Senior Machine Learning Engineer, Parallel Domain
Diffusion & Reconstruction
CA$150-180k
Synthetic data for computer vision and perception
Be an early applicant
Synthetic data for computer vision and perception
21-100 employees
Be an early applicant
CA$150-180k
21-100 employees
Company mission
To speed up the transition to autonomy by leveraging synthetic data.
Role
Who you are
- 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
Desirable
- 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 the job involves
- 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
- 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
Our take
Autonomous vehicles are fast making the leap from science fiction to reality. However, there are huge safety implications that need to be addressed. Parallel Domain has developed software for training autonomous vehicles in a virtual setting, reducing the risks associated with real-world testing and giving vehicle manufacturers the ability to identify problems and put them right, before the vehicle ever hits the road.
Founded in 2017, Parallel Domain leverages synthetic data to train and test the vision and perception systems for AVs. Clients can customise training environments with parameters such as weather, lighting, and distribution of agents, allowing them to model specific situations, including rare ones that would be unlikely to occur in real-world training. This results in AIs that behave better and more safely in novel situations.
Parallel Domain has seen significant investment that will fuel operational expansion and increase business reach. With the value of synthetic data quickly becoming apparent, the company is positioned well to benefit from those wishing to train their autonomous vehicle AIs faster and more efficiently.
Steph
Company Specialist at Welcome to the Jungle
Insights
62% employee growth in 12 months
Company
Funding (last 2 of 3 rounds)
Nov 2022
$30m
SERIES B
Dec 2020
$11m
SERIES A
Total funding: $43.9m
Company benefits
- Flexibility to work from our office in the San Francisco Bay Area or your home office
- Competitive compensation
- Employer-paid supplemental medical, mental health, dental, and vision benefits
- 401(k)
- Paid vacation and sick time, winter shutdown, and 11 stat holidays each year
- Paid parental leave
- New hire equipment + accessories budget to optimize your setup
- $1,500 annual learning and development allowance
Company HQ
Financial District, San Francisco, CA
Leadership
Kevin McNamara
(CEO)They studied Computer Science at Harvard before working for almost 3 years as a Microsoft Technical Artist. They left their Procedural Content Lead at Apple in July 2017 to found Parallel Domain, where they have served as CEO since.
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