Job Title: Digital R&D Physical AI Engineering Expert
Location: Hyderabad, India
About The Job
At Sanofi, we are moving beyond traditional Data AI and into the realm of Physical AI. Our team in Hyderabad is bridging the gap between digital intelligence and the physical reality of chemical manufacturing. We don't just predict outcomes; we optimize the physical processes that create life-saving Small Molecule medicines.
We are looking for a visionary engineer to build the Digital Brain for our labs. You will use high-fidelity simulation and active learning to reduce uncertainty in chemical sampling, accelerate the Pharma R&D cycle, and design autonomous, self-optimizing chemical processes.
Key Responsibilities
- Physical AI & Process Optimization: Design AI systems that optimize chemical reaction yields, purity, and safety by modeling the complex interplay of thermodynamics and kinetics.
- High-Fidelity Simulation: Leverage the NVIDIA Isaac Sim / Isaac Lab (Omniverse) ecosystem not just for robotics, but as a GPU-accelerated physics engine to simulate lab environments and fluidic processes.
- Active Learning for Sampling: Develop algorithms (Bayesian Optimization/Active Learning) that intelligently decide the next best experiment, reducing the number of physical samples required to master a chemical space.
- Sim-to-Real Pipelines: Create Digital Twins that synchronize with real-world lab sensors, ensuring our simulations are grounded in the physical reality of the Small Molecule R&D cycle.
- Robotic Integration: Architect the control logic for lab automation hardware, enabling closed-loop discovery where AI designs, executes, and learns from experiments autonomously.
About You
Education & Experience:
- Lead Level: 5+ years of experience in AI/ML applied to physical systems, robotics, or computational chemistry.
- Staff Level: 3-5 years of experience in high-performance AI engineering or simulation-driven R&D.
- Academic Background: Master's in Chemical Engineering, Physics, Robotics, or Computer Science with a focus on Physical Systems.
Technical Requirements
- Simulation Mastery: Hands-on experience with NVIDIA Isaac Sim / Isaac Lab or similar high-fidelity physics engines (e.g., MuJoCo, PyBullet).
- The Pharma Context: Understanding of the Small Molecule R&D lifecyclefrom lead optimization to process chemistry and scale-up.
- Coding & Architecture: Expert-level Python (PyTorch, JAX) and familiarity with C++ for performance-critical simulation modules.
- Mathematical Depth: Strong grasp of uncertainty quantification and optimization strategies for noisy, real-world experimental data.
- Bonus Points (The Unicorn Skills):
- Physics-Informed Neural Networks (PINNs): Experience using PINNs or surrogate modeling to incorporate physical laws (ODE/PDEs) directly into ML architectures.
- NVIDIA Omniverse Ecosystem: Experience building custom extensions or workflows within the Omniverse stack.
Hardware Interface: Familiarity with ROS2 or low-level integration with lab automation hardware (e.g., liquid handlers, reactor controllers).
Why Sanofi Hyderabad
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