{"id":731369,"url":"https://alion.io/job/bio-ai-engineer","title":"AI Engineer","company":{"id":669402,"name":"Bio Protocol","domain":"bio.xyz","url":"https://alion.io/company/bio-5","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":8,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"unspecified","remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"GraphQL","optional":false},{"name":"gRPC","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SFT","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Terraform","optional":false},{"name":"Tool Use","optional":false},{"name":"TypeScript","optional":false},{"name":"Weaviate","optional":false},{"name":"PostgreSQL","optional":true},{"name":"Redis","optional":true},{"name":"RLAIF","optional":true},{"name":"RLHF","optional":true}],"status":"live","first_seen_at":"2026-01-17T21:19:52Z","employer_posted_date":"2026-01-17","last_verified_at":"2026-10-13T11:27:57Z","board_verified":true,"closed_at":null,"days_open":268,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":268},"description":"Bio is a decentralized science protocol that helps launch and grow AI-driven biotech research. It enables scientists to raise funds, create value from their work, and distribute that value directly to their communities. Since 2023, Bio has directed over $50m to global researchers, offering an alternative to traditional pharma funding. Backed by investors like Binance Labs, Northpond Ventures, and Animoca Brands, Bio accelerates real-world therapeutics across longevity, brain health, fertility, psychedelic science, and more.\nWhy this role exists\nAs a Member of Technical Staff on the AI Agents team, you’ll design, build, and scale the core agent systems that power Bio Protocol’s products. You’ll work closely with full-stack engineers and scientist-evaluators to create agents that can plan, use tools, and reason safely. This role offers the opportunity to shape the foundation of how AI collaborates with human scientists - combining technical depth with real-world scientific impact. While technical skills matter, we believe drive and cultural fit matter most. If you're passionate about shipping impactful work and excited by our mission, we encourage you to apply - even if you don't check every single box.\nWhat you’ll do\nBuild agent capabilities for planning, tool use, memory, and context management, and ship them into production.\n\nIntegrate agents with internal and external tools and data sources (retrieval systems, structured datasets, lab/biomed APIs, spreadsheets, search), with robust schemas and safeguards.\n\nDevelop quality and evaluation systems, including unit, regression, and scenario/benchmark tests, telemetry, and automated scoring.\n\nCollaborate with scientists to analyze failure modes and improve performance.\n\nPartner with the knowledge and ontology team to ensure outputs are source-traceable and compliant with provenance standards.\n\nImplement safety measures, guardrails, and sandboxed execution for risky operations.\n\nOptimize performance and reliability through profiling, idempotency, retries, rate limiting, and uptime management.\n\nInstrument data pipelines for supervised fine-tuning and reinforcement learning when needed.\n\nContribute to the agent platform, including services, APIs, orchestration, CI/CD, and observability.\n\nExample projects (first 90 days)\nDeliver a multi-tool agent capable of executing long-horizon scientific tasks with memory and self-correction, supported by regression tests and telemetry.\n\nImplement automated citation enforcement, including source checking, freshness validation, and provenance display in the UI.\n\nBuild an evaluation dashboard tracking competency pass rates, latency, and failure modes.\n\nSuccess metrics\n\nImproved pass rates and reduced critical error rates across core scientific competencies.\n\nPerformance against SLOs for latency, task success, tool-call reliability, and uptime.\n\nIncreased coverage of regression and evaluation scenarios.\n\nBroader adoption of the agent platform by internal teams.\n\nQualifications\nExperience building production software in Python and/or TypeScript, with strong systems and API design skills (FastAPI, gRPC, GraphQL, or similar).\n\nProven experience shipping LLM applications or agentic systems (tool use/function calling, retrieval/RAG, structured outputs, evaluation, or observability).\n\nFamiliarity with agent/orchestration frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, MCP) and vector databases (FAISS, Weaviate, Pinecone).\n\nExperience with cloud infrastructure and containers (AWS, GCP, or Azure), Docker/Kubernetes/Terraform, CI/CD, and production telemetry.\n\nAbility to translate research prototypes into robust, scalable systems.\n\nNice to have\nExperience with fine-tuning and reinforcement learning (RL, RLAIF, RLHF), including reward design and offline evaluation.\n\nFamiliarity with benchmarks and evaluations such as SWE-Bench, OS-World, or tau-bench.\n\nKnowledge of retrieval and knowledge systems, including schema and ontology design, entity modeling, and provenance tracking.\n\nBackground in agentic system safety and security (sandboxing, isolation, permissions, auditability).\n\nExposure to life sciences or scientific computing and collaboration with domain experts.\n\nHow we work\nEvidence-first: every output is grounded and source-verifiable.\n\nTight feedback loops: weekly quality reviews with scientists to ship, measure, and improve.\n\nPlatform mindset: we create safe, reusable systems that empower others to build new agent capabilities.\n\nTools you’ll use\nPython, TypeScript, FastAPI/gRPC, Postgres, Redis/queues, Docker, Kubernetes, Terraform, cloud LLM APIs, open-weight models, vector databases, telemetry and observability tools, and internal agent/evaluation 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