Founding Robotics Researcher Job San Jose area,California USA,IT/Tech

FOUNDING ROBOTICS RESEARCHER

JOB TITLE Founding Robotics Researcher

LOCATION Onsite (Bay Area, CA)

COMMITMENT Full-time / Founding Team

COMPENSATION Competitive Salary + Meaningful Equity, Pay Frequency:
Monthly HIGHLIGHT Support Immediate H1 Visa Application

ABOUT DEEPREACH

Deep Reach is building the infrastructure layer for real-world embodied AI. We focus on large-scale teleoperation data, Vision-Language-Action (VLA) training, and real deployment environments—not staged demos. We are not a simulation-only research lab; we train on real distributions, deploy in real environments, and iterate fast to close the loop between model training and physical performance.

ABOUT TALEX.AI

Talex.ai empowers global experts to shape the future of human-AI collaboration. We connect professionals with leading AI research teams to improve the communication quality, creativity, and cultural intelligence of AI systems. For this founding role, we are partnering with Deep Reach AI to find a researcher who thinks like a scientist but builds like a startup operator.

THE ROLE

As a Founding Robotics Researcher, you will own the VLA and policy learning direction. You won't just consume datasets; you will define the data strategy, ship models onto real robots, and design experiments that directly improve deployment performance. This role is for someone who wants to build a research engine inside a startup and is comfortable switching from PyTorch to hardware debugging when necessary.

KEY RESPONSIBILITIES

VLA & Policy Training: Architect and train VLA models for real-world tasks and design fine-tuning pipelines using deployment-collected data.

Data System Design: Develop teleoperation data collection frameworks and build filtering, curation, and scaling pipelines to address distribution gaps.

Hardware Integration: Deploy policies to physical robot arms and sensor stacks, tuning latency, calibration, and system reliability.

Research–Deployment Loop: Build internal benchmarks tied to actual tasks and translate model failures into data and system improvements.

Systems Debugging: Hands‑on work with robot arms, grippers, and multi‑camera systems to debug perception, policy, and control loops.

Experimental Leadership: Define the experiments that matter and develop evaluation metrics tied to physical success rates.

REQUIREMENTS

Education: MS/PhD or equivalent deep experience in Robotics or Embodied AI.

Technical Expertise: Strong background in Imitation Learning, RL, Diffusion Policies, or VLA models.

ML Tooling: Proficiency in PyTorch and modern large‑scale model training workflows.

Systems Thinking: Deep understanding of the intersection between perception, policy, and control.

Real‑World

Experience:

Proven experience with physical robots (not simulation only).

Mindset: High ownership mindset with the ability to thrive in ambiguity and fast‑paced iteration cycles.

Authorization: U.S. work authorization required (Visa transfer supported).

STRONG SIGNALS

You have successfully trained and deployed learned policies onto physical robotic systems.
You have experience debugging "messy" real‑world hardware and software failures.
You enjoy being close to hardware and have designed your own experimental frameworks.
You are driven to build and scale systems rather than just optimizing benchmarks.

WHY JOIN US

Real-World Impact: Unlike traditional labs that stop at publication, we measure success by real-world deployment performance.
Build the Data Engine: Help define the data scaling laws for embodied intelligence from the ground up.
Founding Ownership: Gain meaningful equity and direct influence over the company's research direction.
Physical AI Frontier: Work at the hardest unsolved problem in robotics—making robots work reliably in production environments.
Global AI

Collaboration:

Leverage Talex.ai’s ecosystem to integrate cultural and linguistic intelligence into physical systems.

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