Senior Software Engineer

We’re a fast-moving, top tier VC funded Silicon Valley startup, building ambitious products at the intersection of web apps and modern AI systems. That means we’re looking for people who thrive in ambiguity, move quickly, and love building zero-to-one products. We ship fast, cut clean corners, and obsess over reliability, security and craft. You’ll work end-to-end—from whiteboard to prod—owning services, shaping architecture, and raising the bar for engineering excellence.

‍

You’ll fit right in if you’ve worked in an early-stage company before — or if you’ve always wanted to experience the intensity and freedom of a startup. We value initiative over process, learning over perfection, and collaboration over hierarchy.

‍

Things change daily, and that’s part of the fun: you’ll wear multiple hats, experiment, and help shape both the product and the culture from the very beginning.

• Build production-quality products and APIs: design and implement frontends, backend services, and cloud-native distributed systems with simplicity, performance, scalability, and maintainability; ship pragmatic REST, GraphQL, or gRPC interfaces with versioning, pagination, authentication and authorization, schema evolution, and backward compatibility.
• Rapid iteration with ownership and bias for action: iterate quickly with a bias for delivery, take autonomy in decision-making from day one, and adapt comfortably to a fast-paced, evolving codebase; own design, implementation, and production outcomes.
• Operate with extreme leverage and build guardrails: aggressively use modern AI tools (e.g., Claude) to accelerate development while maintaining correctness, reproducibility, and maintainability; write rigorous unit and integration tests, maintain meaningful coverage, and produce clear documentation—especially for AI-assisted code.
• Evolve the AI stack and make principled tradeoffs: prototype and productionize LLM-dependent features (prompt pipelines, retrievals, structured outputs, evals, guardrails, memory); understand when AI is the right tool versus simpler deterministic solutions, balancing latency, cost, and reliability.
• Distributed systems & cloud architecture: design and operate cloud-native systems with a solid understanding of scaling, failure modes, state management, consistency models, and graceful degradation.
• Collaborate with product and design: work closely to define roadmap and architecture, turn ambiguous problems into secure, compliant, and high-quality user experiences, and ensure alignment across teams.
• Mentor and influence teammates: guide through code reviews, design discussions, tech talks, and crisp documentation; promote high engineering standards and team growth.

• Curiosity as a habit: you read release notes, try new runtimes and models, and build weekend prototypes for fun, a lot of your Youtube consumption is hacking and learning new technologies, and you enjoy learning what you don’t know on your own
• Self-driven: you find the problem behind the problem, align stakeholders, and land outcomes without hand-holding.
• Systems mindset: you think in SLOs, budgets (latency/cost/error), blast radius, and graceful degradation.
• API ergonomics: you care about naming, error design, rate limits, observability, generated SDKs, and docs that don’t lie.
• Data foundations: you know when to pick Postgres vs columnar vs KV vs vector; you’ve shipped schema migrations and zero-downtime deploys.
• LLM-practical: you’ve built RAG or agents in anger; you understand context windows, tokenization, evals, and prompt/tooling hygiene.
• Security instincts: you default to least privilege, tame secrets, and design auth flows that survive real traffic.

• Hands-on with vector DBs (e.g., FAISS, HNSW, Milvus, PGVector), embeddings pipelines, and hybrid ranking.
• Can Design retrieval layers: build embeddings pipelines, vector indexes, hybrid search (BM25 + ANN), chunking/merging strategies, and memory graphs.
• Protocol-fluent: you actually enjoy HTTP/1.1 vs HTTP/2/3 quirks, caching semantics, content negotiation, and idempotency.
• Experience with model hosting (self-hosted inference servers, serverless GPUs, or managed endpoints) and caching/streaming strategies.
• Infra chops: containers, IaC, CI/CD, service meshes, feature flags, canary/blue-green, cost observability.
• Frontend XP with modern TypeScript frameworks and component systems; accessibility and performance budgets.
• Prior startup experience or meaningful open-source contributions.

• Bias to action: short feedback loops, measured experiments, reversible decisions.
• Quality without ceremony: tests, tracing, and dashboards are table stakes.
• Written culture: design docs, runbooks, and decision logs that future-you will thank you for.
• Flexible, ownership-heavy: take on scope that excites (and scares) you a little.

• You’ve shipped a user-visible feature end-to-end and made it boringly reliable.
• You’ve hardened an API (or three): clear contracts, observability, and error budgets in place.
• You’ve improved our AI retrieval/memory layer with measurable gains in quality or cost.
• You’ve leveled up teammates via patterns, docs, or tooling that sticks.

• Be part of the core technical team at an early-stage, well-funded AI startup
  
• Work directly with experienced founders and top-tier investors
  
• Solve hard, real-world problems where thoughtful engineering matters more than hype
  
• High ownership, high impact, and rapid growth opportunities
  
• Competitive compensation, meaningful equity, benefits, and strong long-term upside

We are backed by a top-tier Silicon Valley venture capital firm and led by two seasoned Silicon Valley entrepreneurs and executives who have scaled startups from inception to IPO, led successful exits to Fortune 500 companies, and tripled the valuation of publicly traded firms in record time.