Senior Software Engineer (Machine Learning)

Valor Equity PartnersChicago, IL
93d

About The Position

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.

Requirements

  • B.S. in Computer Science or related field.
  • 5+ years of experience developing production-ready software systems.
  • Expertise in end-to-end machine learning operations: model deployment, monitoring, and retraining.
  • 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 containerization (e.g., Kubernetes and Docker).
  • Experience with data processing tools (e.g., Prefect, Airflow, and dbt).
  • Experience with parallel processing (e.g., Ray, Dask, and Spark).
  • Experience with cloud infrastructure (e.g., Google Cloud Platform).
  • Experience with continuous integration/continuous deployment (e.g. GitHub Actions).
  • Experience with infrastructure as code (e.g., Terraform).
  • Experience with tools to support machine learning operations (e.g., MLFlow and DVC).

Nice To Haves

  • Prior work experience in financial services is highly valued.
  • Humble, hard-working, and collaborative attitude.

Responsibilities

  • Develop high-impact software products in collaboration with data scientists and engineers.
  • Translate complex project requirements into actionable technical solutions.
  • Build, deploy, monitor, and maintain software solutions in production.
  • Drive the adoption of best practices in software development.
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