Robotics Research Scientist

Menlo Research Pte Ltd; Singapore; On-site; SGD 75,000 - 95,000; Full time

Job summary

A leading research organization in Singapore is seeking a Research Scientist to enhance their training infrastructure. This role involves maintaining a PyTorch codebase, optimizing model training, and ensuring reproducibility in experiments. Candidates should have strong expertise in deep learning and Python, with a passion for engineering rigor applied to research. This position offers a collaborative work environment focused on impactful projects.

Benefits

Collaborative research team

Flexible work environment

Qualifications

• Proven experience training deep learning models in real-world settings.
• Ability to optimize model training for performance and stability.
• Familiarity with checkpointing systems and logging tools.

Responsibilities

• Maintain a high-quality PyTorch training codebase.
• Design workflows for scaling and reproducibility.
• Optimize model training across multiple GPUs and nodes.

Skills

Deep expertise in PyTorch

Strong engineering skills in Python

Experience with large datasets

Understanding of training dynamics

Tools

Weights & Biases

TensorBoard

TorchScript

Job description

About the Role

We’re looking for an Research Scientist who blends frontier research curiosity with engineering discipline. You’ll work at the core of our research efforts, training state-of-the-art models and building 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 making experimentation fast, reproducible, and reliable.

What You’ll Do

• Own and maintain a modular, high-quality PyTorch training codebase
• Design and build training workflows for scaling, checkpointing, logging, and reproducibility
• Implement new ideas, debug training runs, and accelerate iteration
• 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: organize modules, enforce standards, write clean and testable code
• Contribute to experiment tracking, reproducibility, and versioning infrastructure

You Should Have

• Deep expertise in PyTorch, including custom modules, loss functions, and distributed training
• 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 For

• Experience with TorchScript, ONNX, or custom inference runtimes
• Contributions to PyTorch or open-source 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