Machine Learning Engineer

PitchBook Data•Seattle, WA

7h•$125,000 - $180,000•Onsite

About The Position

As a Machine Learning Engineer on PitchBook’s AI & ML (Insights) team, you will develop AI-powered features that extract insights from structured and unstructured data, including reports, news, and other textual content. You will design, build, train, deploy, optimize, and maintain machine learning models and services used in customer-facing features on the PitchBook Platform. The work spans NLP, generative AI, large language models, and scalable data systems, with a focus on improving the speed, discoverability, quality, and quantity of customer insights. You will address complex technical challenges, contribute to architecture decisions, and work with engineers, data scientists, product managers, and data collection teams to align solutions with product goals and AI/ML strategy. The role also involves translating research and emerging technologies into practical solutions, integrating models into AI/ML infrastructure, and supporting production-grade reliability, efficiency, security, data integrity, transparency, monitoring, evaluation, compliance, and responsible AI practices. The engineer contributes to technical excellence through knowledge sharing, code and design reviews, situational guidance to junior engineers, candidate evaluation, onboarding, and documentation. The position is expected to be in the office five days a week in a standard office setting, with ongoing use of a PC and phone. Limited travel to remote offices or business meetings and events may be required.

Requirements

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

Nice To Haves

Hands-on expertise in NLP and machine learning, 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, and experience using these tools in production.
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; working knowledge of Java or Scala is a plus.
Practical experience with cloud-native development, containerization, and orchestration technologies such as Docker and Kubernetes.
Ability to solve complex technical problems, contribute to architecture decisions, and deliver high-performance, reliable solutions.
Strong communication and collaboration skills, including cross-functional work with product managers, engineers, and data scientists in globally distributed teams.
Experience in fast-paced, data-driven environments; fintech or financial data platform experience is an advantage.
Experience authoring research papers for peer-reviewed AI/ML conferences such as NeurIPS, ICML, or ACL and participating in the AI research community.

Responsibilities

Deliver AI and ML capabilities that support insight generation on the PitchBook Platform and align with business goals and team priorities.
Design, build, and deploy AI/ML models and services, particularly for NLP, summarization, semantic search, classification, and prediction.
Contribute to scalable, high-performance systems that meet production reliability and efficiency standards.
Build and optimize models using classifiers, transformers, LLMs, and other NLP techniques to extract insights from structured and unstructured data.
Integrate models into broader AI/ML infrastructure in collaboration with partner teams.
Collaborate with engineering, product management, and data collection teams to ensure models use high-quality data and support product goals.
Explore emerging technologies, methods, and tools in GenAI, NLP, and search, and translate research into practical solutions.
Contribute to model transparency, monitoring, evaluation, and compliance practices, and uphold security, data integrity, and responsible AI standards.
Share knowledge, pair with teammates, participate in code and design reviews, provide situational guidance to junior engineers, and contribute to team best practices.
Participate in technical candidate evaluations and help onboard team members through documentation, pairing, and knowledge sharing.
Apply Agile, Lean, and Fast-Flow principles to support efficient model development and deployment.
Support company vision and values by role modeling desired behaviors.
Participate in company initiatives and projects as requested.