Bridging cutting-edge research with practical engineering to solve diverse technical challenges across modeling, infrastructure, and product development in spatial intelligence.
Requirements
- 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.
- 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).
Responsibilities
- 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.
Other
- 3+ years of experience in applied machine learning, research engineering, 3D, or related development roles, ideally in fast-paced or startup environments.
- 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.
