusion models, or large-scale vision/NLP tasks
• Familiarity with batch schedulers (SLURM), cluster environments, and GPU resource management
• Ability to collaborate closely with systems engineers or MLOps teams to ensure smooth integration

Why Join Us

• Collaborate with a world-class research team on meaningful, high-impact projects
• Own and shape the core training code infrastructure used daily by the team
• Work on real models, real data, and real scale not toy problems
• Help bridge the gap between research velocity and engineering quality
• Flexible work environment with a culture that values depth, clarity, and curiosity