Machine Learning Engineer
Job Description
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.
