?Machine Learning Engineer

?Overview Of 73 Strings

?73 Strings is an innovative platform providing comprehensive data extraction, monitoring, and valuation solutions for the private capital industry. The companys AI-powered platform streamlines middle-office processes for alternative investments, enabling seamless data structuring and standardization, monitoring, and fair value estimation at the click of a button. 73 Strings serves clients globally across various strategies, including Private Equity, Growth Equity, Venture Capital, Infrastructure and Private Credit.

Our 2025 $55M Series B, the largest in the industry, was led by Goldman Sachs, with participation from Golub Capital and Hamilton Lane, with continued support from Blackstone, Fidelity International Strategic Ventures and Broadhaven Ventures.

?Role And Background

?As a Senior Machine Learning Engineer, you will lead the development of advanced models for document intelligence, focusing on information extraction, document understanding, and intelligent search capabilities. Your work will directly influence our ability to derive insights from unstructured data.

?Responsibilities

• ?Architect and implement machine learning models to automate structured and unstructured data extraction from financial documents (e. g. , contracts, balance sheets).

• ?Develop natural language processing (NLP) solutions to enhance qualitative document understanding and improve decision-making.

• ?Optimize algorithms for scalability and real-time performance across cloud-based platforms.

• ?Collaborate with cross-functional teams (finance experts, product managers) to align technical solutions with business objectives.

• ?Monitor deployed models to ensure accuracy, efficiency, and adaptability to changing market dynamics.

• ?Conduct experiments with state-of-the-art AI techniques to refine model performance and explore innovative applications.

?Core Competencies

? Programming & Development

• ? Python Mastery: Craft elegant, production-ready code that automates financial data processing.

• ? ML Framework Proficiency: Leverage PyTorch and TensorFlow to build sophisticated data extraction models.

• ? API Development: Design and implement robust APIs that integrate with financial platforms and data sources.

• ? Engineering Excellence: Apply Git version control and CI/CD practices to ensure code quality.

? Computer Vision

• ?Extract financial data from diverse document formats (PDF, Excel, PowerPoint) regardless of layout.

• ?Work with OCR systems that accurately capture financial figures and text

• ?Develop models that can extract tabular data from charts (bar, pie, etc. ) in financial presentations.

• ?Create table structure recognition systems that understand complex financial statement layouts.

• ?Design document classification systems for organizing diverse financial document types.

? Natural Language Processing

• ?Build generative AI systems for qualitative understanding of financial documents.

• ?Implement text classification and named entity recognition for financial metrics identification.

• ?Create systems that automatically detect dates, periods, currencies, and units in extracted data.

• ?Develop question-answering capabilities that enable chat interfaces with structured financial data.

• ?Build semantic search functionality for financial research and competitive intelligence.

? Data Engineering & MLOps

• ?Design database schemas that efficiently store structured financial data

• ?Architect pipelines that integrate data from diverse sources (documents, web scraping, third-party APIs)

• ?Deploy and monitor models in production environments for critical financial applications

• ?Leverage cloud platforms (AWS/Azure/GCP) for scalable financial data processing.

?Qualifications

• ?Masters degree in computer science, Data Science, or related field (or equivalent practical experience)

• ?Deep software engineering expertise including design patterns, code optimization, and testing best practices

• ?Python programming excellence with a focus on production-quality code

• ?Experience with Git-based workflows and collaborative development

• ?MLOps capabilities including CI/CD for ML models, workflow automation, and production monitoring

• ?Cloud platform experience (Azure, AWS, Google Cloud) and containerization expertise (Docker, Kubernetes)

• ?Data engineering proficiency with tools like Apache Spark, Airflow, or Databricks (preferred)

• ?Visualization skills using Streamlit, Tableau, or similar tools

• ?Knowledge of data privacy, model governance, and financial regulatory requirements

• ?Strong theoretical and practical understanding of Machine and Deep Learning principles

• ?NLP expertise covering text processing, tokenization, language models, and advanced techniques

• ?Computer Vision fundamentals for document image analysis and chart data extraction

• ?Hands-on experience with ML libraries: NumPy, PyTorch, HuggingFace, OpenCV, scikit-learn, spaCy, NLTK

• ?Experience with vector databases, good understanding of LLMs and Prompt Engineering, and knowledge of frameworks like LangChain

• ?API development using Flask, FastAPI, Django or similar frameworks

• ?Good communication skills for explaining complex concepts to diverse audiences

• ?Self-direction and organizational abilities to manage multiple concurrent projects

• ?Passion for continuous learning in the rapidly evolving ML/DL landscape

• ?Open-source contributions or maintenance experience (preferred)

?About 73 Strings

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?Machine Learning Engineer

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