Research Engineer – Perception and Machine Learning

Algorithm & System Implementation:
Translate cutting-edge research in Vision-Language Models (VLMs), Reinforcement Learning, and Multimodal LLMs into performant, real-world applications on smart glasses and robotic platforms

End-to-End Ownership:
Own the full lifecycle of feature development, from initial prototyping and data collection to deployment and system integration

Experimental Rigor:
Design and lead large-scale ablation studies

develop robust benchmarking suites to evaluate and iterate on next-gen contextual AI

Cross-Functional Influence:
Partner with Hardware Engineers to influence sensor/silicon design and collaborate with Researchers and Product Managers to define the future of human-AI interaction

Architecture &

Roadmap:

When hired at a staff level, lead the design and execution of engineering roadmaps, making critical architectural decisions to ensure low-latency, high-accuracy inference on power-constrained “always-on” edge devices

Technical Leadership & Mentorship:
When hired at a staff level, provide technical guidance and mentorship to peers and engineers, setting the bar for engineering and software maintainability

Bachelor’s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience

Bachelor’s degree in Computer Science, Robotics, or a related technical field (or equivalent practical experience)

Experience:

3-8 years of professional experience in AI research engineering, software development, or a related field. (Candidate leveling will be determined based on technical depth, scope of previous impact, and leadership experience)

Core Technical

Skills:

In-depth experience programming in C and Python, with a focus on developing high-performance, maintainable codebases

Framework Mastery:
Extensive experience with PyTorch or Tensor Flow, including model optimization (e.g., quantization, distillation, or custom kernel development)

Deployment

Experience:

Proven history of deploying machine learning models into production environments or integrated hardware-software systems

Advanced Degree:
Ph.D. or M.S. in Computer Science, Software Engineering, or Robotics

Domain Expertise:
Specialized experience in one or more:
Egocentric Perception, Vision-Language-Action (VLA) models, SLAM, or Sim-to-Real transfer

Large-Scale Data:
Experience architecting data pipelines for high-dimensionality, multi-modal datasets

Publication Record:
Contributions to the research community through publications at market leading venues (CVPR, ICCV, NeurIPS, ICRA, RSS) or significant patent filings

Communication:
Ability to communicate complex technical trade-offs to both technical peers and non-technical stakeholders