About the role:
- 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.
What you'll do:
- 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.
What you’ll bring:
- 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.
What will help you stand out:
- 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.
What we offer:
- 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.
Top Skills
What We Do
Training and testing autonomous systems in the real world is a slow, expensive and cumbersome process. Parallel Domain is the smartest way to prepare both your machines and human operators for the real world, while minimizing the time and miles spent there. Connect to the Parallel Domain API and tap into the power of synthetic data to accelerate your autonomous system development.
Parallel Domain works with perception, machine learning, data operations, and simulation teams at autonomous systems companies, from autonomous vehicles to delivery drones. Our platform generates synthetic labeled data sets, simulation worlds, and controllable sensor feeds so they can develop, train, and test their algorithms safely before putting these systems into the real word.
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