Senior Physical AI & Robotics Scientist - Lab Automation

Genentech; South San Francisco (CA); Sur place; USD 168 000 - 312 000; Plein temps

Résumé du poste

Genentech seeks a Senior Physical AI & Robotics Scientist to lead hands-on development of autonomous laboratory capabilities. You will deploy 3D perception, digital twins, and closed-loop control on advanced robotic hardware using Isaac, Omniverse, and related stacks.

Strong C++/Python and sim-to-real expertise are essential. The role requires 5+ years in Physical AI or a PhD with deployment experience and offers a high-impact, scalable platform for drug discovery at Genentech in San Francisco.

Qualifications

• 5+ years deploying Physical AI or robotics solutions or a PhD with relevant deployment experience.
• Strong background in 3D computer vision, kinematics, and motion planning.
• Proficiency in C++/Python and modern ML frameworks (PyTorch).
• Hands-on experience with robotics simulation and sim-to-real validation.

Responsabilités

• Develop 3D perception pipelines and motion planning for robotic manipulation in dynamic workcells.
• Build multi-physics simulation environments to test plans in digital twins before deployment.
• Apply sim-to-real tuning and domain randomization for robust policies.
• Fine-tune multimodal vision models and optimize edge inference on Jetson devices.

Connaissances

3D computer vision

ROS 2

MoveIt 2

cuMotion

C++/Python

TensorRT

NVIDIA Isaac Sim

Jetson edge compute

Formation

MS in Computer Science/Robotics/Engineering

PhD with 2+ years in Physical AI/Robotics

Outils

NVIDIA Isaac Sim/Lab

NVIDIA Omniverse

Newton Engine

MuJoCo

Drake, Gazebo

gRPC

SiLA 2

Description du poste

The Position

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche. Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.

The Opportunity

At Genentech (gRED) and Roche (pRED), we’re applying Physical AI to solve one of humanity’s hardest challenges: building fully autonomous, self-healing laboratories for lights‑out drug discovery.

Advances in AI, data, and computational sciences are transforming drug discovery and development. The Computational Sciences Center of Excellence (CoE) and the Data and Digital Catalyst (DDC) team are building the foundational platform for autonomous labs – turning state‑of‑the‑art AI into real‑world physical action to deliver life‑saving medicines to patients faster than ever before.

As a Senior Physical AI & Robotics Scientist, you will be the hands‑on technical lead and scientific architect bringing Physical AI to life – deploying 3D perception, multi‑physics digital twins, edge vision, and closed‑loop control onto state‑of‑the‑art robotic hardware using cutting‑edge stacks like NVIDIA Isaac, Omniverse, and Metropolis. If you are inspired by the challenge of bridging foundation models with complex real‑world hardware to give robots the spatial awareness and resilience to autonomously solve problems on the lab floor, this is your opportunity to pioneer the next generation of autonomous laboratories at global scale.

In this role, you will:

• Develop 3D computer vision perception pipelines (point clouds, depth maps, pose estimation) and dynamic motion planning algorithms (ROS 2, MoveIt 2, cuMotion) to enable reliable arm manipulation, hand‑eye calibration, grasping, and AMR navigation in dynamic workcells.
• Build multi‑physics simulation environments (NVIDIA Isaac Sim/Lab, Newton Engine) to model robot‑labware interactions, evaluate LLM‑generated execution plans inside digital twins before deployment, and train policy recovery models.
• Apply system identification, domain randomization, and sim‑to‑real tuning to ensure simulation‑trained control policies execute predictably on physical hardware.
• Fine‑tune multimodal Vision AI models (VLMs, NVIDIA Cosmos) using synthetic failure datasets (Isaac Sim) for real‑time detection of lab anomaly modes (spills, bubbles, dropped microplates, tilted lids, tip‑pick misses).
• Optimize low‑latency inference with TensorRT on NVIDIA Jetson (Thor/Orin) edge hardware, combining visual servoing and closed‑loop feedback control so systems self‑correct without halting master workflows.
• Harness OpenUSD scene graphs to represent workcell geometry and state, integrating synchronization services to link executable digital twins across heterogeneous hardware APIs (SiLA 2, gRPC, vendor SDKs).

Who You Are

Core Qualifications & Experience

• MS in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or related technical field with 5+ years of experience, or a PhD with 2+ years of experience in deploying Physical AI or robotics solutions.
• Strong interest in translating physical AI capabilities into biological workflows and drug discovery pipelines.
• Passionate builder with a proven track record of taking real‑world robotic manipulation and sim‑to‑real systems beyond demonstrations into sustained operation.
• Strong background in 3D computer vision, kinematics, motion planning (ROS 2, MoveIt 2, cuMotion), and modern machine learning frameworks (PyTorch) with strong C++/Python skills.
• Hands‑on experience with robotics simulation and robot‑learning frameworks (NVIDIA Isaac Sim/Lab, Newton, MuJoCo, Drake, Gazebo) and sim‑to‑real validation methods.
• Experience training or fine‑tuning vision/multimodal models (VLMs, Cosmos) and optimizing neural networks with TensorRT for edge hardware (Jetson Thor/Orin).
• Proficiency with visual servoing, closed‑loop controllers, and hardware API integration (gRPC, SiLA 2, vendor SDKs).

Preferred but not required

• Hands‑on experience with OpenUSD scene graphs, custom USD schemas, or NVIDIA Omniverse workflows.
• Experience working with high‑throughput lab automation hardware, manipulators, or liquid handlers (e.g., Hamilton, Tecan, Precise Automation, UR arms).
• Background in imitation learning, reinforcement learning, or robot foundation models for contact‑rich manipulation and recovery.
• A public portfolio of real‑world projects on GitHub/GitLab showcasing work in robotics, simulation, or physical AI.

Relocation benefits are NOT available for this opportunity

The expected salary range for this position based on the primary location of San Francisco is $168,100 - 312,300 of hiring range. Actual pay will be determined based on experience, qualifications, geographic location, and other job‑related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.

Benefits

#ComputationCoE

#tech4lifeComputationalScience

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