Machine Learning Engineer

As a Machine Learning Engineer at Zeromatter, you'll be at the forefront of creating the neural backbone that powers next-generation autonomous systems. You'll develop cutting-edge ML models that transform simulation fidelity, behavior prediction, and robotic learning capabilities. This role offers the rare opportunity to apply state-of-the-art machine learning techniques to solve real-world robotics challenges while working with a team that's redefining what's possible in simulation technology. RESPONSIBILITIES - Develop and implement machine learning models to improve the realism of simulated sensor data, including for example approximation of light transport, material models, 3D representation and black box sensor characterization. - Develop content generation systems for creating diverse and realistic simulation scenarios, including for example, perception signals from geospatial data, measurements from novel hardware, and both structured and unstructured text conditioning. - Create behavior models for various actors in our simulations (e.g., pedestrians, vehicles, drones). - Design and implement reinforcement learning algorithms for training robotic systems within our simulation environment. - Optimize ML model performance for simulation applications, including low latency inference, architectural improvements and model compression. - Collaborate with the software engineering team to integrate ML models into our core simulation platform. - Work with customers to understand their ML-related needs and develop tailored solutions. QUALIFICATIONS - Master's or PhD in a relevant field (e.g. computer science, mechanical engineering, physics, math, robotics, etc.) or equivalent working experience. - Strong programming skills in Python and experience with ML frameworks such as PyTorch or TensorFlow. - Deep understanding of machine learning architecture and algorithms, particularly those applicable to robotics and simulation (e.g., computer vision, reinforcement learning, generative models). - Experience applying ML techniques to real-world problems, preferably in 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: Experience with systems programming languages such as C, C++ and Rust (with Rust preferred) - Bonus: Experience with low-level GPU programming, such as CUDA or Vulkan. - Bonus: Experience with distributed computing for large-scale ML training. - Bonus: Familiarity with ML model deployment and serving in production environments.