Overview

Machine Learning Engineer, Robotic Foundation Models About MetAntz MetAntz has one of the most advanced GenAI recruitment platforms.

Responsibilities

• Training and post-training: Train and post-train robotic foundation models, including vision-language-action models, using imitation learning and reinforcement learning. You’ll own the work from data loading through multi-GPU training runs.
• Training infrastructure: Build and scale distributed multi-node GPU training, data loaders that keep GPUs fed, and reproducible pipelines with experiment tracking.
• Simulation: Build simulation environments for our tasks. Train policies with reinforcement learning that transfer to real robots.
• Rewards and verifiers: Design the reward functions and the verifiers that check whether a task step actually succeeded.
• Evaluation: Build harnesses that tell us whether a model actually got better, in simulation and on real hardware. Design the metrics that decide what ships.
• Data and data infrastructure: Define what data the models need. Build the pipelines that turn raw robot episodes into versioned, training-ready datasets. Trace a stuck policy back to its data.
• Models on robots: Get models running reliably and fast enough on real hardware. Debug failures in the lab across the model, the data, and the robot.

Qualifications

• 5+ years of hands-on experience training and shipping ML models in production or in a research lab, with a B.Tech, M.Tech or PhD in Computer Science, Electrical Engineering or a related field.
• A track record of owning an ML system end to end: you’ve taken something from problem definition through data, training, evaluation, and deployment, and you can tell us what you’d do differently.
• Deep hands-on experience with PyTorch, including writing and debugging non-trivial training loops.
• Experience training at scale: multi-GPU or multi-node training (DDP, FSDP, DeepSpeed, or equivalent) and the profiling work to keep it efficient.
• Strong fundamentals in linear algebra, probability, optimization, and machine learning, and in data structures, algorithms, and systems.
• Strong software engineering habits: you write code others can build on, and hold a high bar for code quality.
• Experience post-training transformers, diffusion models, or multimodal models, especially with imitation learning or reinforcement learning.
• Experience in robotics, autonomous vehicles, or another domain where models act in the physical world and failures are expensive.
• Experience with robot simulation (MuJoCo, Isaac Sim, PyBullet) and sim-to-real transfer.
• Experience building data pipelines for large multimodal datasets (object storage, Ray or Spark, Parquet, WebDataset).
• Experience deploying models to constrained or real-time hardware (ONNX, TensorRT, quantization, latency work).
• Publications at top AI or robotics venues, or meaningful open-source contributions.
• Experience mentoring engineers or leading a small technical effort.
• Ownership. You finish things. A result that doesn’t run on the robot isn’t finished.
• Rigor. You catch the subtle bug that the dashboard says is fine.
• Range. You’re as willing to debug a data pipeline or a ROS node as a training run.
• Comfort with real hardware. Our models run on robots, and you’ll spend time in the lab.
• Speed of learning. There’s no playbook. You’ll be learning new parts of the stack every month.
• Ambition. You want to build something that matters at the frontier of AI and robotics.