Machine Learning Engineer
PitchBook Data1 day ago
Seattle, WA, USAMid Level
Base Salary
$125k - $180k/yr
Responsibilities
- Design, build, deploy, and maintain AI/ML models and services for insight generation on the PitchBook Platform.
- Develop solutions for NLP, summarization, semantic search, classification, prediction, and generative AI.
- Build and optimize models using classifiers, transformers, LLMs, and other NLP techniques.
- Integrate models into broader AI/ML infrastructure and scalable production systems.
- Collaborate with engineering, product management, data collection teams, data scientists, and other partners.
- Contribute to architecture decisions, technical excellence, code and design reviews, documentation, and team best practices.
- Explore emerging GenAI, NLP, and search technologies and translate research into practical solutions.
- Support model transparency, monitoring, evaluation, security, data integrity, compliance, and responsible AI practices.
- Help evaluate candidates, onboard team members, and provide situational guidance to junior engineers.
- Apply Agile, Lean, and Fast-Flow principles to model development and deployment cycles.
Requirements
- Bachelor’s degree in Computer Science, Mathematics, or Data Science.
- At least 2 years of software engineering or machine learning engineering experience focused on AI/ML applications in insight generation, summarization, semantic search, and prediction.
- Authorization to work in the United States without current or future visa sponsorship.
- Hands-on experience with NLP, classifiers, transformer models, LLMs, and ML/data science libraries such as scikit-learn, pandas, NumPy, TensorFlow, and PyTorch.
- Experience delivering production-grade GenAI or LLM-based systems with measurable business impact.
- Experience with LangChain, LangSmith, and LangGraph is preferred.
- Experience building scalable data pipelines and distributed systems with Apache Kafka, Airflow, and Snowflake is preferred.
- Strong Python and SQL skills; Java or Scala experience is a plus.
- Experience with Docker, Kubernetes, cloud-native development, containerization, and orchestration.
- Ability to solve complex technical problems, contribute to architecture decisions, and deliver reliable high-performance solutions.
- Experience collaborating cross-functionally in globally distributed, data-driven environments.
- Fintech or financial data platform experience is advantageous.
- Peer-reviewed AI/ML conference research publications and participation in the AI research community are strongly preferred.
Benefits
- Comprehensive health benefits, medical wellness incentives, short- and long-term disability, accidental death and dismemberment, and life insurance.
- Paid sabbatical after four years, paid family and paternity leave, educational stipend, tuition reimbursement, CFA exam stipend, training programs, and employee assistance program.
- Vacation, sick, and volunteer days; matching gifts; employee resource groups; subsidized emergency childcare; Dependent Care FSA; company events; referral bonuses; and team-building events.
- 401(k) match, shared ownership employee stock program, and monthly transportation stipend.
- Expected to work in the office five days per week in a standard office setting; limited corporate travel may be required.
Tech Stack
Apache AirflowApache KafkaDockerJavaKubernetesNumPyPandasPythonPyTorchScalascikit-learnSnowflakeSQLTensorFlow
Categories
About PitchBook Data
PitchBook Data builds a subscription platform, datasets, and research that track private and public capital markets for investors, bankers, and corporate development teams. Its products include the PitchBook Platform and API for company, deal, fund, and limited-partner data, plus analyst reports and benchmarks. Founded in 2007 and headquartered in Seattle, it operates as a Morningstar company.