Machine Learning Engineer

Zeromatter Technologies Inc; Mountain View (CA); On-site; USD 100,000 - 300,000; Full time

Job summary

Zeromatter Technologies Inc is seeking a Machine Learning Engineer to develop models and systems for autonomous robotics simulation. The role focuses on turning simulation into a realistic, scalable environment through model development, reinforcement learning, and close collaboration with software engineers and customers.

You will build ML models to improve simulated sensor realism, create diverse scenario content, design behavior models for pedestrians, vehicles and drones, and integrate ML

Qualifications

• Master's or PhD in CS/engineering or equivalent experience.
• Proficient in Python with PyTorch or TensorFlow.
• Experience in robotics or simulation ML.
• Familiarity with CI/CD and software practices.

Responsibilities

• Develop and implement ML models for realistic sensor data.
• Build content generation systems for diverse simulation scenarios.
• Create behavior models for pedestrians, vehicles and drones.
• Design and implement reinforcement learning for robotics in simulation.
• Optimize ML performance with low-latency inference and model compression.
• Partner with software engineers to integrate ML into the simulation platform.
• Work with customers to tailor ML solutions.

Skills

Python

PyTorch

TensorFlow

Reinforcement learning

Robotics

Education

Master's or PhD in a relevant field

Tools

C

C++

Rust

CUDA

Vulkan

Job description

At Zeromatter Technologies Inc, the Machine Learning Engineer will help develop machine learning models and systems for next-generation autonomous systems and robotics simulation. The work focuses on turning simulation into a more realistic, scalable environment through model development, reinforcement learning, and close collaboration with software engineering and customers.

What you'll do

• Develop and implement machine learning models to improve the realism of simulated sensor data, including approximations related to light transport, material modeling, 3D representation, and black box sensor characterization.
• Build content generation systems that create diverse and realistic simulation scenarios, such as perception signals derived from geospatial data, measurements from novel hardware, and conditioning using both structured and unstructured text.
• Create behavior models for actors in simulation environments, including pedestrians, vehicles, and drones.
• Design and implement reinforcement learning algorithms to train robotic systems within the simulation environment.
• Optimize ML model performance for simulation use cases, including low-latency inference, architecture 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 needs and develop tailored solutions.

Qualifications

• Master's or PhD in a relevant field (for example: 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 machine learning architectures and algorithms, especially those applicable to robotics and simulation (including computer vision, reinforcement learning, and generative models).
• Experience applying ML 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.

Tech stack

• Python, PyTorch, TensorFlow, C, C++, Rust, CUDA, Vulkan

Location and compensation

• Location: Mountain View, CA (onsite)
• Salary range: USD 100,000 - 300,000 per year

Additional considerations

• Experience with systems programming languages such as C, C++, and Rust (with Rust preferred).
• 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.