Staff Machine Learning Scientist, Agentic AI

About the role

For our client, we are seeking a Staff Machine Learning Scientist Agentic AI to join the team of a leader in the Pharmaceuticals & Biotech space. This role will lead work at the intersection of data, AI-enabled capabilities, and scalable technology delivery. You will work across engineering, product, operations, and business stakeholders to translate complex requirements into practical technology solutions. The position offers the opportunity to influence architecture, execution quality, and the technology capabilities that enable long-term growth within a life sciences environment.

Responsibilities

• Lead the design and deployment of autonomous multi-agent systems for hypothesis generation
• Advance the company's transformer-based foundation model by integrating multi-omic data
• Implement LLM reasoning frameworks to ensure accurate and explainable insights
• Architect systems that convert complex data into diagnostic and therapeutic insights
• Own the strategy and roadmap for agentic workflows in Biopharma Solutions
• Establish machine learning engineering standards for model transparency
• Drive technical consensus in peer reviews while defending architectural choices

Qualifications

• PhD or Master's in Computer Science, Bioinformatics, Statistics, or related field
• 8+ years in AI research or engineering with experience in multi-agent architectures
• Deep knowledge of agentic frameworks like LangChain or Claude Agent SDK
• Strong understanding of cancer genomics and mutational signatures
• Advanced development experience using PyTorch and distributed training on large GPU clusters

Benefits

• Remote work opportunity within the USA
• Access to advanced technology and data resources for cutting-edge research
• Opportunity to impact patient outcomes through innovative AI applications
• Collaborative environment with a strong AI and bioinformatics team

Location: Remote - US based candidates only, no visa sponsorship available
Compensation: $163,200 – $220,000 annually