Senior/Staff Software Engineer, Distributed Systems
About Hedra
Hedra is a research and infrastructure lab advancing post-training and inference for multimodal and physical AI. Our ambition is to bring recursive self-improvement to models that learn from and interact with the real world.
We combine reinforcement learning research with GPU compute and optimized inference to help teams post-train, serve and continuously improve models they own.
We're backed by Index Ventures, a16z and Abstract Ventures. Our team works fully in person in San Francisco.
We're looking for people with startup experience who take ownership of their work and care deeply about the problems our customers need to solve. You'll help build the technology, products and business that turn this research into a lasting company.
The Role
We’re looking for a Senior or Staff Software Engineer with deep experience building and operating distributed production systems.
You’ll work on the infrastructure underlying Hedra’s inference platform: systems that schedule and route compute, serve models efficiently, handle high-throughput workloads, and remain reliable as both traffic and the number of models we support grow.
The problems are often ambiguous and don’t have obvious answers. We’re looking for someone who can reason from first principles, identify bottlenecks and failure modes before they become problems, and make thoughtful tradeoffs across performance, reliability, complexity, and cost.
You do not need to come from an AI company or already be an expert in model inference. We care much more about depth in distributed systems and your ability to apply that experience to a new problem space.
What You’ll Do
Design, build, and operate distributed systems that power Hedra’s inference infrastructure.
Build systems for scheduling, routing, and managing compute-intensive workloads across heterogeneous resources.
Improve throughput, latency, reliability, and resource utilization across our serving infrastructure.
Design systems that remain predictable and resilient under load, partial failures, changing capacity, and unpredictable workloads.
Own production systems end to end, including deployment, CI/CD, testing and validation, observability, monitoring, alerting, debugging, and incident response.
Identify architectural bottlenecks and failure modes and drive solutions rather than waiting for problems to be fully specified.
Work across infrastructure, model serving, APIs, and developer-facing systems as the platform evolves.
Use modern agentic coding workflows as part of how you design, build, debug, and ship software.
Help shape the technical direction of a small engineering organization where individual engineers have substantial ownership.
What We’re Looking For
5+ years of software engineering experience, with significant experience building distributed backend or infrastructure systems.
A track record of designing and operating high-availability production services at meaningful scale.
Strong distributed systems fundamentals, including experience reasoning about concurrency, queues, retries, failure recovery, consistency, backpressure, capacity, and system behavior under load.
Experience debugging complex production systems across multiple layers of the stack.
Strong judgment around architectural tradeoffs, particularly reliability, performance, complexity, and operational cost.
Experience with CI/CD, comprehensive testing and validation strategies, monitoring, alerting, and production operations.
Strong programming fundamentals and experience working in systems-oriented backend languages. Python experience is helpful given our stack.
Experience using agentic coding tools and workflows beyond basic prompting.
Comfort operating in an environment where problems are often underspecified and engineers are expected to independently determine the right approach.
Ability to communicate technical decisions clearly and collaborate with engineers across infrastructure, research, and product.
Nice to Have
Experience with inference or model-serving infrastructure.
GPU or accelerator infrastructure.
Compute scheduling, orchestration, or resource management.
High-throughput or low-latency systems.
Kubernetes or other cluster orchestration systems.
Performance optimization and profiling.
Developer infrastructure, APIs, SDKs, or platform engineering.
Experience operating infrastructure across cloud and/or bare-metal environments.
Benefits
Competitive compensation and equity
401k
Healthcare (Silver PPO Medical, Vision, Dental)
Lunch and snacks at the office
This role is based in San Francisco, and we work together in person five days a week.