AI Engineer, Mytra
Reinforcement Learning
$180-220k
Office located in South San Francisco
Automated material flow & storage systems
Be an early applicant
Automated material flow & storage systems
21-100 employees
Be an early applicant
$180-220k
Office located in South San Francisco
21-100 employees
Company mission
To automate the most common industrial tasks - moving and storing materials - with flexible, scalable and intuitive robotics and AI.
Role
Who you are
- You are a reinforcement learning and system optimization enthusiast, theoretical yet pragmatic, curious yet focused, always learning yet humble, and you are motivated to see your work deployed at scale, solving real world problems
- You’re very comfortable writing Python and have experience with PyTorch, Ray and training deep neural networks
- You have an owner's mindset. You are a doer
- You are excited about working on new projects, growing together, and shaping the future of the company
- You’ve played with different types of sensors and are familiar with robot software frameworks
- Uncertainty isn’t scary, and you’re willing to realize mistakes and pivot
- You’re familiar with simulators such as Isaac Sim, Gymnasium, MuJoCo, Unity, or other video game environments
- You can convey complex ideas and technical concepts to a diverse audience
- Openness, inquisitiveness, and constant ambition are important values
- You enjoy pair programming and taking time to help others
What the job involves
- Mytra's AI team seeks pioneering engineers to architect the decision-making core of our distributed robotic intelligence system
- As a Reinforcement Learning AI Engineer, you'll push the boundaries of multi-agent coordination by developing novel approaches for real-time adaptive control, hierarchical policy learning, and decision optimization
- You'll tackle fascinating challenges like dynamic resource allocation in uncertain environments, multi-robot task scheduling, collective behavior emergence, and real-time policy adaptation with safety-critical constraints
- Working at the intersection of reinforcement learning and robotics, you'll implement state-of-the-art algorithms for multi-agent coordination, efficient exploration strategies, and robust policy optimization while collaborating with a multidisciplinary team to bridge the gap between theoretical advances and production-grade systems that reliably operate in real-world industrial environments
- Example Projects:
- Design and implement a multi-agent RL deep learning model for coordinating robot tasks
- Implement a new algorithm or architecture from a newly published paper
- Develop a simulation environment that accurately models real-world warehouse dynamics, including variable payload characteristics, battery management, and per-customer success metrics, to enable faster training and validation of RL policies
- Implement and adapt state-of-the-art RL algorithms (like Proximal Policy Optimization or Soft Actor-Critic) to handle the partial observability and high-dimensional state spaces
- Devise a hierarchical RL system that decomposes complex tasks into manageable sub-tasks, enabling more efficient learning and better generalization across different topologies
- Build a reward shaping mechanism that balances multiple competing objectives
- Implement a transfer learning approach to leverage knowledge from simulation training to real-world deployment
Our take
Since the pandemic disrupted global supply chains, labor shortages and inefficiencies in warehouse operations have become a major issue. Many warehouses are still largely manual, with only a small percentage using advanced automation, leaving space for improvement in efficiency and productivity.
To tackle this, Mytra AI is developing an advanced automation system for warehouses. Founded by former Tesla engineers, the company is creating modular and reconfigurable robotic solutions that can handle heavy payloads and adapt to various warehouse tasks. With the aim of enhancing the flexibility and capability of automated systems, making warehouses better suited to current industry needs.
Backed by substantial funding, Mytra is ready to roll out its solutions with major clients like Albertsons and other Fortune 50 companies. With its innovative approach and strong backing, Myra is well-positioned for an exciting future in the warehouse automation sector.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Few candidates hear
back within 2 weeks
Company
Funding (2 rounds)
Jul 2024
$50m
SERIES B
Jan 2023
$28m
SERIES A
Total funding: $78m
Company HQ
The East Side, South San Francisco, CA
Articles
Leadership
Chris Walti
(CEO)Previously worked at Tesla for over 7 years. While there they held a number of roles, including Senior Manager/Lead for Tesla Bot. Prior to that, they co-founded Tastebud Technologies, which was acquired by Raise.com in 2014.
Ahmad B.
(CTO)Previously a Director at Rivian, a Senior Engineering Manager at Tesla, and a Chief Product Architect at PARC.
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