Senior Software Engineer (Machine Learning)

Valor Equity Partners is a different kind of private investment firm. We pioneered the idea of operational growth. We work side-by-side, shoulder-to-shoulder, to help grow the operations of great companies solving the world’s biggest problems. We invest in technology and technology-enabled companies that innovate and disrupt existing industries — from biosciences to transportation to food to health and wellness. We’ve had the honor of serving some of the world’s greatest entrepreneurs and companies, including Tesla, SpaceX, Anduril, Eight Sleep, GoPuff, and others .

On the Valor Labs Team, we develop cutting edge machine learning models to derive proprietary investment insights and build software applications to augment the Firm’s investment decision making process. As a small team of software engineers and data scientists with diverse backgrounds, we work collaboratively on wide-ranging problems to deliver high-impact products for the Firm.

As a Software Engineer on our data science and machine learning team, you will contribute directly to the development of high-impact products. Working together with data scientists, engineers, and stakeholders, you will translate complex project requirements into actionable technical solutions and work collaboratively to build, deploy, monitor, and maintain those solutions in production. Your technical expertise and commitment to excellence will help drive the adoption of best practices and ensure the highest level of rigor in everything we do.

B.S. in Computer Science or related field

5+ years of experience developing production-ready software systems

Although not necessary, prior work experience in financial services is highly valued

Expertise in end-to-end machine learning operations: model deployment, monitoring, and retraining, supporting integration with production data pipelines and API services.

Proficient with Python, especially machine learning libraries like NumPy, Pandas, Scikit-Learn, and PyTorch

Proficient with SQL, including transactional ( e.g. , PostgreSQL) and analytical ( e.g. , BigQuery) databases

Professional experience with most, if not all, of the following:

Containerization ( e.g. , Kubernetes and Docker)

Data processing ( e.g. , Prefect, Airflow, and dbt)

Parallel processing (e.g., Ray, Dask, and Spark)

Cloud infrastructure (e.g., Google Cloud Platform)

Continuous integration/continuous deployment (e.g. GitHub Actions)

Infrastructure as code (e.g., Terraform)

Tools to support machine learning operations (e.g., MLFlow and DVC)

Humble, hard-working, and collaborative