Research Engineer – Machine Learning & Systems

We are looking for a versatile Research Engineer with a strong background in machine learning or 3D, software development, and systems design. This role is ideal for someone excited about bridging cutting-edge research with practical engineering, and who thrives on solving diverse technical challenges across modeling, infrastructure, and product development.

You’ll work closely with researchers, engineers, and product teams to design, build, and deploy advanced models and systems in real-world applications on the cutting edge of spatial intelligence. The role emphasizes hands-on contributions across the entire lifecycle—exploration, prototyping, experimentation, and production deployment. You will help ensure our technology continues to push boundaries in AI and remains robust, scalable, and impactful.

• Research, design, and implement machine learning models and systems across multiple domains (vision, generative AI, simulation, rendering).

• Develop efficient software pipelines and infrastructure for data curation, training, evaluation, and deployment.

• Translate research insights into production-ready solutions, collaborating with product teams to meet real-world requirements.

• Contribute hands-on to all aspects of the engineering cycle—prototyping, optimization, integration, and scaling.

• Stay current with the latest research trends and explore opportunities to apply new methods to product and system development.

• Share technical expertise with colleagues, mentor junior team members, and promote engineering best practices.
  

• 3+ years of experience in applied machine learning, research engineering, 3D, or related development roles, ideally in fast-paced or startup environments.

• Proficiency with ML frameworks such as PyTorch or TensorFlow, and solid understanding of generative modeling, deep learning, or reinforcement learning.

• Demonstrated ability to work across different problem domains (e.g., computer vision, simulation, graphics, or systems).

• Proven track record of delivering robust prototypes and/or production systems.

• Strong coding skills in Python (additional experience with C++ or CUDA a plus) and comfort with GPU-accelerated computing.

• Strong problem-solving skills with the ability to adapt quickly, manage ambiguity, and operate in a dynamic environment.

• Excellent communication skills, with the ability to work effectively across research and product-focused teams.

• Contributions to open-source projects in ML, systems, or developer tools.

• Experience with large-scale training or distributed systems (multi-GPU or multi-node).

• Familiarity with deployment and integration of ML models in production settings.

• Experience writing efficient low-level code (e.g., CUDA kernels, performance optimization).