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About This Role
At PitchBook, a Morningstar company, we are always looking forward. We continue to innovate, evolve, and invest in ourselves to bring out the best in everyone. We're deeply collaborative and thrive on the excitement, energy, and fun that reverberates throughout the company.
Our extensive learning programs and mentorship opportunities help us create a culture of curiosity that pushes us to always find new solutions and better ways of doing things. The combination of a rapidly evolving industry and our high ambitions means there's going to be some ambiguity along the way, but we excel when we challenge ourselves. We're willing to take risks, fail fast, and do it all over again in the pursuit of excellence.
If you have a good attitude and are willing to roll up your sleeves to get things done, PitchBook is the place for you.
About the Role:
As a member of the Product and Engineering team at PitchBook, you will be part of a team of big thinkers, innovators, and problem solvers who strive to deepen the positive impact we have on our customers and our company every day. We value curiosity and the drive to find better ways of doing things. We thrive on customer empathy, which remains our focus when creating excellent customer experiences through product innovation.
We know that greatness is achieved through collaboration and diverse points of view, so we work closely with partners around the globe. As a team, we assume positive intent in each other's words and actions, value constructive discussions, and foster a respectful working environment built on integrity, growth, and business value. We invest heavily in our people, who are eager to learn and constantly improve. Join our team and grow with us!
As a Senior Machine Learning Engineer (MLE) on the AI & ML (Insights) team, you will play a critical role in delivering AI-powered features that extract meaningful insights from PitchBook's wealth of structured and unstructured data including reports, news, and other textual content. This role requires deep technical expertise in advanced data analytics and machine learning, as well as a hands-on approach to designing, building, and optimizing ML solutions that power user-facing features on the PitchBook Platform.
You will be deeply involved in the end-to-end development and operationalization of ML models, including their architecture, training, deployment, and ongoing maintenance. Your focus will span across natural language processing (NLP), generative AI (GenAI), large language models (LLMs), and scalable data systems. You will be expected to tackle complex technical challenges, contribute to architectural decisions, and collaborate closely with other engineers, data scientists, and product managers to ensure that your work aligns with business goals and AI/ML strategy.
Your contributions will help unlock unique value for PitchBook customers by improving the speed, discoverability, quality, and quantity of insights available on the platform. This includes developing models that can infer meaning and structure from millions of discrete data sources, and applying ML to enrich our datasets with predictive and generative intelligence. As a senior engineer, you will take ownership of key technical components and ensure that our systems meet the highest standards of performance, reliability, and security.
Primary Job 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
Basic Qualifications:
- Bachelor's in Computer Science, Mathematics, or Data Science
- 6 years minimum 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
- Must be authorized to work in the United States without the need for visa sponsorship now or in the future
Preferred Qualifications:* 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
- Familiarity with the LangChain ecosystem, including tools such as LangSmith and LangGraph, and experience using them in production environments is a strong plus
- 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
- 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
Benefits + Compensation at PitchBook:
Physical Health
- Comprehensive health benefits
- Additional medical wellness incentives
- STD, LTD, AD&D, and life insurance
Emotional Health
- Paid sabbatical program after four years
- Paid family and paternity leave
- Annual educational stipend
- Ability to apply for tuition reimbursement
- CFA exam stipend
- Robust training programs on industry and soft skills
- Employee assistance program
- Generous allotment of vacation days, sick days, and volunteer days
Social Health
- Matching gifts program
- Employee resource groups
- Subsidized emergency childcare
- Dependent Care FSA
- Company-wide events
- Employee referral bonus program
- Quarterly team building events
Financial Health
- 401k match
- Shared ownership employee stock program
- Monthly transportation stipend
- Please be aware the above PitchBook benefit and perk offerings are subject to corresponding plan and policy documents and may change during the course of your employment.
Compensation
- Annual base salary: $170,000-$240,000
- Target annual bonus percentage: 10%
Working Conditions:
At the heart of our company is a belief in the power of in-person collaboration. Being together in the office fuels our creativity, strengthens our connections, and drives the innovation that sets us apart. Our culture is built on spontaneous moments—those hallway conversations, whiteboard brainstorms, and shared celebrations in each of our global offices—that simply can't be replicated remotely. This role is expected to be in the office 5 days a week.
The job conditions for this position are in a standard office setting. Employees in this position use PC and phone on an on-going basis throughout the day. Limited corporate travel may be required to remote offices or other business meetings and events.
We are excited to get to know you and your background. Concerned that you might not meet every requirement? We encourage you to still apply as you might be the right candidate for the role or other roles at PitchBook.
#LI-TK1
#LI-Onsite
Salary Context
This $170K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At PitchBook, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $170K to $240K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
PitchBook AI Hiring
PitchBook has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $180K - $240K.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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