СШАSoftware Engineer - Platform
2 нед. назад
СШАOnsite
pythondistributed systemscloud infrastructure
Build core infrastructure for AI evaluation platform's backend systems.
О компании
- is the platform for evaluating how AI models perform in the real world. Founded by researchers from UC Berkeley's SkyLab, we're on a mission to measure and advance the frontier of AI for real-world use, and to build the foundation for everyone to understand, shape, and benefit from it.
- Tens of millions of people use each month to evaluate how frontier systems handle the work they actually do. The preferences they share power the most transparent, rigorous, and human-centered evaluations in AI. Leading AI labs, enterprises, and independent researchers rely on our work and open datasets to understand how models behave in real workflows: agentic coding, creative generation, professional productivity, and beyond. We go beyond leaderboards and decompose what human experience reveals about AI, so models advance toward the work people actually do.
- We're a team of researchers, academics, builders, and creatives from UC Berkeley, Google, Stanford, and DeepMind. We seek truth, move fast, and value craftsmanship, curiosity, and impact over hierarchy. We're building a company where thoughtful, curious people from all backgrounds can do their best work together, in an office culture that radiates excellence, energy, and focus.
Обязанности
- Intelligence is looking for a Software Engineer - Platform to build the core infrastructure that sits beneath our online evaluation systems — the AI gateways, automated runtimes, and serving layers that make real-world model evaluation possible at scale.
- This is a critical part of the Service. Arenas are live, online systems: they route traffic across frontier models from many providers, handle bursty and unpredictable load, need to fail gracefully when upstream models do, and have to remain fair and consistent under all of it. We exist to build foundational infrastructure for our users that scales, is reliable, and makes the complexities of operating this infrastructure at scale disappear. We need a practitioner who's shipped this kind of infrastructure before and knows where the sharp edges are.
- Our AI gateway is currently in private, gated launch with individual developers as our primary users today; enterprise use cases will follow later. Next month we're shipping direct model access and new infrastructure features, and may begin collecting usage traces and building leaderboards shared with lab partners — so there's a lot of near-term, zero-to-one work ahead.
- You'll be an early member of our infrastructure team, working closely with researchers, engineers, and product leadership. The work is zero-to-one in places and scale-it-up in others. We move fast and stay rigorous.
- This is a hands-on individual contributor role — we're not hiring for a tech lead or SRE function right now; everyone on the team is heads-down building.
- Build API-based products from the ground up. Design and implement low-latency, high-reliability APIs for leaderboards, models, and arenas.
- Solve hard streaming problems. Handle SSE/streaming responses across heterogeneous providers, including partial failure recovery, mid-stream fallback, and consistent response normalization.
- Ship enterprise-grade infrastructure. Build the systems enterprise customers will eventually expect — rate limiting, authentication, usage metering, cost attribution, audit logging, and SOC 2 compliance — as we grow beyond our current individual-developer user base.
- Build deep observability. Instrument infrastructure with distributed tracing, latency breakdowns, token-level usage tracking, and real-time dashboards so customers (and we) can see exactly what's happening.
- Build AI-centered products. Integrate with our core evaluation platform, data, and customer-specific benchmarks. Collaborate with the research team to turn novel ideas into full-featured products. (This role does not involve data labeling or third-party data-verification work.)
- Flex across the stack. Contribute to the backend of our Leaderboards and Evals platforms when needed, helping unify our public and private data architectures.
Будет плюсом
- Cloud infrastructure experience (AWS, GCP, or Azure), Kubernetes, Terraform, and database systems like Postgres and Redis — helpful, but not a hard filter.
- Experience building API gateways, proxies, or developer tools (Bifrost, Kong, Envoy, Tyk, or custom).
- Background in AI/ML infrastructure, model serving, inference, or evaluation frameworks.
- Experience building enterprise-ready features: SSO, RBAC, audit logs, multi-tenancy.
- Experience building billing infrastructure around systems like Stripe, Metronome, and Orb.
- Familiarity with the modern AI infra stack (vLLM, LiteLLM, LangChain, etc.).
Условия
- This role is based in San Francisco, with a minimum of 3 days/week onsite. Fully remote candidates will only be considered with a very strong endorsement.
- We offer competitive compensation and equity aligned to the markets where our team members are based. The base salary range will depend on the candidate’s permanent work location.
- Comprehensive health and wellness benefits, including medical, dental, vision, and additional support programs.
- The opportunity to work on cutting-edge AI with a small, mission-driven team
- A culture that values transparency, trust, and community impact
- Come help build the space where anyone can explore and help shape the future of AI.
Другое
- ~5+ years of backend engineering experience, with meaningful time spent on distributed systems, infrastructure, or developer-facing platforms. Senior: proven delivery on meaningful backend work.
- Staff: extensive, deep experience owning backend systems end to end.
- Strong proficiency in Go — this is our primary backend language and a must-have for the role.
- Experience with LLM provider APIs (OpenAI, Anthropic, Google, etc.) and a working understanding of the challenges: streaming, token management, rate limits, model-specific quirks.
- A product-oriented mindset. You think about the developer experience of your APIs, not just the implementation. You ask "why" before "how."
- Comfort with ambiguity. We're a startup. Scope is fluid, context shifts, and you'll wear many hats. That should sound exciting, not stressful.