Interested in this AI/ML Engineer role at Freestone Capital Management?
Apply Now →Skills & Technologies
About This Role
Who We Are
At Freestone, we believe in making a meaningful difference by focusing on what truly matters to our clients. With a 26\-year history of consistent growth and success, we are an independent wealth and asset management firm with over $14 billion in assets under management. Headquartered in Washington, with a team of over 120 professionals, we are dedicated to delivering a highly personalized experience that combines intelligent, thoughtful advice with unique investment strategies and comprehensive financial planning.
About the Role
We are seeking an AI \& Automation Engineer to join Freestone's Technology and Operations team in Seattle. Your mission is to connect the firm's systems and automate the manual, document\-heavy work that runs across them, building integrations between our CRM, Microsoft 365, e\-signature, custodial and banking systems, reporting, and using AI to automate the workflows that ride on top of them.
This is a hands\-on role at the intersection of systems integration and applied AI. You will build and maintain reliable connectors over REST APIs and webhooks and use large language models, primarily through tools such as Claude, to automate work that has resisted traditional automation. To be successful these builds require strong verification, appropriate human review, and full auditability, within the firm's security and compliance framework.
Key Responsibilities
- Build and maintain integrations between the firm's core systems CRM, Microsoft 365 and SharePoint, e\-signature, custodial and banking platforms, and reporting, using REST APIs, webhooks, and related interfaces.
- Use AI tools such as Claude to automate document\-heavy workflows, including extracting information from signed forms, populating and processing PDFs.
- Partner across departments to identify manual workflows across the firm and deliver automated solutions that save time and reduce error.
- Deploy and maintain automations with strong validation, human\-in\-the\-loop review, monitoring, and audit trails, within the firm's data\-governance, security, and compliance framework.
- Work with the firm's software engineering team to build on existing systems and help set standards for how Freestone applies AI and automation.
Qualifications and Skills
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related field, or equivalent professional experience.
- 5\+ years of software engineering, integration, or automation experience, ideally in financial services or another regulated industry.
- Proficiency in Python and/or C\#/.NET.
- Strong systems\-integration experience building connectors over REST APIs, webhooks, and OAuth, including workflow automation tools such as Power Automate.
- Experience integrating with core business systems, including:
+ CRM platforms
+ Microsoft 365, including the Graph API, SharePoint, and OneDrive
+ E\-signature platforms such as DocuSign
+ Custodial, banking, and other financial vendor APIs
- Strong systems\-integration experience building connectors over REST APIs, webhooks, and OAuth, including workflow automation tools such as Power Automate, such as Claude, including prompt engineering, structured outputs, document extraction, and retrieval\-augmented generation, with sound judgment about building in verification and human review where accuracy matters.
- Solid data fundamentals, including SQL.
- A strong security and data\-governance mindset for handling sensitive client and financial information.
- Strong analytical and process\-design skills, able to map a manual workflow end to end and reimagine it as a reliable, automated process.
- Enjoys fast\-paced work environments and having ownership over projects.
- A keen eye for detail; efficient and highly productive.
- Flexible and level\-headed, with strong organizational and project management skills.
Why Join Us?
- The expected annual base salary for this position is $130,000–$185,000, depending on experience and skill set. In addition, the position is eligible for a discretionary bonus based on company and individual performance.
- Comprehensive benefits include medical, dental, vision, and prescription coverage, 401(k) matching, life and disability insurance, long\-term care, parental leave, accidental death insurance, and flexible spending.
- Generous paid time off \- 17 days of PTO to start, 10 paid holidays, and Summer Fridays.
- Meal perks, company events, team celebrations, and more.
- A collaborative, fun, and supportive culture where you're encouraged to innovate and grow.
Our Core Values
- Commitment: Passion, integrity, and energy in every action.
- Collaboration: Together, we are stronger, smarter, and more innovative.
- Continuous Improvement: Dedicated to growth—for clients and ourselves.
*Freestone is an equal opportunity employer. All candidates will be recruited and, if applicable, selected and employed without regard to sex, race, religion, marital status, veteran status, age, national origin, sexual orientation, gender identity, color, creed, ancestry, disability, genetic information or any other basis prohibited by law.*
*We are not accepting unsolicited resumes from agencies and/or search firms for this job posting.*
*For information about our privacy practices, including disclosures for California residents, please see our* *Privacy Notices**.*
Salary Context
This $130K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Freestone Capital Management, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($157K) sits 13% below the category median. Disclosed range: $130K to $185K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Freestone Capital Management AI Hiring
Freestone Capital Management has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $185K - $185K.
Location Context
AI roles in Seattle pay a median of $227,400 across 1,084 tracked positions. That's 14% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.