Machine Learning Engineer

About the role

At PitchBook, we are dedicated to fostering innovation and continuous improvement within a collaborative environment that values creativity and teamwork. Our goal is to develop cutting-edge AI solutions that enhance our platform's capabilities and deliver valuable insights to our users. As a Machine Learning Engineer, you will be an integral part of our AI & ML team, working on developing, deploying, and maintaining advanced machine learning models that support various features and functionalities. Your work will directly impact how our clients access and interpret financial and market data, enabling smarter decision-making. You will collaborate closely with product managers, data scientists, and software engineers to ensure the successful integration of AI-driven features into our platform, contributing to the company's mission of providing comprehensive and actionable data insights.

Key facts

What you'll do

• Develop, implement, and optimize AI and machine learning functionalities that generate insights on the PitchBook Platform, ensuring they align with the strategic objectives of the team and the company.
• Design, build, and deploy scalable AI/ML models and services, particularly focusing on natural language processing, summarization, semantic search, and predictive analytics to enhance data analysis capabilities.
• Promote a culture of technical excellence by sharing your expertise through code reviews, mentoring junior engineers, and contributing to best practices within the team.
• Create, train, and refine models using classifiers, transformer architectures, and large language models (LLMs) to extract meaningful insights from diverse data sources, ensuring these models are integrated seamlessly into the existing AI/ML framework.
• Collaborate with cross-functional teams, including data engineers, product managers, and data scientists, to ensure models are built on high-quality data and meet the needs of various product features.
• Investigate and apply emerging technologies and methodologies in Generative AI (GenAI) and natural language processing to continuously improve PitchBook's AI capabilities.
• Maintain best practices for model transparency, monitoring, and compliance, ensuring high standards of data integrity, model explainability, and responsible AI use.
• Participate in the recruitment process by evaluating potential candidates, conducting technical interviews, and assisting in onboarding new team members to build a strong, collaborative team.
• Apply Agile and Lean development methodologies to streamline the model development lifecycle, from experimentation to deployment and ongoing maintenance.
• Embody and promote the company's vision and core values through your actions, fostering a positive and innovative team environment.
• Engage in various company initiatives, projects, and cross-departmental collaborations as needed to support overall business goals and technological advancements.
• Document technical designs, model architectures, and best practices to ensure knowledge sharing and maintainability of AI solutions.
• Monitor and evaluate model performance in production, implementing improvements and updates to ensure sustained accuracy and relevance of insights.
• Work within a fast-paced, data-driven environment, adapting to evolving priorities and technological trends to keep PitchBook at the forefront of AI innovation.
• Support the integration of AI features into the platform, collaborating with software engineers to ensure smooth deployment and user experience.
• Contribute to the development of internal tools and frameworks that facilitate AI model development, testing, and deployment.
• Engage with industry research, conferences, and publications to stay current with advancements in AI, NLP, and related fields, bringing innovative ideas into the team.
• Ensure compliance with relevant data privacy and security standards, adhering to company policies and legal requirements.
• Foster a collaborative environment by sharing knowledge, participating in team discussions, and supporting continuous learning initiatives.

Requirements

• Bachelor's degree in Computer Science, Mathematics, Data Science, or a related technical field; advanced degrees such as Master's or PhD are preferred.
• Minimum of 2 years of professional experience in software engineering or machine learning engineering, with a focus on AI/ML applications related to insight generation, prediction, or natural language processing.
• Proven expertise in natural language processing and machine learning, with hands-on experience developing models using classifiers, transformer architectures, and large language models (LLMs).
• Demonstrated experience in delivering production-ready AI systems, particularly GenAI or large language model-based solutions, that have achieved measurable business outcomes.
• Familiarity with the LangChain ecosystem, including tools like LangSmith and LangGraph, is advantageous but not mandatory.
• Strong background in building scalable data pipelines and distributed systems, with experience using technologies such as Apache Kafka, Airflow, and cloud data platforms like Snowflake.
• Proficiency in programming languages including Python and SQL; knowledge of Java or Scala is a plus.
• Experience working with cloud-native development, containerization, and orchestration tools such as Docker and Kubernetes.
• Ability to analyze complex technical challenges, contribute to architectural decisions, and implement scalable solutions.
• Excellent communication skills, with the ability to work effectively within globally distributed teams and explain technical concepts to non-technical stakeholders.
• Experience working in fast-paced, data-driven environments, especially within the fintech or financial data platform sectors, is highly desirable.
• A track record of publishing research papers for peer-reviewed AI/ML conferences is a plus, demonstrating engagement with the broader AI community.
• Knowledge of data privacy, security standards, and compliance requirements relevant to financial data is beneficial.
• Strong problem-solving skills and a proactive attitude toward learning and innovation.

Nice to have

• Experience with cloud-native development, including deploying AI models on cloud platforms such as AWS, GCP, or Azure.
• Familiarity with container orchestration tools like Kubernetes and containerization technologies such as Docker.
• Background in fintech or financial data platforms, providing context for the specific challenges and opportunities in this industry.
• Experience with model explainability, interpretability, and responsible AI practices.
• Knowledge of additional programming languages such as Java or Scala.
• Participation in AI or ML research communities, conferences, or peer-reviewed publications.

Skills & tools

• Python, SQL, Java, Scala (preferred)
• TensorFlow, PyTorch, scikit-learn, pandas, numpy
• Natural language processing techniques, transformers, large language models (LLMs)
• Data pipeline tools: Apache Kafka, Airflow
• Cloud platforms: Snowflake, AWS, GCP, Azure (experience with cloud-native development)
• Containerization and orchestration: Docker, Kubernetes
• LangChain ecosystem: LangSmith, LangGraph (advantageous but not mandatory)
• Version control: Git
• Agile and Lean methodologies for software development and deployment

Practical notes

This position is open to candidates who require visa sponsorship. We are committed to providing a competitive benefits package and a supportive work environment that encourages growth and innovation. The application process will remain open until the position is filled, and we encourage interested candidates to apply early. The role involves working on-site at our Seattle office, with collaboration across teams in a fast-paced, data-driven environment. Candidates should be prepared to participate in technical interviews, coding assessments, and team discussions. We value diversity and inclusion and strive to create an environment where all team members can thrive and contribute to our shared success.