AI Engineer
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Requirements
• Experience building production software in Python and/or TypeScript, with strong systems and API design skills (FastAPI, gRPC, GraphQL, or similar). • Proven experience shipping LLM applications or agentic systems (tool use/function calling, retrieval/RAG, structured outputs, evaluation, or observability). • Familiarity with agent/orchestration frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, MCP) and vector databases (FAISS, Weaviate, Pinecone). • Experience with cloud infrastructure and containers (AWS, GCP, or Azure), Docker/Kubernetes/Terraform, CI/CD, and production telemetry. • Ability to translate research prototypes into robust, scalable systems. • Experience with fine-tuning and reinforcement learning (RL, RLAIF, RLHF), including reward design and offline evaluation. • Familiarity with benchmarks and evaluations such as SWE-Bench, OS-World, or tau-bench. • Knowledge of retrieval and knowledge systems, including schema and ontology design, entity modeling, and provenance tracking. • Background in agentic system safety and security (sandboxing, isolation, permissions, auditability). • Exposure to life sciences or scientific computing and collaboration with domain experts. • Evidence-first: every output is grounded and source-verifiable. • Tight feedback loops: weekly quality reviews with scientists to ship, measure, and improve. • Platform mindset: we create safe, reusable systems that empower others to build new agent capabilities. • Tools you’ll use • Python, 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 systems.
Responsibilities
• Build agent capabilities for planning, tool use, memory, and context management, and ship them into production. • Integrate agents with internal and external tools and data sources (retrieval systems, structured datasets, lab/biomed APIs, spreadsheets, search), with robust schemas and safeguards. • Develop quality and evaluation systems, including unit, regression, and scenario/benchmark tests, telemetry, and automated scoring. • Collaborate with scientists to analyze failure modes and improve performance. • Partner with the knowledge and ontology team to ensure outputs are source-traceable and compliant with provenance standards. • Implement safety measures, guardrails, and sandboxed execution for risky operations. • Optimize performance and reliability through profiling, idempotency, retries, rate limiting, and uptime management. • Instrument data pipelines for supervised fine-tuning and reinforcement learning when needed. • Contribute to the agent platform, including services, APIs, orchestration, CI/CD, and observability. • Example projects (first 90 days) • Deliver a multi-tool agent capable of executing long-horizon scientific tasks with memory and self-correction, supported by regression tests and telemetry. • Implement automated citation enforcement, including source checking, freshness validation, and provenance display in the UI. • Build an evaluation dashboard tracking competency pass rates, latency, and failure modes. • Success metrics • Improved pass rates and reduced critical error rates across core scientific competencies. • Performance against SLOs for latency, task success, tool-call reliability, and uptime. • Increased coverage of regression and evaluation scenarios. • Broader adoption of the agent platform by internal teams.
Benefits
• Bio Protocol's mission to accelerate real-world therapeutics across various fields including longevity, brain health, fertility, and psychedelic science. • Opportunity to shape the foundation of how AI collaborates with human scientists in a decentralized biotech research environment. • Work on cutting-edge projects that combine technical depth with real-world scientific impact. • Collaborate closely with full-stack engineers and scientist-evaluators, fostering teamwork across disciplines. • Access to robust schemas and safeguards for integrating agents with internal and external tools and data sources. • Development of quality and evaluation systems including unit tests, regression tests, scenario/benchmark tests, telemetry, and automated scoring mechanisms. • Implementation of safety measures such as guardrails and sandboxed execution to mitigate risks in AI operations. • Optimization opportunities for performance and reliability through profiling, idempotency, retries, rate limiting, uptime management, and more. • Contributions to the agent platform development including services, APIs, orchestration, CI/CD pipelines, observability tools.
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