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 acrossmodels / 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 upwardLatency and token-cost improvements per featureSLO attainment and incident trendsAdoption of templates / connectors / IaC across product teamsClarity and usage of documentation and recorded walkthroughsHiring process
Focused coding exercise (2–3h) : ingestion → retrieval → tool-calling endpoint with tests, traces, and evalsSystems design (60m) : multi-tenant GenAI service, reliability, and rollout strategyGenAI deep dive (45m) : RAG, guardrails, eval design, and cost / latency tradeoffsDocs 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.
Email : info@cerebry.co