AI Engineer

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Bio Protocol

SiteLinkedInTodas as Vagas

11 - 50 funcionários

🧬 Biotecnologia

₿ Cripto

🤖 Inteligência Artificial

Biotechnology • Crypto • Artificial Intelligence

O Bio Protocol é uma camada financeira descentralizada (DeSci) e plataforma que tokeniza e financia pesquisas biotecnológicas em estágio inicial. Ele permite que DAOs (organizações autônomas descentralizadas) governadas pela comunidade e propriedade intelectual tokenizada levantem capital por meio de lançamentos de tokens BIO e Vendas de Ignição, oferece mecânicas de staking e fidelidade (BioXP), e fornece infraestrutura como uma Launchpad e um Motor de Liquidez. O protocolo também suporta ferramentas científicas impulsionadas por IA (BioAgents e o cientista BIOS AI) para acelerar a pesquisa, coordenação e comercialização de inovações científicas.

Descrição

• 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.

🎯 Requisitos

• 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. • Nice to have: • 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.

🏖️ Benefícios

• 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.