Mission
Transform Cerebry Research designs into production-grade GenAI features —retrieval-grounded, safe, observable, and ready for seamless product rollout.
Architect, code, evaluate, and package GenAI services that power Cerebry end-to-end.
Why this is exciting (Ownership-Forward)
- Founder-mindset equity.
We emphasize meaningful ownership from day one.
- Upside compounds with impact.
Initial grants are designed for real participation in value creation, with refresh opportunities tied to scope and milestones.
- Transparent offers.
We share the full comp picture (salary, equity targets, vesting cadence, strike/valuation context) during the process.
- Long-term alignment.
Packages are crafted for builders who want to grow the platform and their stake as it scales.
What you’ll build
- Retrieval & data grounding: connectors for warehouses/blobs/APIs; schema validation and PII-aware pipelines; chunking/embeddings; hybrid search with rerankers; multi-tenant index management.
- Orchestration & reasoning: function/tool calling with structured outputs; controller logic for agent workflows; context/prompt management with citations and provenance.
- Evaluation & observability: gold sets + LLM-as-judge; regression suites in CI; dataset/version tracking; traces with token/latency/cost attribution.
- Safety & governance: input/output filtering, policy tests, prompt hardening, auditable decisions.
- Performance & efficiency: streaming, caching, prompt compression, batching; adaptive routing across models/providers; fallback and circuit strategies.
- Product-ready packaging: versioned APIs/SDKs/CLIs, Helm/Terraform, config schemas, feature flags, progressive delivery playbooks.
Outcomes you’ll drive
- Quality: higher factuality, task success, and user trust across domains.
- Speed: rapid time-to-value via templates, IaC, and repeatable rollout paths.
- Unit economics: measurable gains in latency and token efficiency at scale.
- Reliability: clear SLOs, rich telemetry, and smooth, regression-free releases.
- Reusability: template repos, connectors, and platform components adopted across product teams.
How you’ll work
- Collaborate asynchronously with Research, Product, and Infra/SRE.
- Share designs via concise docs and PRs; ship behind flags; measure, iterate, and document.
- Enable product teams through well-factored packages, SDKs, and runbooks.
Tech you’ll use
- LLMs & providers: OpenAI, Anthropic, Google, Azure OpenAI, AWS Bedrock; targeted OSS where it fits.
- Orchestration/evals: LangChain/LlamaIndex or lightweight custom layers; test/eval harnesses.
- Retrieval: pgvector/FAISS/Pinecone/Weaviate; hybrid search + rerankers.
- Services & data: Python (primary), TypeScript; FastAPI/Flask/Express; Postgres/BigQuery; Redis; queues.
- Ops: Docker, CI/CD, Terraform/CDK, metrics/logs/traces; deep experience in at least one of AWS/Azure/GCP.
What you bring
- A track record of shipping and operating GenAI/ML-backed applications in production.
- Strong Python , solid SQL , and systems design skills (concurrency, caching, queues, backpressure).
- Hands-on RAG experience (indexing quality, retrieval/reranking) and function/tool use patterns.
- Experience designing eval pipelines and using telemetry to guide improvements.
- Clear, concise technical writing (design docs, runbooks, PRs).
Success metrics
- Evaluation scores (task success, factuality) trending upward
- Latency and token-cost improvements per feature
- SLO attainment and incident trends
- Adoption of templates/connectors/IaC across product teams
- Clarity and usage of documentation and recorded walkthroughs
Hiring process
- Focused coding exercise (2–3h): ingestion → retrieval → tool-calling endpoint with tests, traces, and evals
- Systems design (60m): multi-tenant GenAI service, reliability, and rollout strategy
- GenAI deep dive (45m): RAG, guardrails, eval design, and cost/latency tradeoffs
- Docs review (30m): discuss a short design doc or runbook you’ve written (or from the exercise)
- Founder conversation (30m)
Apply
Share links to code (GitHub/PRs/gists) or architecture docs you authored, plus a brief note on a GenAI system you built—problem, approach, metrics, and improvements over time.
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