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Internship Machine Learning Finance Jobs in Seattle, WA

PitchBook

Sr. Machine Learning Engineer

Seattle, WA · On-site

$118K - $163K/yr

PitchBook, a Morningstar company, is seeking a Senior Machine Learning Engineer to join their ... Prior exposure to fintech or financial data platforms is a strong advantage • Experience ...

Amazon

Senior Machine Learning Compiler Engineer

Seattle, WA · On-site

$139K - $183K/yr

The Product: Annapurna Labs Machine Learning accelerators are at the forefront of Amazon ... About the team BASIC QUALIFICATIONS - 5+ years of non-internship professional software development ...

Axon

Senior Machine Learning Engineer

Seattle, WA · On-site

$150K - $241K/yr

Reports to: Manager, Machine Learning Engineering * Collaborate with scientists and product ... Our benefits offer an array of options to help support you physically, financially and emotionally ...

Visa

Senior Director- Machine Learning

Bellevue, WA · On-site

... financial institutions and government entities across more than 200 countries and territories ... Experience with Generative AI and Machine Learning is essential. Prior background in payments and ...

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All JobsMachine Learning Finance JobsInternship Machine Learning Finance JobsInternship Machine Learning Finance Jobs in Seattle, WA

Internship Machine Learning Finance information

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How much do internship machine learning finance jobs pay per hour?

As of Aug 15, 2026, the average hourly pay for internship machine learning finance in Seattle, WA is $22.60, according to ZipRecruiter salary data. Most workers in this role earn between $19.42 and $25.43 per hour, depending on experience, location, and employer.

What is an internship machine learning finance?

Internship Machine Learning Finance positions are temporary roles where students or recent graduates work with financial organizations to apply machine learning techniques to solve finance-related problems. Interns may analyze large datasets, build predictive models, automate trading strategies, or detect fraud using machine learning algorithms. These internships provide hands-on experience in both finance and artificial intelligence, helping interns develop technical and industry-specific skills. They often require a background in programming, statistics, and a basic understanding of financial concepts.

What types of projects do interns typically work on in a machine learning finance internship?

As a Machine Learning Finance intern, you can expect to work on a variety of projects that blend quantitative analysis with practical financial applications. Common responsibilities include developing predictive models for stock prices or credit risk, analyzing large financial datasets, and building tools to automate trading strategies or detect fraud. Interns often collaborate closely with data scientists, software engineers, and finance professionals, gaining exposure to both technical and business aspects of the field. This hands-on experience is invaluable for building real-world skills and understanding the fast-paced finance environment.

What are the key skills and qualifications needed to thrive as an intern in machine learning finance, and why are they important?

To thrive as an Intern in Machine Learning Finance, you need a foundational understanding of statistics, programming (especially Python or R), and financial concepts, often supported by progress toward a quantitative degree. Familiarity with machine learning libraries (such as scikit-learn, TensorFlow, or PyTorch), data analysis tools, and version control systems like Git is typically expected. Strong analytical thinking, problem-solving abilities, and effective communication help you translate technical results into actionable financial insights. These skills are critical for developing robust models, supporting data-driven decision-making, and contributing meaningfully within interdisciplinary finance teams.

What is the difference between Internship Machine Learning Finance vs Data Analyst Intern?

AspectInternship Machine Learning FinanceData Analyst Intern
Required SkillsProgramming (Python, R), Machine Learning, Finance knowledgeData analysis, SQL, Excel, basic statistics
Work EnvironmentFinance firms, tech-driven finance teamsFinancial institutions, consulting firms, tech companies
Industry UsageFinance, Fintech, Quantitative researchFinance, marketing, consulting

Internship Machine Learning Finance focuses on applying machine learning techniques to financial data, requiring programming and finance knowledge. Data Analyst Internships involve analyzing data sets, creating reports, and using statistical tools. Both roles are common in finance-related industries but differ in technical focus and skill requirements.

What are popular job titles related to Internship Machine Learning Finance jobs in Seattle, WA?

For Internship Machine Learning Finance jobs in Seattle, WA, the most frequently searched job titles are:

What job categories do people searching Internship Machine Learning Finance jobs in Seattle, WA look for?

