Condor Software is the Financial Intelligence Platform for life sciences that unifies clinical, operational, and financial data to give biopharma teams real-time financial visibility.
ABOUT CONDOR Every year, hundreds of billions of dollars are invested to discover and develop new therapies, yet the financial infrastructure behind that work has not kept pace. Clinical operations and finance live in disconnected worlds, forcing teams to make high-stakes decisions using fragmented tools and static data. Condor exists to change that. We are a system of action, building the financial intelligence layer that will power the next era of clinical development. Condor connects clinical operations, vendor activity, and financial signals into a single, real-time intelligence layer, giving R&D and finance leaders true command over how their organizations operate. Condor is pharma-native, AI-driven infrastructure built to scale industry standards we helped define with Big 4 partners. It powers prediction, control, and execution across the most complex R&D environments in the world. WHY THIS MATTERS NOW Condor has moved past proving the concept. Enterprise teams already trust Condor to run critical operations and finance. The work ahead is the hardest part: scaling something people depend on when the stakes are this high. Condor is a high-growth company backed by top institutional partners like Felicis and 645 Ventures, growing rapidly with Top 200 biopharma companies. This is a rare opportunity to help build foundational infrastructure that will shape how new therapies reach patients. THE ROLE As a Senior AI/ML Engineer at Condor, you will own the design and production operation of the AI agents and machine learning systems at the core of Condor's financial intelligence platform. This is the engineering work that turns Condor from a system of record into a system of action: agents that forecast, reconcile, and explain financial signals across the most complex R&D environments in the world. You will be responsible for taking agentic systems from prototype to reliable, observable, production-grade services—running at scale in a live AWS environment where correctness and auditability are non-negotiable. Alongside LLM-based agents, you will build, train, and serve custom models where they outperform general-purpose approaches, owning those models through their full lifecycle in production. This is not prompt-tuning in a notebook. You will build the durable primitives—tool interfaces, retrieval systems, evaluation harnesses, and serving infrastructure—that let intelligent systems operate dependably on financial data that customers stake real decisions on. You will work close to real customer use cases as a core member of a cross-functional product team, collaborating with product managers, designers, quality engineers, and platform engineers to take AI capabilities from concept through production. This role is for engineers who want to solve hard problems at the frontier of applied AI, and build infrastructure that becomes essential to how an industry operates. KEY RESPONSIBILITIES - Design, build, and operate LLM-based agents in production, owning their reliability, latency, cost, and observability at scale. - Build and maintain the infrastructure that supports agentic workflows—tool schemas, retrieval systems, orchestration, and automated reasoning—on a serverless AWS architecture. - Build, train, and serve custom ML models in production where they add value over general-purpose LLMs, owning the full lifecycle from data and training through deployment, monitoring, and retraining. - Design and implement evaluation, testing, and guardrail systems that ensure AI outputs meet the correctness and auditability standards required for financial data. - Build backend services and APIs that integrate AI capabilities with complex workflows across clinical trial accounting, financial forecasting, and budgeting. - Build frontend components and interfaces as needed to surface AI capabilities in the product, using React. - Investigate and resolve complex production issues across AI and platform systems, performing root cause analysis and implementing long-term architectural improvements. - Write clean, maintainable, well-tested code following internal development standards and best practices. - Participate in architectural discussions and technical planning, contributing to the evolution of Condor's AI-native platform. - Mentor team members on applied AI, system design, and technical excellence. REQUIRED SKILLS & EXPERIENCE - Minimum of 5+ years of software engineering experience, with significant recent focus on AI/ML systems. - Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience. - Demonstrated experience running LLM-based agents or AI-powered automation in production—not just prototypes—including responsibility for their reliability, evaluation, and cost. - Hands-on experience deploying and operating AI/ML systems in a cloud environment, preferably AWS (Lambda, ECS/Fargate, SageMaker, Bedrock, or similar). - MLE experience: hands-on experience building, training, and serving at least one custom model in production, owning it end to end. - Deep expertise in Python and associated frameworks. - Strong track record of building and scaling backend systems, APIs, and data-intensive applications. PREFERRED QUALIFICATIONS - Experience with agentic frameworks and LLM tooling (e.g., function calling/tool use, retrieval-augmented generation, evaluation frameworks) using providers such as Anthropic Claude or OpenAI. - Experience with AWS ML and AI services (SageMaker, Bedrock) and serverless infrastructure. - Experience building evaluation and observability systems for LLM applications. - Experience with TypeScript and React for full-stack feature delivery. - Familiarity with Django or Flask. - Experience in regulated, high-correctness, or financial data domains. WHAT WE OFFER - Competitive compensation and meaningful equity participation - Comprehensive employee benefi