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Onsite or remote • Texas+1💼 Full-time🗓 2026-06-25

Core

Building AI simulation tools for autonomous vehicle testing, including virtual test drives, synthetic data generation, and reinforcement learning pipelines.

Role type

Machine Learning Engineer (AI Simulation)

Builds

AI simulation environment for autonomous vehicle testing

Domain

Autonomous driving / Robotics / Computer Vision

Deliverable

production ML models

Required skills

Python, PyTorch, deep learning fundamentals, distributed training, CUDA, computer vision (segmentation/detection), Linux/bash scripting

Preferred skills

TensorFlow, MXNet, neural rendering, neural animation, scene/object reconstruction, academic literature implementation

Technologies

PyTorch, TensorFlow, MXNet, CUDA, NumPy, SciPy, Matplotlib, Scikit-learn, Jupyter, Bash

Responsibilities

Build tools for virtual test drives, test code changes for regressive behavior, generate synthetic datasets and RL pipelines, develop neural rendering/animation/reconstruction features

Seniority

Mid-Senior, hands-on IC

Rewrite
## About the Role ML is of critical importance to Mozee's mission. It is safer, makes our rides more enjoyable, and will ultimately deliver on the promise of self-driving robo taxis. As a member of Mozee's AI Simulation team, you will be in a unique position to accelerate the pace at which our AI improves over time. The main ways in which the simulation team realizes this include: - Building tools that enable our software developers to perform virtual test drives instead of real ones. - Testing all code changes and software releases for regressive behavior. - Generating synthetic data sets and reinforcement learning pipelines for neural network training. As an Machine Learning Engineer at mozze, you will contribute to the development of mozze's simulation by enabling and accelerating the creation of photo realistic 3D scenes through neural rendering, neural animation, scene/object reconstruction. More broadly we are looking for experts in these fields: - Neural rendering - Neural animation - Object reconstruction - Environment reconstruction - Scenario reconstruction ## Requirements - Expert level Python Skills - The team operates in a production setting. An ideal candidate has strong software engineering practices and is very comfortable with Python programming, debugging/profiling, and version control. - We train neural networks on a cluster in large-scale distributed settings. An ideal candidate is very comfortable in cluster environments and understands the related computer systems concepts (CPU/GPU interactions/transfers, latency/throughput bottlenecks during training of neural networks, CUDA, pipelining/multiprocessing, etc). - We are at the cutting edge of deep learning applications. The ideal candidate has a strong understanding of the under the hood fundamentals of deep learning (layer details, backpropagation, etc). Additional requirements include the ability to read and implement related academic literature and experience in applying state of the art deep learning models to computer vision (e.g. segmentation, detection) or a closely related area (speech, NLP). - Experience with PyTorch, or at least another major deep learning framework such as TensorFlow, MXNet. - Some experience with data science tools including Python scripting, numpy, scipy, matplotlib, scikit-learn, jupyter notebooks, bash scripting, Linux environment. ## About Mozee Mozee is on a mission to develop the first fully autonomous vehicle fleet and the ecosystem needed to bring this technology to market. As a company at the intersection of robotics, machine learning, and design, we aim to provide innovative mobility-as-a-service in urban environments. We are seeking top talent who are passionate about what we do and want to be part of a dynamic and highly-focused team.
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