Machine Learning Engineer, Robotic Foundation Models

MetAntz; Bengaluru; On-site; INR 4,000,000 - 8,000,000; Full time

Job summary

MetAntz in Bengaluru is seeking a machine learning engineer to work across data, models, training, simulation and deployment on real robots. You’ll drive research‑grade work and ship it to hardware within the quarter, ensuring every result runs on the robot.

You’ll own end‑to‑end ML systems from data loading and model training to evaluation, deployment, and continual improvement in production and lab environments. Collaboration with robotics and software teams is essential.

Qualifications

• 5+ years hands-on experience training and shipping ML models in production or research labs.
• End-to-end ML system ownership from data through deployment.
• Strong PyTorch experience and scalable training knowledge.
• Experience with multi-GPU or multi-node training and profiling.

Responsibilities

• Train and post-train robotic foundation models using imitation learning and RL.
• Build scalable multi-node GPU training and data loaders.
• Develop simulation environments and transfer policies to real robots.
• Design rewards and verifiers to check task success.
• Create evaluation harnesses and metrics for model progress.
• Define data requirements and pipelines for versioned training data.

Skills

PyTorch

Multi-GPU training

RL/Imitation learning

Linear algebra

Software engineering

Education

B.Tech / M.Tech / PhD in CS/EE or related

Tools

MuJoCo

Isaac Sim

PyBullet

Ray

TensorRT

Job description

About The Role

A machine learning engineer works across the full loop that makes a robot better: data, models, training, simulation, evaluation, and deployment on real hardware. You’ll do research-grade work and ship it onto robots in the same quarter. A result that doesn’t run on the robot isn’t finished.

About The Role

A machine learning engineer works across the full loop that makes a robot better: data, models, training, simulation, evaluation, and deployment on real hardware. You’ll do research-grade work and ship it onto robots in the same quarter. A result that doesn’t run on the robot isn’t finished.

What You’ll Work On

• 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.

What We’re Looking For

• 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.

Preferred

• 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.

What We Value

• 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.