Senior Software Engineer – Simulation & ML Platform

Intuitive; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Intuitive seeks a Senior Software Engineer – Simulation & ML Platform to build scalable software platforms for simulation-based data generation and machine-learning development in robotic motion AI.

You will work with Simulation Data Scientists and ML teams to translate research requirements into reliable, extensible systems, pipelines, and tools across headless, local, cluster, and cloud execution environments.

Responsibilities

• Build and maintain software infrastructure for robotic simulations and ML.
• Architect scalable pipelines for synthetic data generation and experimentation.
• Develop automated integration and validation pipelines for simulation platforms.
• Create reusable abstractions for robot configurations and sensors.
• Build tools to allow data scientists to define experiments without low-level changes.
• Develop GPU-enabled, containerized environments for simulation and model inference.
• Collaborate with controls, embedded, systems, and robotics engineers.
• Establish standards for software development, testing, and release management.

Job description

Job Description

Primary Function of Position

As a Senior Software Engineer – Simulation & ML Platform, you will build software platforms supporting simulation-based data generation and machine-learning development for robotic motion AI. You will work closely with Simulation Data Scientists and ML teams responsible for simulation strategy, synthetic-data design, action-model development, sim-to-real evaluation, and machine-learning experimentation. The Senior Software Engineer will translate those research and data-science requirements into reliable, scalable, and extendible software systems.

Responsibilities

• Build and maintain software infrastructure for robotic simulations & ML using NVIDIA Isaac Sim, Isaac Lab, MuJoCo or custom simulation environments.
• Architect scalable pipelines for generating synthetic and procedurally varied robotic interaction data including development of APIs, configuration systems, command-line tools, and SDKs for defining and launching simulation workloads.
• Build automated integration and validation pipelines for evolving simulation platforms, physics engines, and the NVIDIA AI ecosystem, enabling rapid adoption of new releases while maintaining compatibility, reproducibility, and stability across the robotics simulation software stack.
• Develop AI-assisted engineering workflows for automated code refactoring, maintainability analysis, architectural consistency, and CI/CD optimization, enabling readable, modular, and sustainable codebases while improving engineering productivity across large-scale software projects.
• Create reusable abstractions for robot configurations, tasks, sensors, scene assets, domain randomization, failure injection, data capture, episode termination, and evaluation.
• Build tools that allow data scientists to define experiments without modifying low-level platform code. Support headless, interactive, local, cluster, and cloud-based execution.
• Develop secure, GPU-enabled containerized environments for simulation, data processing, model inference, and experimentation. Develop common interfaces for robot state, coordinate frames, actions, trajectories, control commands, sensor observations, timing, and safety status.
• Build tools for importing physical robot logs into analysis and simulation environments. Enable software-in-the-loop, hardware-in-the-loop, and controlled trajectory replay where appropriate.
• Collaborate with controls, embedded, systems, and robotics engineers to manage differences between simulated and physical interfaces.
• Establish standards for software development, testing, documentation, code review, release management, and operational ownership.