[Hiring] Applied AI & Machine Learning Engineer @SPD Technology

Role Description

PitchBook’s Machine Learning Engineer is responsible for using machine learning, statistical modeling, and natural language processing (NLP) to collect a high volume of data for the PitchBook Platform and surface insights for the capital markets. This role will also serve as a resource and mentor for junior members of the team. A strong motivation to succeed is critical, and everyone has the opportunity to shape the long-term direction of our team.

About the project

PitchBook — a platform for investment professionals. Our software provides access to data and the analytical tools to get answers fast and discover promising opportunities. Uncovers actionable insights and trends hidden within the financial data of more than three million companies. Users all over the world include large corporations, start-ups, venture capital and private equity firms, investment banks, and many others.

• Features of PitchBook:
• Advanced search
• Discovery & insights
• Company profiles
• Workflow & efficiency
• Financials
• And many more.

Work Environment

• The role offers a flexible work schedule, allowing you to adapt your working hours with the requirement to attend all team meetings.
• The team follows a Scrum-based Agile methodology.

Technical Stack

• Python
• PyTorch
• Scikit-Learn
• Spacy
• LangChain
• LangGraph
• Etc.

As a qualified expert, You will

• Work with GenAI & LLMs: Design, develop, and fine-tune generative AI models using transformer architectures.
• Use Modern Toolkits: Leverage frameworks like LangChain, LangGraph, and LangSmith to build automated pipelines and intelligent agents.
• Extract Data Insights: Process and analyze large volumes of unstructured data using embeddings and statistical modeling to identify meaningful insights.
• Build Scalable Systems: Collaborate with cross-functional teams to create production-grade ML systems that handle real-time, high-volume data processing.
• Mentor & Lead: Act as a mentor for junior team members and stay updated on trends in Agentic AI, transformer models, and generative systems.
• Collaborate & Communicate: Work with Product and Engineering teams to improve data management and support innovation.

Tech Stack & Focus Areas

• Work with generative AI and transformer models, integrating frameworks like LangChain, LangGraph, and LangSmith to automate workflows and build intelligent systems.
• Use PyTorch, Scikit-Learn, and Spacy to build NLP pipelines.
• Implement vector databases for high-dimensional data embeddings and develop hybrid models to extract insights from diverse data sources.

Qualifications

• Educational Background: You hold a Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or a related field.
• Experience: You bring 4+ years of production experience in the ML field, with a track record in developing transformer-based models and deploying them in scalable systems.
• Technical Expertise: Proficient in Python and machine and deep learning frameworks like TensorFlow, PyTorch, ScikitLearn, Spacy, or others; experienced in natural language processing and modern LLM-based techniques; experience with full ML lifecycle, data preprocessing, training, evaluation stage, and monitoring techniques; skilled with SQL and data extraction methods.
• Innovative Thinker: You thrive on solving complex problems and are always exploring new trends in Generative AI.
• Collaborator & Communicator: You’re an excellent communicator able to collaborate and professionally express your thoughts to team members, other engineering teams, and stakeholders.
• Domain Knowledge: Exposure to financial services, investment banking, venture capital, or private equity is nice to have.
• Languages: English — Upper-Intermediate or higher; Ukrainian — fluent/native.

Benefits

• Reveal great tech solutions: Join the team of experts who create custom, cutting-edge tech solutions for world-renowned businesses, fueling clients' growth.
• Experience an agile and flexible working environment: Work fully remote around the globe, or in a hybrid work model from our office hubs.
• Embrace the opportunity for personal and professional growth: Take advantage of performance review and career path.
• Feel cared about: Be among like-minded people and feel supported with collaboration, open communication, and mutual respect.

Interview steps

• Pre-Screening with the recruiter
• Technical interview (up to 90 min)
• System Design interview (up to 45 min)
• Final interview with Engineering Manager (60 min)