Senior Machine Learning Engineer, Diffusion Reconstruction
Actively Hiring
Full-time Posted 7 months ago
Role overview
- check_circle + We are seeking a Senior Machine Learning Engineer to advance the state of learned reconstruction for Replica. In this role, you will drive development of feed-forward and diffusion-based models for 3D and spatiotemporal reconstruction and work closely with cross-functional teams to integrate these models into production systems.
Responsibilities
- check_circle + Develop innovative ML models: Design and implement video-to-video diffusion models and efficient 4D feed-forward reconstruction methods. Improve scalability and performance: Optimize models and pipelines to support large-scale Replica environments. Productionize research: Ship robust, documented ML components integrated with Replica tooling. Collaborate cross-functionally: Engage with simulation, rendering, and infrastructure engineers to deliver end-to-end solutions.
- check_circle Improve scalability and performance: Optimize models and pipelines to support large-scale Replica environments.
- check_circle Productionize research: Ship robust, documented ML components integrated with Replica tooling.
- check_circle Collaborate cross-functionally: Engage with simulation, rendering, and infrastructure engineers to deliver end-to-end solutions.
- check_circle + Advanced degree: MS or PhD in ML, computer vision, robotics, or related field. Deep ML expertise: Experience with deep learning frameworks (e.g., PyTorch) and large model development. Strong engineering skills: Experience taking ML prototypes into production quality code. Experience with 3D vision or reconstruction: Demonstrated knowledge of modern 3D representation learning. Generative model background: Experience with diffusion models or neural rendering.
- check_circle Deep ML expertise: Experience with deep learning frameworks (e.g., PyTorch) and large model development.
- check_circle Strong engineering skills: Experience taking ML prototypes into production quality code.
- check_circle Experience with 3D vision or reconstruction: Demonstrated knowledge of modern 3D representation learning.
- check_circle Generative model background: Experience with diffusion models or neural rendering.
- check_circle + Hands-on experience: Demonstrated experience in developing and deploying production-level machine learning models. Research background: Familiarity with academic research in video diffusion models, LoRA fine-tuning, or feed-forward reconstruction. Industry knowledge: Understanding of the autonomous systems landscape and the potential applications of machine learning in this domain. Publication record: Publications in top-tier conferences or journals related to machine learning.
- check_circle Research background: Familiarity with academic research in video diffusion models, LoRA fine-tuning, or feed-forward reconstruction.
- check_circle Industry knowledge: Understanding of the autonomous systems landscape and the potential applications of machine learning in this domain.
- check_circle Publication record: Publications in top-tier conferences or journals related to machine learning.
Benefits
- check_circle + Competitive compensation: Dependent on your skills, qualifications, experience, and location. Impactful work: The chance to contribute to the advancement of autonomous systems and AI. Collaborative culture: A dynamic and supportive work environment where your ideas are valued. Professional growth: Opportunities to learn and develop your skills in a cutting-edge field.
- check_circle Impactful work: The chance to contribute to the advancement of autonomous systems and AI.
- check_circle Collaborative culture: A dynamic and supportive work environment where your ideas are valued.
- check_circle Professional growth: Opportunities to learn and develop your skills in a cutting-edge field.
Tags & Focus Areas
Fulltime Machine Learning Computer Vision Robotics Ai