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Email [email protected] with resume to apply!

Location: Remote or Hybrid (Global)

Compensation: $250,000

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Position Overview

We’re seeking a Machine Learning Engineer to join our technical team and help build the next generation of data-driven tools that power our investment decision-making process. This role sits at the intersection of engineering, machine learning, and private markets investing, with a strong focus on data acquisition, model development, and deployment.

Key Responsibilities

• Develop, deploy, and maintain machine learning models to support investment sourcing, due diligence, portfolio monitoring, and risk analysis.
• Design systems to extract, normalize, and structure unstructured/private data, including deal documents, company profiles, founder bios, pitch decks, cap tables, and financials.
• Collaborate with data scientists and investment analysts to transform raw data into actionable signals.
• Build and maintain robust pipelines for alternative data ingestion, feature extraction, labeling, and model training.
• Implement models for tasks such as deal quality prediction, founder success modeling, market trend analysis, NLP on investor communications, and clustering of deal flow data.
• Ensure scalability, performance, and reliability of ML systems in production.
• Stay current with the latest research and tools in machine learning, NLP, and private markets data analysis.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field. PhD a plus.
• 3+ years of experience building and deploying ML models in a production environment.
• Strong proficiency in Python, including libraries such as scikit-learn, pandas, NumPy, PyTorch or TensorFlow.
• Experience with NLP and unstructured data (text extraction, embeddings, transformer models, LLM APIs, etc.).
• Familiarity with private equity, venture capital, or financial datasets is strongly preferred.
• Proficiency with data engineering tools: SQL, Airflow, Spark, or similar.
• Experience deploying models in cloud environments (AWS, GCP, or Azure).
• Strong understanding of ML best practices: data leakage, model validation, feature engineering, and interpretability.