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Stack AV is building autonomous truck systems backed by a heavy ML and systems engineering stack: C++, Python, PyTorch, Kafka, and Kubernetes for training and inference workloads. The hiring mix is 62% senior/staff engineers and 25% data specialists, reflecting the technical depth required for safety-critical autonomous systems. Active projects span real-time motion planning, long-range detection, and simulation-driven testing — all core to solving the latency, safety, and reliability challenges that dominate their pain-point list.
Tech Stack 34 technologies
Core StackC++ Python Linux PyTorch GitHub gRPC Apache Spark Iceberg Go PostgreSQL Redis Kafka Docker Kubernetes Bazel Boost POSIX FreeRTOS SafeRTOS Google Docs Google Sheets WebSockets QUIC WebRTC Google Workspace Ray Tilt SQL CAN bus
What Stack AV Is Building
◆Challenges
- Ensuring safety and performance standards
- Complex ambiguous situations
- Low latency/high throughput processing
- Existing ml infra performance issues
- Real-time safety-critical ml deployment
- Improving autonomous truck safety
- Resource-constrained environment
- Safety critical applications
- Improving decision-making reliability
- Long-range detection capabilities
▲Active Projects
- Daily driving and testing of autonomous trucks
- Clockwork
- Tooling and infrastructure for ml model iteration
- Autonomy system development for autonomous trucks
- Scalable semantic search service for multimodal data
- Simulation scenario test case development
- Long-range detection capabilities
- Infrastructure for ml training and inference workloads
- Motion planning components for self-driving systems
- Inference service using llms and vector databases
Hiring Activity
Steady30 roles · 10 in 30d
Department
Seniority
Notable leadership hires: Tech Lead
Company intelligence
