Sr. Machine Learning Engineer

PitchBook•Seattle, WA

7d•Onsite

About The Position

As a Senior Machine Learning Engineer on PitchBook’s AI & ML (Insights) team, you will develop AI-powered features that extract insights from PitchBook’s structured and unstructured data, including reports and news. You will design, build, deploy, and maintain machine-learning models and services spanning NLP, generative AI, large language models, and scalable data systems. The work supports user-facing features on the PitchBook Platform and aims to improve the speed, discoverability, quality, and quantity of insights available to customers. You will own key technical components, contribute to architecture decisions, and help ensure systems meet high standards for performance, reliability, security, data integrity, and responsible AI. You will collaborate with engineering, data science, product management, and data collection teams to align solutions with customer and business goals, while mentoring peers and supporting the development of the team. This role is expected to be in the office five days per week in a standard office setting. Employees use a PC and phone throughout the day. Limited corporate 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 6 years of experience in software engineering or machine-learning engineering, focused on AI/ML applications such as insight generation, summarization, semantic search, and prediction.
Must be authorized 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.
Proficiency building and maintaining scalable data pipelines and distributed systems with technologies such as 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 architectural decisions, and deliver high-performance, reliable solutions.
Excellent 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.
Research papers authored for peer-reviewed AI/ML conferences such as NeurIPS, ICML, or ACL, and participation in the broader AI research community.

Responsibilities

Deliver AI and machine-learning capabilities that generate insights on the PitchBook Platform and align work with business goals and team priorities.
Design, build, and deploy AI/ML models and services focused on NLP, summarization, semantic search, classification, and prediction.
Build and optimize models using classifiers, transformers, LLMs, and other NLP techniques to derive insights from structured and unstructured data.
Integrate models into the broader AI/ML infrastructure in collaboration with partner teams.
Contribute to scalable, high-performance systems that meet production reliability and efficiency standards.
Collaborate with engineering, product management, and data collection teams to ensure models use high-quality data and support product goals.
Explore emerging technologies, methodologies, and tools in GenAI, NLP, and search, translating research findings into practical solutions.
Contribute to best practices for model transparency, monitoring, evaluation, compliance, security, data integrity, and responsible AI.
Mentor peers, share knowledge, and participate in code and design reviews.
Participate in 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 values and initiatives, and take part in other projects as requested.