Senior AI Engineer

About The Position

We are seeking a Senior AI Engineer to lead the design and development of agent-based AI systems that transform how computational biology work is done. This is a hands-on, high-ownership role focused on building production-grade AI tools that act as collaborators for scientists – automating workflows such as data interpretation, automatic annotation, and knowledge extraction.

You will operate at the intersection of cutting-edge AI (LLMs, agents) and real-world biological applications, helping define how agentic systems integrate into scientific research.

Responsibilities
Lead the design and implementation of agentic AI systems for scientific workflows
Build internal tools using LLMs, MCP, RAG pipelines, and multi-agent frameworks
For example, develop systems for automatic annotation of biological data, literature understanding, and knowledge extraction
Translate complex research workflows into AI-driven, semi-autonomous processes
Take ownership from idea to production: prototype deploy iterate
Work closely with computational biologists to identify and prioritize high-impact use cases
Define best practices for agent reliability, evaluation, and performance in scientific contexts
Contribute to the architecture of an AI-powered platform for biological discovery
Requirements
5+ years of experience in software engineering, applied AI, or ML systems
Proven track record of owning and delivering production-grade systems
Strong Python skills and solid software engineering fundamentals
Hands-on experience with benchmarking, evaluation, and validation of LLM/agent-based systems, including defining success metrics, running experiments, and analyzing performance tradeoffs
Hands-on experience with LLMs and agent frameworks (LangChain, OpenAI, Anthropic, or similar)
Experience building tools/products used by internal users or customers
Strong problem-solving skills and ability to operate in ambiguous, fast-moving environments
Excellent communication skills and ability to work cross-functionally