About the role
About the Role
Before an autonomous vehicle navigates a busy intersection, before a robot learns to pick and place in a warehouse, before any Physical AI system is trusted in the real world, it has to prove itself in ours. Parallel Domain builds the platform that validates the next generation of autonomous systems in high-fidelity virtual environments, and the infrastructure underneath that platform is what makes simulation at scale possible.
We're hiring a Senior Site Reliability Engineer to help build and operate that infrastructure. This role sits at the core of how we run large-scale, distributed simulation workloads for autonomous-systems testing and validation. You'll work across multi-region AWS infrastructure, operate Kubernetes at scale, and contribute directly to reliability, security, and deployment systems that the rest of the engineering org depends on.
This is a hands-on role with the broad ownership typical of a startup. You'll partner closely with platform, simulation, and ML teams to keep the system running smoothly and evolving. We're growing the team—two of these roles are open—and the work is substantive: multi-region GPU scheduling, Windows workloads on Kubernetes, large-scale batch simulation, and an enterprise product direction that will require rethinking parts of how we deploy and operate.
Skills & Tags
Aplyr's read
Parallel Domain is at the forefront of synthetic data generation for AI, attracting tech-savvy professionals passionate about simulation and machine learning.
What's promising
- •Innovative focus on synthetic data for training autonomous systems.
- •Strong growth potential in the AI and simulation markets.
- •Attracts top talent with roles in engineering and machine learning.
What to watch
- •Highly competitive industry with rapid technological changes.
- •Dependence on the success of autonomous vehicle adoption.
- •Limited public information about long-term financial stability.
Why Parallel Domain
- •Pioneers in creating realistic simulation environments for AI training.
- •Specializes in synthetic data to enhance machine learning models.
- •Combines expertise in simulation and rendering with machine learning.
Aplyr’s read is generated by AI from public sources. Was it useful?
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