Staff AI Engineer

Job Description

Job Description

Description:

About BullFrog AI and bfArenas

BullFrog AI develops machine-learning platforms for drug discovery and clinical decision support. Our work integrates multi-omic, clinical, and real-world data with modern AI methods to accelerate decisions across the discovery-to-development pipeline.

bfARENAS is our decision-adjudication platform. It produces defensible, evidence-anchored rankings of complex scientific and commercial questions (e.g. drug target prioritization, program portfolio composition, vendor and partner selection) using a structured tournament approach with LLM-powered analysis. The platform has been featured in industry venues including AACR and the XTalks webinar series and supports an active engagement pipeline with pharmaceutical and technology partners.

We are hiring a senior engineer to own bfARENAS end-to-end and lead its evolution into a self-serve, client-grade platform.

What You Will Do

You will be the technical lead and full-time owner of bfARENAS, working directly with the Senior Director of AI, ML and Innovation. The role spans algorithm design, agent engineering, human-in-the-loop interface construction, and production infrastructure.

Specifically, you will:

• Advance the core algorithms by improving ranking quality, uncertainty quantification, and scaling characteristics; design and run evaluations that distinguish genuine algorithmic improvements from prompt-level artifacts.
• Build the agent layer by developing and refining the LLM-powered components that structure decision problems before adjudication and that maintain quality during execution.
• Construct human-in-the-loop interfaces that let domain experts contribute pairwise judgments, review and override automated decisions, and configure problem structure for new datasets. These are not internal tools, they will be used by scientists at pharmaceutical clients.
• Own the platform infrastructure including multi-provider LLM orchestration, structured-output handling, run reproducibility, cost tracking, telemetry, and resilient execution.
• Deliver client-facing outputs such as interactive visualizations, drill-down dossiers, and uncertainty-aware reporting that pharma scientists trust enough to make decisions on. This is what the market sees; it has to be polished.
• Maintain a standing quality discipline through bias detection, redundancy analysis, judgment diagnostics, and multi-judge reliability — built into the platform rather than performed ad hoc.

Year One Outcomes

A more scalable, more reproducible, and more autonomously operating bfARENAS that supports faster onboarding of new client datasets, more efficient large-scale runs, multi-model judge ensembles, and interactive client deliverables out of the box. You will be a named contributor on the next generation of the platform and on the publications and client engagements that come with it.

What You Bring

• 6+ years of production software engineering experience, with at least 2+ years building LLM-powered systems that other people depend on — not prototypes, not demos.
• Strong Python coding experience. The codebase is Python end-to-end.
• Real statistical literacy. Comfort with ranking and pairwise comparison models, bootstrap methods, and the difference between a misleading confidence interval and a useful one. Computer-science-with-statistics or statistics-with-computer-science backgrounds are welcome.
• Demonstrated ability to design evaluation harnesses for LLM systems. “I designed the eval that proved the prompt change was real” rather than “I tried some prompts.”
• Product instinct. You will be building interfaces that determine whether scientists trust the system enough to act on its outputs.
• Comfort owning a system end-to-end across algorithms, infrastructure, and user-facing experience. This role is not split into three.

Nice to Have

• Domain exposure to drug discovery, clinical development, or computational biology. Not required but shortens the ramp.
• Background in ranking systems, multi-criteria decision analysis, social choice theory, recommender systems, or tournament/matchmaking design.
• Experience shipping client-facing analytical products in regulated or scientific settings.
• Familiarity with multi-provider LLM orchestration (OpenAI, Anthropic) and structured-output workflows.
• Prior published work: engineering, research, or both.

Compensation and Process

Competitive base salary, public-company equity, and standard BullFrog benefits. Specific compensation range available on request and calibrated to experience.BullFrog AI is an equal opportunity employer.

Benefits:

• 15 days of paid time off annually
• 11 paid holidays annually
• Medical, Dental, and vision coverage with eligibility on the first day of employment
• Short-Term Disability
• 401 (k) with enrollment upon day one
• Eligibility for bonus and stock options based on a combination of individual and company performance.