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описание
Lifebit develops a patented federated technology that brings analysis and computation to sensitive biomedical data where it resides. Its Federated AI Platform helps governments, health systems, and pharmaceutical institutions securely analyze distributed biomedical and real-world data for precision medicine, public health, and research.
задачи
Design and implement autonomous AI agents using frameworks such as LangGraph, CrewAI, or AutoGen;
Develop reasoning loops that decompose high-level research goals into actionable sub-tasks;
Build and optimize Advanced RAG pipelines integrating structured clinical data and unstructured scientific literature;
Create and maintain tools that allow AI agents to interface safely with Lifebit’s federated APIs, SQL databases, and bioinformatic execution engines;
Implement secure, sandboxed code-interpreter capabilities for Python and R data visualization and statistical analysis;
Fine-tune LLMs for function-calling and tool-use accuracy in the life sciences domain;
Develop evaluation frameworks to measure agentic performance, truthfulness, and safety in clinical contexts;
Implement Human-in-the-loop patterns for reviewing high-stakes scientific decisions;
Partner with Security teams to enforce data privacy boundaries and prevent prompt injection or unauthorized data egress in federated nodes;
Work with Product and UX teams to design interfaces for interacting with agentic systems;
Scale agentic workloads in production using Kubernetes for low-latency reasoning and efficient token usage.
требования
BSc/MSc in Computer Science, Artificial Intelligence, Machine Learning, or a highly quantitative field;
PhD preferred;
2+ Years of hands-on experience as an AI or ML Engineer building a validated real product;
Experience ideally gained in a product-led biotech, health-tech, or SaaS company;
Deep proficiency in Python, Typescript, and standard ML frameworks such as Langfuse, PyTorch, TensorFlow, JAX, and Scikit-learn;
Proven experience with Large Language Models, including fine-tuning and RAG architectures;
Familiarity with AWS, Azure, or GCP;
Experience deploying models in Docker and Kubernetes environments;
Ability to navigate ambiguity and drive AI projects from concept to production without constant oversight;
Nice to have: Experience working with biological, genomic, or clinical data.
условия
Competitive salary and performance-based incentives;
Annual personal development budget of £1,000;
Access to industry conferences, training, and certifications;
21–25 Days of annual leave;
International and diverse team;
Exposure to cloud, data analysis, ML, life sciences, and big data;
Remote-first work model with virtual collaboration;
Equal opportunity employer committed to diversity, equity, and inclusion.
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