Machine Learning Engineer

The Machine Learning Engineer will work on PitchBook’s AI & ML (Insights) team, building AI-powered capabilities that turn structured and unstructured data into actionable insights across the PitchBook Platform.

Role Focus

This position centers on end-to-end machine learning model development and operationalization. You will contribute to NLP, generative AI, and LLM-based solutions used within the PitchBook Platform to support insight generation and broader business goals.

Responsibilities

• Deliver AI and ML capabilities that generate insights on the PitchBook Platform, ensuring alignment with team strategy and business priorities.
• Design, build, and deploy AI/ML models and services with emphasis on NLP workflows including summarization, semantic search, classification, and prediction.
• Develop scalable, high-performance systems that meet production-grade reliability and efficiency requirements.
• Strengthen technical execution through knowledge sharing, pairing, and active participation in code and design reviews.
• Provide situational guidance to junior engineers and contribute to team best practices.
• Build and optimize models using classifiers, transformers, LLMs, and other NLP techniques to derive insights from structured and unstructured sources.
• Integrate models into the broader AI/ML infrastructure in collaboration with partner teams.
• Work with engineering, product management, and data collection teams to ensure models are supported by high-quality data and map to strategic product goals.
• Experiment with emerging methods and tools in GenAI, NLP, and search, translating research into practical improvements to PitchBook’s AI capabilities.
• Contribute to best practices for model transparency, monitoring, evaluation, and compliance, while supporting security, data integrity, and responsible AI standards.
• Participate in technical evaluation of candidates and support onboarding through documentation, pairing, and knowledge-sharing.
• Apply Agile, Lean, and Fast-Flow principles to support efficient model development and deployment cycles.
• Support company vision and values through role modeling and encouraging desired behaviors.
• Participate in additional company initiatives and projects as requested.

Required Qualifications

• Bachelor’s degree in Computer Science, Mathematics, or Data Science.
• 2+ years of experience in software engineering or machine learning engineering, with a focus on AI/ML applications for insight generation, summarization, semantic search, and prediction.
• Authorization to work in the United States without visa sponsorship now or in the future.

Technologies

Python, SQL, Java, Scala, scikit-learn, pandas, numpy, TensorFlow, PyTorch, LangChain, LangSmith, LangGraph, Apache Kafka, Airflow, Snowflake, Docker, Kubernetes, NLP, GenAI, LLMs

Preferred Qualifications

• Hands-on NLP and machine learning experience, including classifiers, transformer models, LLMs, and libraries such as scikit-learn, pandas, numpy, TensorFlow, and PyTorch.
• Experience delivering production-grade GenAI or LLM-based systems with measurable business impact.
• Familiarity with the LangChain ecosystem, including LangSmith and LangGraph, with production experience using these tools.
• Experience building and maintaining scalable data pipelines and distributed systems using Apache Kafka, Airflow, and cloud data platforms such as Snowflake.
• Strong Python and SQL skills; knowledge of Java or Scala is a plus.
• Experience with cloud-native development, containerization, and orchestration using Docker and Kubernetes.
• Ability to solve complex technical problems, influence architecture decisions, and deliver high-performance, reliable solutions.
• Cross-functional communication and collaboration experience with product managers, engineers, and data scientists in globally distributed teams.
• Experience in fast-paced, data-driven environments, with fintech or financial data platform exposure as a strong advantage.
• Experience authoring research papers for peer-reviewed AI/ML conferences (NeurIPS, ICML, ACL) and participating in the broader AI research community.

Compensation and Location

Location: Seattle, WA (onsite)

Salary: USD 125,000 to 180,000 per year

Annual base salary: $125,000-$180,000

Target annual bonus percentage: 10%

Benefits

• Comprehensive health benefits
• Additional medical wellness incentives
• STD, LTD, AD&D, and life insurance
• Paid sabbatical program after four years
• Paid family and paternity leave
• Annual educational stipend
• Tuition reimbursement option
• CFA exam stipend
• Robust training programs on industry and soft skills
• Employee assistance program
• Generous allotment of vacation days, sick days, and volunteer days
• Matching gifts program
• Employee resource groups
• Subsidized emergency childcare
• Dependent Care FSA
• Company-wide events
• Employee referral bonus program
• Quarterly team building events
• 401k match
• Shared ownership employee stock program
• Monthly transportation stipend

Working Conditions

• Expected in the office 5 days a week.
• Standard office setting.
• Employees use PC and phone throughout the day.
• Limited corporate travel may be required to remote offices or other business meetings and events.