# Machine Learning Engineer II - Learned Planning (Reinforcement Learning) at Algolux

- Company: Algolux
- What the company does: Algolux is an award-winning AI software company delivering the industry’s most robust and scalable perception for all conditions, addressing both existing cameras and new designs through cloud-based tools and embedded software.

The company was founded on... Backed by Real Ventures.
- Company website: https://algolux.com
- Type: Startups (AI role)
- Level: Mid level
- Location: Remote - US, Ann Arbor, MI
- Work setup: Remote
- Posted: 2026-09-29
- Apply by: 2026-11-13
- Apply: https://job-boards.greenhouse.io/torcrobotics/jobs/8852888002
- Page: https://www.1752.vc/careers/jobs/algolux-machine-learning-engineer-ii-learned-planning-reinforcement-learning/

## About the role

As a Machine Learning Engineer II – Learned Behaviors, you will help develop and deploy behavior models that power decision-making for autonomous trucks. Working closely with teams across perception, prediction, planning, and safety, you will contribute to learned behavior modules that enable safe, efficient, and human-like driving in real-world freight operations.

## What they're looking for

- Bachelor’s degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with 4+ years of industry experience, or a Master’s degree with 2+ years of experience
- Experience applying machine learning techniques such as imitation learning, reinforcement learning, or sequence modeling to robotics, autonomous systems, or complex control environments
- Strong programming skills in Python and PyTorch, with experience writing production-quality ML code
- Experience training and evaluating machine learning models using large datasets and scalable compute environments
- Understanding of ML architectures used in autonomy systems, such as transformers, graph neural networks, or sequence models
- Experience debugging model behavior, analyzing performance metrics, and iterating on training pipelines

Tags: Autonomy