Machine Learning Engineer

Build machine learning systems for next-generation autonomous systems and robotics simulation at Zeromatter Technologies Inc in Mountain View, CA (onsite). This role focuses on turning advanced ML methods into practical models and reinforcement learning training pipelines, then integrating them into a core simulation platform used with real customer needs.

Compensation: USD 100,000 - 300,000 per year. You will work at the intersection of model development, performance optimization, and simulation integration, with opportunities to apply your expertise to realism, scenario generation, behavior modeling, and robotic training.

Responsibilities

• Develop and implement machine learning models that improve realism of simulated sensor data, including approaches for light transport approximation, material models, 3D representation, and black box sensor characterization.
• Create content generation systems to produce diverse and realistic simulation scenarios, including perception signals from geospatial data, measurements from novel hardware, and conditioning on both structured and unstructured text.
• Design and implement behavior models for simulation actors such as pedestrians, vehicles, and drones.
• Develop reinforcement learning algorithms to train robotic systems inside the simulation environment.
• Optimize ML performance for simulation use cases, focusing on low-latency inference, architectural improvements, and model compression.
• Partner with the software engineering team to integrate ML models into the core simulation platform.
• Work with customers to understand their ML-related requirements and deliver tailored solutions.

Requirements

• Master's or PhD in a relevant field (e.g., computer science, mechanical engineering, physics, math, robotics) or equivalent working experience.
• Strong programming skills in Python and experience with ML frameworks such as PyTorch or TensorFlow.
• Deep understanding of ML architectures and algorithms applicable to robotics and simulation, including computer vision, reinforcement learning, and generative models.
• Experience applying ML to real-world problems, preferably robotics, autonomous systems, or simulation, including building and maintaining data pipelines and datasets.
• Familiarity with software engineering best practices, including version control, testing, and CI/CD.

Bonus / Preferred

• Experience with systems programming languages such as C, C++, and Rust (Rust preferred).
• Experience with low-level GPU programming, including CUDA or Vulkan.
• Experience with distributed computing for large-scale ML training.
• Familiarity with ML model deployment and serving in production environments.

Technologies: Python, PyTorch, TensorFlow, C, C++, Rust, CUDA, Vulkan.