The top searched job categories for Internship Machine Learning Finance jobs in Seattle, WA are:

Internship Machine Learning Finance jobs near you
Infographic showing various Internship Machine Learning Finance job openings in Seattle, WA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $46,999 per year, or $22.6 per hour.

Sr. Machine Learning Engineer

PitchBook

Seattle, WA • On-site

$118K - $163K/yr

Full-time

Re-posted 1 hour ago

Job description

Job Summary:
PitchBook, a Morningstar company, is seeking a Senior Machine Learning Engineer to join their Product and Engineering team. The role involves delivering AI-powered features that extract insights from data, requiring expertise in machine learning, natural language processing, and collaboration with cross-functional teams.
Responsibilities:
• Deliver high-impact AI and ML capabilities that drive insight generation on the PitchBook Platform. Ensure your work contributes to broader business goals and is aligned with the team's strategic priorities
• Provide hands-on expertise in designing, building, and deploying AI/ML models and services with a focus on NLP, summarization, semantic search, classification, and prediction. Contribute to the development of scalable, high-performance systems that meet production-grade reliability and efficiency standards
• Support a culture of technical excellence by mentoring peers, sharing knowledge, and participating in code and design reviews. Promote innovation and continuous improvement through collaborative engineering practices
• Build and optimize models that leverage classifiers, transformers, LLMs, and other NLP techniques to generate meaningful insights from structured and unstructured data. Integrate these models into the broader AI/ML infrastructure in collaboration with partner teams
• Collaborate with engineering, product management, and data collection teams to ensure models are informed by high-quality data and support strategic product goals
• Explore and experiment with emerging technologies, methodologies, and tools in the fields of GenAI, NLP, and search. Translate research findings into practical solutions that enhance PitchBook’s AI capabilities
• Contribute to best practices in model transparency, monitoring, evaluation, and compliance. Help maintain high standards of security, data integrity, and responsible AI use across your projects
• Participate in the technical evaluation of candidates and help onboard new team members by contributing to documentation, pairing, and knowledge-sharing practices
• Apply principles from Agile, Lean, and Fast-Flow methodologies to support efficient model development and deployment cycles
• Support the vision and values of the company through role modeling and encouraging desired behaviors
• Participate in various company initiatives and projects as requested
Qualifications:
Required:
• Bachelor’s or advanced degree in Computer Science, Mathematics, Data Science, or a related technical field
• 6+ years of experience in software engineering or machine learning engineering, with a strong focus on AI/ML applications in insight generation, summarization, semantic search, and prediction
• Demonstrated expertise in natural language processing (NLP) and machine learning, including hands-on experience with classifiers, transformer models, large language models (LLMs), and widely used ML and 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
• Deep proficiency in building and maintaining scalable data pipelines and distributed systems using technologies such as Apache Kafka, Airflow, and cloud data platforms like Snowflake
• Strong programming skills in Python and SQL, with working knowledge of additional languages such as Java or Scala considered a plus
• Practical experience with cloud-native development, containerization, and orchestration technologies such as Docker and Kubernetes
• Demonstrated ability to solve complex technical problems, contribute to architectural decisions, and deliver high-performance, reliable solutions
• Excellent communication and collaboration skills, with experience working cross-functionally with product managers, engineers, and data scientists in globally distributed teams
• Must be authorized to work in the United States without the need for visa sponsorship now or in the future
Preferred:
• Advanced degree preferred
• Familiarity with the LangChain ecosystem, including tools such as LangSmith and LangGraph, and experience using them in production environments is a strong plus
• Experience working in fast-paced, data-driven environments. Prior exposure to fintech or financial data platforms is a strong advantage
• Experience authoring research papers for peer-reviewed AI/ML conferences (e.g., NeurIPS, ICML, ACL) and participating in the broader AI research community is strongly preferred
Company:
PitchBook offers financial data and tools on companies, deals, investors, and markets to support sales and business development. It is a sub-organization of Morningstar. Founded in 2007, the company is headquartered in Seattle, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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