Staff Software Engineer

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.

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.

We are looking for a Staff Software Engineer, Backend & Data to set the technical direction for the core data infrastructure behind Condor's financial intelligence platform. This role sits at the heart of how Condor turns complex clinical and financial activity into intelligence that enterprise biopharma teams trust to run their operations.

You will design and own the data foundations that power Condor's financial engine and AI-driven capabilities. That means modeling highly complex, high-stakes data, building reliable pipelines and services, and ensuring that downstream product features and intelligence workflows operate with accuracy, consistency, and scale. As a Staff engineer, you will drive architectural decisions that span multiple teams and establish the patterns others build on. The systems you shape will directly support mission-critical finance and operational use cases, not dashboards or experiments.

This is a hands-on, high-leverage role with broad technical ownership. You will work across backend services, data pipelines, and APIs, taking the hardest problems from design through production, while raising the bar for how the wider engineering organization models, moves, and serves data. You will help define the schemas, transformations, and architectural patterns that become the backbone of the platform as it scales. While the primary focus is backend and data engineering, you are expected to engage pragmatically across the stack—including machine learning and AI systems—to ensure data and intelligence are surfaced correctly in the product.

This role is for engineers who want to build durable infrastructure under real-world complexity, where correctness, trust, and scale are not optional, and where the systems you design will shape how an entire industry operates.

• Set technical direction for Condor's data platform, driving cross-team architectural decisions on data modeling, storage strategies, and integration with LLMs, embeddings, and vector databases.

• Design, build, and maintain scalable data pipelines that ingest, normalize, and transform financial and clinical trial data from multiple internal and external sources, with a focus on making data suitable for analytics, reporting, and LLM-based AI agents.

• Develop backend services and data access layers that expose high-quality financial data to internal systems and customer-facing features, ensuring data is structured for direct consumption by LLMs and automated workflows.

• Implement and operate embedding pipelines and vectorized representations of structured and semi-structured data to support semantic search, RAG, and agentic workflows.

• Partner with ML and AI engineers on feature pipelines, model inputs, and serving patterns, ensuring data foundations support both classical machine learning and LLM-based systems in production.

• Optimize database performance, query execution, and batch processing jobs to support large-scale financial datasets and AI-driven access patterns.

• Work closely with product managers and analysts to translate business requirements into durable datasets, metrics, and APIs.

• Contribute to frontend implementations to ensure data-heavy and AI-enabled features are presented clearly and correctly.

• Mentor engineers across teams on data modeling best practices, SQL performance optimization, ETL design patterns, and building reliable, observable data systems.

• 8+ years of professional software engineering experience, with a strong focus on backend development and data engineering, including demonstrated Staff-level technical leadership and cross-team impact.

• Deep expertise in SQL and relational database design (PostgreSQL, MySQL, or similar), including complex analytical queries and performance tuning.

• Experience building and operating ETL or ELT pipelines using orchestration frameworks (Airflow, Dagster, Prefect, or AWS Step Functions).

• Production experience building AI-powered data systems, including vector databases (pgvector, Pinecone), embedding pipelines, and designing data access patterns for RAG and agentic workflows.

• Solid exposure to machine learning: understanding of the ML lifecycle (feature engineering, training, evaluation, and serving) and experience building the data foundations that power ML and AI systems in production.

• A pragmatic approach to the full stack, including a willingness to contribute to React codebases to ensure data-heavy features are delivered end-to-end.

• Familiarity with data quality validation, testing, and monitoring practices in production systems.

• Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience.

• Strong proficiency in Python, with experience using web frameworks (Django, DRF).

• Experience working with modern data warehouse platforms (Snowflake, BigQuery, Redshift).

• Hands-on experience with the ML/MLOps stack—building feature pipelines, and training or serving models in production (SageMaker, MLflow, or similar).

• Experience integrating backend systems or data pipelines with LLM APIs for enrichment, summarization, or analysis.

• Experience with data transformation and validation tools (dbt, Great Expectations).

• Experience building production AI features end-to-end, including LLM integration, prompt engineering, agentic workflows, and observability/evaluation frameworks.

• Experience working in a Series A-C startup with rapid scale.

• Competitive compensation and meaningful equity participation

• Comprehensive employee benefits, including 100% company-paid health, dental, vision, and life insurance

• 401(k) plan with a 3% company match that vests immediately

• Unlimited PTO

Condor is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other legally protected status.