Machine Learning Engineer
Machine Learning - Senior Software Engineer (No Sponsorships)
Location: Chicago, IL
Overview
This private investment firm is an operationally focused investment organization that partners closely with founders and leadership teams to help scale high impact businesses. Their approach emphasizes hands‑on collaboration and long term value creation rather than passive capital deployment. The firm invests primarily in technology driven companies across sectors such as life sciences, mobility, food systems, and health, and has supported a number of category‑leading ventures.
The organization operates with a strong values driven culture centered on excellence, humility, integrity, and accountability. These principles guide how teams collaborate internally and how the firm engages with its portfolio companies and other stakeholders.
The Team
The internal Labs group is responsible for building advanced analytics, machine learning capabilities, and software tooling that support investment research and decision‑making. The team is intentionally small and multidisciplinary, bringing together engineers and data scientists who enjoy tackling ambiguous problems and delivering systems that are both technically rigorous and highly practical.
The Opportunity
The Senior Software Engineer (Machine Learning) will be a key contributor within the Labs team, responsible for developing and operating production‑grade machine learning systems. This role involves close partnership with data scientists and internal stakeholders to design scalable solutions that move from experimentation to reliable deployment.
The position spans the full ML lifecycle, including system design, implementation, release management, monitoring, and ongoing iteration. In addition to hands on technical work, this engineer will help shape development standards and influence architectural decisions as the platform evolves.
Responsibilities
- Architect, implement, and support machine learning backed applications in production
- Collaborate with technical and non‑technical partners to translate complex use cases into maintainable systems
- Deploy models into live environments and ensure ongoing performance through monitoring and retraining
- Build and integrate data pipelines and APIs that support ML workflows
- Contribute to tooling, documentation, and development best practices across the team
Background & Qualifications
- Bachelor's degree in Computer Science, Engineering, or a related technical discipline
- At least 5 years of experience delivering and supporting production software
- Experience in financial, investment, or data‑intensive environments is a plus, but not required
- Demonstrated experience managing end‑to‑end machine learning workflows in real‑world systems
- Strong proficiency in Python and common ML libraries, including NumPy, Pandas, Scikit‑Learn, and PyTorch
- Solid command of SQL and experience working with both operational and analytical databases (e.g., PostgreSQL, BigQuery)
- Hands‑on experience with many of the following technologies:
- Containerized applications and orchestration platforms (Docker, Kubernetes)
- Data orchestration and transformation tools (e.g., Prefect, Airflow, dbt)
- Distributed or parallel computing frameworks (Ray, Dask, Spark)
- Cloud platforms, with preference for Google Cloud
- Automated testing and deployment pipelines (GitHub Actions or similar)
- Infrastructure‑as‑code frameworks (Terraform)
- MLOps and experimentation tools (MLflow, DVC)
- A collaborative, low‑ego working style with a strong sense of personal accountability
FAQs
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