Condor Software
Staff Software Engineer
San Francisco · Staff+
Sponsorship not specifiedDetected 168 days ago
PythonReactDjangoBackend DevelopmentFull-Stack DevelopmentSQLPostgreSQLMySQLBigQuerySnowflakeRedshiftVector DatabasesAWSMachine LearningAirflowdbtData EngineeringLLMsRAGMLOpsAI OrchestrationLeadership
About the role
- 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.
- This is a hands-on, high-leverage role with broad technical ownership.
Responsibilities
- 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.
- Optimize database performance, query execution, and batch processing jobs to support large-scale financial datasets and AI-driven access patterns.
- Mentor engineers across teams on data modeling best practices, SQL performance optimization, ETL design patterns, and building reliable, observable data systems.
- 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.
Requirements
- 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.
- Familiarity with data quality validation, testing, and monitoring practices in production systems.
- Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience.
Nice to have
- 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 working in a Series A-C startup with rapid scale.
- 401(k) plan with a 3% company match that vests immediately
Skills
- Condor exists to change that.
- 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.
Compensation
- Competitive compensation and meaningful equity participation
Benefits
- Competitive compensation and meaningful equity participation
- Comprehensive employee benefits, including 100% company-paid health, dental, vision, and life insurance
- 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.
- 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.
- 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.
Equal opportunity
- equal opportunity employer.
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