Senior Software Engineer (Simulation Orchestration)
Posted Updated
- As a senior software engineer on the Simulation Orchestration team, you will own the compute platform that Applied Intuition’s autonomy products run on. Our customers, and our own internal self-driving programs, run millions of simulations to validate every change to their self-driving stack — throughput is directly how fast they ship
- From that foundation, the team is expanding to own on-demand compute scheduling and capacity management across Applied: one platform, any workload, any environment
- Develop a multi-environment compute platform for workload scheduling, resource allocation, and node lifecycle
- Collaborate with customers and internal teams to translate compute needs into platform features
- Solve distributed systems challenges including GPU scheduling, autoscaling, and resource efficiency
- Ensure high reliability and scalability for production simulation systems
- Drive architecture decisions to expand simulation orchestration into a company-wide compute platform
- 4+ years of coding in Golang, Python, Java, or C++
- Deep Kubernetes expertise, including pod scheduling, node lifecycle, and autoscaling under load
- Experience building or operating workload scheduling systems (e.g., dispatch, bin-packing, quota enforcement)
- Self-starter with experience driving production system development from 0 to 1
- 4+ years building complex backend systems on major cloud providers
- 5+ years of backend engineering experience, with a track record of owning production distributed systems
- Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles
- Experience with cloud cost optimization strategies for high-scale compute workloads
- Experience managing multi-cluster environments using Infrastructure as Code
- Multi-cloud (AWS, OCI, GCP, Azure) or on-prem Kubernetes deployments
- Experience scheduling GPU workloads or managing GPU capacity at scale
- Experience building custom Kubernetes controllers or operators