Techstars seeks an experienced Machine Learning Engineer to join a growing team of investment professionals shaping our fund and investment strategy in support of ambitious entrepreneurs worldwide. As an integral member of the Techstars Capital team, the Machine Learning Engineer will be responsible for owning and delivering a suite of data and analytics solutions to support the firm's capital formation, investment strategy, fund management, and investor relations activities globally. 

We are looking for a machine learning engineer that is well-versed in all aspects of delivering and maintaining machine learning data products. This person must have a demonstrated experience building end-to-end machine learning solutions within industry environments - from ideation, to the acquisition/construction of relevant data from disparate internal and external sources, to the structuring of a reasonable hypothesis, to the iteration of model architectures, to the training, to the integration into a working system, and ongoing refinement of the thesis.

Duties and Responsibilities

• Software Engineering: Deep experience and understanding of all aspects of delivering and maintaining data-driven machine learning solutions within a quantitative investment environment.
• Data preparation: Engineer robust data pipelines that automate the collection, aggregation, cleaning, validation, and testing of structured and unstructured datasets from a variety of public and private sources;
• Data analysis: Engineer well-structured systematic model architectures composed of quantitative, statistical, machine learning, and deep learning models.
• Data & model narratives: Design clear, actionable, data-driven insights in support of the firm's capital formation, investment strategy, fund management, and investor relations activities.
• Communication: Clearly communicate, document, and iterate objectives with an interdisciplinary team of professionals on the Capital team; collaborate with key partners across the business (Accelerator Investments, Engineering, Portfolio Services, Finance, Legal, Marketing, Sales, Executive);
• Mentor and lead junior staff

Qualifications

• 5+ years of work experience in a quantitative machine learning engineering or research engineering role;
• Demonstrated experience moving legacy analysis from Excel/Google Sheets to API-driven packaged tooling;
• Expert-level programming experience in Python, C, C++, Bash, and SQL;
• Demonstrated experience writing scrapers in Python;
• Understanding of scraper deployment infrastructure (e.g. Proxies, tooling, etc);
• Experience with code/container orchestration (e.g. Prefect, Luigi, Dagster, etc);
• Experience with parallel programming and custom parallel backends (e.g. joblib, Ray, Dask, Cloud-based parallel code deployment, etc);
• Experience with Docker, Docker orchestration, and cloud deployment;
• Experience with API design and deployment (e.g. FastAPI, Cloud-based API generators);
• Experience with full-stack dashboard development, and Python dashboard packages (e.g. Panel, Streamlit, Dash, etc.);
• Demonstrated experience working with private markets data (e.g. Pitchbook, Crunchbase, CapitalIQ, Preqin, Burgiss, etc.);
• Demonstrated affinity for data analytics and machine learning;
• Intellectual curiosity, and a desire to constantly learn and grow;
• The ability to work both independently and within a team; possess a congenial disposition, an entrepreneurial spirit, and have comfort in an informal work environment;
• A conviction that entrepreneurs create a better future and a desire to support our mission to help entrepreneurs succeed.

Compensation range: $125,000 - $175,000 + 10% Bonus 

US Benefits