Mission

Founding ML & Foundation Model Engineer

Greater Delhi Area (Hybrid) Full TimeFounding Engineer

Xolver AI is at the forefront of innovation, driving advancements at the intersection of AI, robotics, computer vision, and generative AI. Our mission is to design cutting-edge solutions that transform industries and make a meaningful impact on society. By leveraging state-of-the-art technologies, we develop groundbreaking products to redefine automation and intelligent systems, unlocking new possibilities for the future of Physical Intelligence.

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Why this role matters

We are looking for researchers who love the "real world" and are obsessed with making models robust enough for messy, unpredictable environments.
Xolver / Infrastructure for Autonomy
Role Context

What this role actually is.

This is a founding engineering role for a specialist who will help build the learning systems behind Xolver's physical intelligence platform. You won’t just be training models in a vacuum; you will be responsible for helping machines perceive, reason, and act in unstructured environments. As a Founding ML Engineer, you will work across model development, evaluation, data quality, and deployment-readiness workflows. Detailed model, data, and integration approaches are shared through qualified internal channels.
Scope of Work

The Actual Work.

01

Model Development & Training

Design and train machine-learning systems that help robots perform useful tasks under real-world variation.

02

Data Engine Ownership

Build reliable data workflows for multimodal robotics learning, evaluation, and review.

03

Sim-to-Real Evaluation

Develop rigorous evaluation frameworks to stress-test model behavior before supervised hardware work is considered.

04

Inference Optimization

Work closely with the systems team to make model behavior practical near physical equipment without compromising safety.

05

Research Translation

Stay at the bleeding edge of robotics research, rapidly implementing and adapting new breakthroughs in VLA and generative physical AI for our platform.

Who tends to fit

You are a researcher who loves the "real world." You are frustrated by models that only work in papers and are obsessed with making them robust enough for messy, unpredictable environments. You have a "builder" mindset and aren't afraid to dive into raw data to understand why a model is failing a specific physical task.

What we expect

  • Core ML: Deep expertise in PyTorch or JAX, with a track record of training and deploying large-scale neural networks.
  • Robotics AI: Strong foundation in Imitation Learning, Reinforcement Learning, and Multi-modal perception.
  • Ecosystem Knowledge: Hands-on experience with robotics learning tooling and public robotics datasets.
  • Vision & Spatial Reasoning: Deep understanding of 3D computer vision, spatial transformers, and temporal modeling.
  • Engineering Rigor: Proficiency in Python and experience with distributed training (DeepSpeed, FSDP) and ML Ops.
  • Preferred: A history of contributions to open-source AI or robotics research.
  • Preferred: Experience with modern robot policy learning approaches.
  • Preferred: Knowledge of Sim-to-Real transfer techniques and domain randomization.
Application

Ready to architect the
future of autonomy?

Share your CV at hello@xolver.ai.

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