Research Engineer - Perception and Machine Learning

Meta•Redmond, WA

10h•$88 - $257,000

About The Position

We are seeking Research Engineers to serve as technical pillars within the Surreal Team. In this role, you will move beyond algorithm implementation to architecting the systems that power always-on contextual AI. This position sits at the intersection of Computer Vision, Embodied AI, and Multimodal LLMs. You will drive the engineering efforts to bring research breakthroughs from high-compute clusters to power-constrained, egocentric devices like smart glasses and robotic platforms.

Requirements

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 TensorFlow, 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

Nice To Haves

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

Responsibilities

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