SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Applied Intuition is a $15B-valued AI infrastructure company powering autonomous vehicles and physical AI systems across automotive, defense, trucking, construction, mining, and agriculture. The Control Center team builds the central operational platform that connects autonomous vehicle fleets to human operators—acting as the central nervous system for autonomous operations.
As a Software Engineer on the Control Center team, you will own core platform domains end-to-end, building low-latency, ultra-reliable backend systems that scale across diverse vehicle types and operational environments, from public cloud deployments to air-gapped, on-premise sites.
Key responsibilities:
- Architect, implement, scale, and maintain critical control center subsystems, from high-throughput telemetry ingestion to live asset state tracking and mission orchestration
- Design cloud and edge services where performance and correctness directly impact physical safety, including geofence enforcement, proximity alerts, and remote mission execution
- Collaborate directly with autonomy, perception, and vehicle-platform teams to establish robust APIs, data protocols, and state synchronization frameworks
- Build resilient data pipelines and APIs that transform high-volume vehicle metrics, logs, and state updates into actionable, queryable intelligence for customers
- Deploy, observe, and harden SaaS services operating across public cloud, private infrastructure, and air-gapped on-premise environments
- Partner with field engineers and deployment leads to debug live fleet operations, resolve real-world edge cases, and continuously close the feedback loop on platform reliability
REQUIREMENTS:
- 4+ years of proven backend/systems expertise designing, building, and operating production distributed systems using Go, C++, or Python
- Hands-on experience with high-throughput streaming architectures, event-driven systems, or time-series data management (e.g., Kafka, gRPC, time-series databases)
- Strong system design and API instincts: mastery of data modeling, API design, and distributed consensus, with focus on reliability, fault tolerance, and security
- Production ownership mindset: comfort managing the full software lifecycle including CI/CD, telemetry/observability, on-call rotations, and root-cause analysis
- Ability to take vague requirements, break down complex domain problems, and execute pragmatic trade-offs in a fast-paced environment
- Excellent cross-functional communication skills with a track record of bridging technical gaps between cloud software and hardware platforms
NICE TO HAVE:
- Familiarity with robotics protocols and middleware (CAN, MQTT, DDS, SOME/IP)
- Experience deploying on Kubernetes across cloud-hosted and air-gapped/on-prem environments
- Background in safety-critical, functional safety, or heavily regulated domains (mining, automotive, industrial automation)
