Staff AI Research Engineer

Standard Bots•New York, NY

21h•$250,000 - $300,000

About The Position

We're seeking a Staff AI Research Engineer to develop and optimize our AI models and training systems. This is an exciting opportunity to apply your deep ML expertise to cutting-edge AI/robotics applications. You'll work closely with our engineering team to design, implement, and iterate on large-scale AI models while building efficient systems for rapid experimentation and deployment. We have a small team of AI engineers, so we’re looking for someone who is excited to work at a startup, and work across the stack to do what is needed to get a model that achieves a customer problem. We're looking for an experienced engineer ready to make a tangible impact in the AI robotics revolution. This role requires a proven background working on ML planning within the autonomous vehicle space with experience using the latest techniques in diffusion and autoregressive models in a professional setting. If you're excited about building the latest advancements in AI and working in the robotics space, we’d love to hear from you.

Requirements

7+ years of AI modeling experience, specifically within the self-driving car industry (or PhD with 3+ years of AI modeling experience in the self-driving car industry)
Proven track record developing and deploying large-scale ML models
Experience using the latest techniques in diffusion and autoregressive models in a professional setting
Familiar with training inference and infra pipelines that go from camera input to trajectory output for self driving or robotics
Experience with RL (reinforcement learning)
Strong understanding of modern ML architectures and training techniques
Experience with model debugging, optimization, and performance tuning
Background in implementing ML research papers and adapting academic work
Experience with PyTorch required
Python
NodeJS/Typescript
Docker

Responsibilities

Design and implement state of the art ML models and training pipelines
Apply novel machine learning techniques to wide range of robotics applications
Develop efficient data and training strategies
Implement model evaluation frameworks and metrics tracking
Lead model development and iteration with focus on: Rapid experimentation and prototyping of new model architectures, Performance optimization and model debugging, Transfer learning and fine-tuning strategies
Build robust evaluation and debugging systems to: Analyze model behavior and failure modes, Implement interpretability tools and visualization frameworks, Track and improve model metrics
Collaborate with engineering team to optimize training infrastructure and deployment