About the Role
SPD Technology is looking for a Middle ML & AI Engineer to join the team. 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 the team.
PitchBook is a platform for investment professionals. The software provides access to data and analytical tools to get answers fast and discover promising opportunities, uncovering actionable insights and trends hidden within the financial data of more than three million companies. Users worldwide include large corporations, start-ups, venture capital and private equity firms, investment banks, and many others. Features of PitchBook include: Advanced search, Discovery & insights, Company profiles, Workflow & efficiency, Financials, and more.
Team: Engineering Manager, 6 MLEs.
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.
Responsibilities
•Work with GenAI & LLMs: Design, develop, and fine-tune generative AI models using transformer architectures. Implement advanced techniques to create practical solutions for real-world challenges.
•Use Modern Toolkits: Leverage frameworks like LangChain, LangGraph, and LangSmith to build automated pipelines and intelligent agents. Integrate these tools with vector databases for efficient similarity search and real-time analysis.
•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. Present complex analyses clearly and effectively.
•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.
Requirements
•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.
Work Conditions
Work fully remote around the globe, or in a hybrid work model from office hubs equipped with everything needed for comfortable work. Enjoy a flexible working schedule and create work and rest balance for yourself. A stable workload and a stable income, focusing on lasting cooperation and high-performance culture. Opportunity for personal and professional growth, performance review and career path, mentorship program, personal development plan, and individual learnings through the corporate library, public speaking support, and more. Be among like-minded people with collaboration, open communication and mutual respect. Join company-wide tech and cultural events, and contribute to meaningful CSR initiatives.
Interview Steps
1.Pre-Screening with the recruiter
2.Technical interview (up to 90 min)
3.System Design interview (up to 45 min)
4.Final interview with Engineering Manager (60 min)
