Foundation Models Engineer for Robotics Research

Genesis; Greater London; Hybrid; GBP 120,000 - 160,000; Full time

Job summary

Genesis is seeking a senior machine learning researcher/engineer to build end-to-end foundation models for robotics control in a collaborative, data-driven environment. You will drive data curation, model architectures, and rigorous evaluation plans alongside simulation and real-world robotics teams.

We value publications or impactful projects, production-level Python expertise, and a relentless focus on data quality and reproducibility.

Qualifications

• 5+ years of experience in ML research or engineering.
• Publication track record or impactful projects demonstrates expertise.
• Production-level Python skills and solid software engineering practices.
• Strong ownership, initiative, and ability to work independently.
• Commitment to data quality, rigorous evaluation, and meticulous detail.

Responsibilities

• Build machine learning models for robotics control end-to-end, including data curation, evaluation, architecture, training/inference stacks.
• Collaborate with simulation and real-world robotics teams to curate high-quality, diverse datasets.
• Curate large-scale Internet datasets for embodied perception and video generation.
• Design generative simulation techniques to expand data scale and diversity, including 3D objects/environments.

Skills

Foundation model research

Full-stack engineering

Python programming

Independent problem solving

Data quality & evaluation

Publications / impactful projects

Vision-Language-Action models (bonus)

Job description

Genesis is seeking a senior machine learning researcher/engineer to build end-to-end foundation models for robotics control in a collaborative, data-driven environment. You will drive data curation, model architectures, and rigorous evaluation plans alongside simulation and real-world robotics teams.

We value publications or impactful projects, production-level Python expertise, and a relentless focus on data quality and reproducibility.