Product Engineer
Parallel Domain
Product Engineer
Parallel Domain
Synthetic data platform for autonomous systems
Salary Range: 100000 to 200000 (Currency: USD) (Pay period: per-year-salary)
We are hiring a Product Engineer to work directly with our Chief Product Officer in building product prototypes and building/managing product integrations for our suite of simulation and neural reconstruction products. This role will be an exciting blend of hands-on engineering, product management, customer interactions, and strategic product roadmapping.
This is a highly technical role and requires software engineering experience. Our users are AI developers and our product heavily leverages cutting edge simulation technologies. This role requires expertise in AI development, simulation technologies, product management, and programming. By spending significant time with our customers, including automotive, drone, and robotics companies, you will become an expert in how our customers need to use simulation to accelerate their development of autonomous systems and then put this expertise at work to help Parallel Domain build winning products.
Synthetic data platform for autonomous systems
Parallel Domain provides synthetic data for autonomous systems. It creates labeled synthetic datasets, simulation environments, and controllable sensor feeds to help perception, machine learning, data operations, and simulation teams train and test algorithms for autonomous vehicles and delivery drones. The product operates through a connect-to-API platform, offering data through subscriptions and usage-based pricing so teams can generate data, run simulations, and feed sensors without real-world testing. Compared with competitors, it specializes in end-to-end synthetic data generation for perception and ML pipelines with an API-driven workflow focusing on autonomous systems rather than general data generation. The goal is to speed up development and testing of autonomous technologies by providing scalable, safe, and repeatable data and simulation environments that reduce the time and resources spent on real-world testing.
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