Senior Machine Learning Engineer, Pose Estimation

At Parallel Domain, we believe that autonomous systems – from self-driving cars to contactless delivery drones – have the opportunity to drastically improve the quality of life for billions of people. That is why we love what we do: enabling our customers to develop their technology safely using the Parallel Domain simulated testing platform. Our software changes the way our customers test their systems, accelerating their ability to deploy safe and reliable AI. We're looking for people like you to join us in pushing the boundaries of artificial intelligence and simulation.

Parallel Domain is building the world’s most advanced simulation and digital twin platform for autonomy, robotics, and computer vision. Our Replica product creates large-scale, photorealistic digital twins of real-world environments used for testing, validation, and development of autonomous systems.

About the role:*

We are looking for a Senior Machine Learning Engineer focused on pose estimation and structure-from-motion (SfM) to improve the backbone of Replica’s geometric reconstruction pipelines. You will develop and integrate ML-based and classical approaches for robust camera pose estimation and scalable multiview geometry systems.

What you'll do:*

Build robust SfM and pose pipelines: Improve accuracy, speed, and reliability of pose estimation components.

Develop geometric vision models: Design and implement learned modules for camera calibration and pose refinement.

Integrate ML and classical methods: Combine geometric algorithms with modern ML tools for practical performance.

Collaborate on data and evaluation: Partner with ML engineers to curate datasets and define metrics for success.

What you’ll bring:*

Advanced degree: MS or PhD in ML, computer vision, robotics, or related field.

Relevant experience: Industry experience applying ML to computer vision problems, with emphasis on pose or multiview geometry.

Technical proficiency: Strong Python experience and production-level code development.

Foundational knowledge: Deep understanding of 3D geometry, linear algebra, and optimization.

What will help you stand out:*

Hands-on experience: Demonstrated experience in developing and deploying production-level machine learning models.

Parallel Compute Experience: Experience in programming CUDA kernels for 3D computer vision applications

Research background: Familiarity with academic research in 3D reconstruction, visual geometry transformer models, bundle adjustment or related fields.

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: A base pay range of $150,000 - $180,000/yr, depending 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.

If you're passionate about machine learning, 3D reconstruction, generative AI, and the future of autonomous systems, we'd love to hear from you. Apply today and help us revolutionize the world of AI!

This position is available in Vancouver, BC and Karlsruhe DE.

Why join Parallel Domain?*

We are assembling a team of creative, talented visionaries seeking to build a new technology that will change the world. You will be able to learn, build, and scale our team and technology in a collaborative, creative culture that values every team member.

To attract and inspire the right talent, we offer:

• Competitive compensation• Employer-paid supplemental medical, mental health, dental, and vision benefits.• Flexible paid vacation, sick time, winter shutdown, and 11+ holidays per year• Paid parental leave• Equipment budget to optimize your setup• Annual learning and development stipend