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Description
*The application window will close Month Day, Year*
Job Location
The primary work location for this role is Berwyn, Raleigh, Boston, Chicago, or Seattle with a hybrid work model.
About Envestnet
Envestnet is an adaptive WealthTech company that is redefining the future of wealth management by helping advisors meet the moment with its comprehensive technology, actionable insights, and industry leading support. Backed byover 25 years of experience and approximately $7\.0 trillion in platform assets, Envestnet is trusted by over one third of financial advisors across leading banks, wealth managers, brokerages, and RIAs.
For a deeper look at how Envestnet is shaping the future of financial advice, visit www.envestnet.com.
The Team You’ll Join
The Quality Assurance Engineering team plays a critical role in ensuring the reliability, security, and effectiveness of Envestnet’s technology solutions, with a growing focus on AI\-enabled products and platforms. Working at the intersection of engineering, data science, product, cybersecurity, and business operations, the team develops and executes innovative testing and validation strategies that help deliver trusted experiences for clients and internal stakeholders alike. By championing quality, governance, automation, and continuous improvement, the team helps accelerate the adoption of emerging technologies while ensuring solutions are scalable, compliant, and built to meet the highest standards of performance and customer confidence.
How You’ll Contribute
Ensures that artificial intelligence solutions are accurate, reliable, secure, compliant and aligned with business and client expectations. Combines traditional QA practices with AI / ML validation techniques to test data integrity, model performance, automation workflows and user outcomes. Works with engineering, data science, product management, cybersecurity, legal and risk teams to validate AI\-enabled products and operational processes. Continuously improves testing frameworks, monitoring methodologies, governance standards and automation capabilities to support scalable and trustworthy AI adoption.
- Leads testing efforts for moderately complex AI products, features and platform enhancements.
- Designs advanced test plans covering model accuracy, bias detection, explainability and operational resilience.
- Develops automated testing frameworks and monitoring approaches for AI systems.
- Performs detailed analysis of defects, model drift and production quality issues.
- Partners with cross\-functional stakeholders to define acceptance criteria and quality standards.
- Mentors junior analysts and reviews testing deliverables for quality and consistency.
- Supports implementation of enterprise AI governance and risk management controls.
- Recommends improvements to testing methodologies, tooling and operational processes.
- Facilitates quality reviews, stakeholder workshops, model validation discussions, and testing strategy sessions.
- Identifies operational, technical, compliance, and model\-related risks and recommends mitigation strategies.
- Leads validation activities for AI models, data pipelines, automation workflows, user\-facing AI capabilities, vendors, tools, and third\-party technologies.
- Evaluates quality, reliability, security, governance, and compliance considerations associated with AI products and services.
What You’ll Need to Bring
- Candidates should demonstrate the relevant experience, skills, and capabilities needed to successfully perform in the role. Relevant experience may be gained through current responsibilities, prior roles, project work, leadership opportunities, or other comparable experiences.
- Ability to evaluate complex problems, identify root causes, assess alternatives, and implement practical, scalable, data\-driven solutions.
- Demonstrated ability to establish and maintain productive relationships across business, technology, product, engineering, data science, and risk organizations.
- Knowledge of process improvement techniques, operational workflows, dependency management, quality optimization, and governance practices.
- Ability to communicate technical and non\-technical concepts clearly to diverse audiences, including leadership stakeholders.
- Experience coordinating cross\-functional initiatives, managing dependencies, tracking progress, and delivering measurable outcomes.
- Strong understanding of AI concepts, machine learning fundamentals, AI system capabilities, limitations, risks, and practical applications, with experience validating AI/ML models, Generative AI applications, LLM\-enabled systems, or data science solutions.
- Experience with automated testing frameworks, API testing, model evaluation, prompt testing, hallucination testing, data quality validation, AI governance controls, and regulatory, security, privacy, or responsible AI requirements.
Nice\-to\-Haves
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Why You’ll Enjoy Working at Envestnet
Help shape the future of WealthTech. At Envestnet you’ll gain hands\-on experience and collaborate with some of the industry’s brightest minds to deliver meaningful, innovative solutions that make a real difference.
We value flexibility in how and where work gets done, and we recognize strong performance with meaningful rewards—because your contributions should drive both business success and your own personal growth. If you’re looking for a place where your work has impact, your development is supported, and your contributions are truly valued, Envestnet is where you can build your future.
The opportunity is now!
*Sponsorship*
*This position is not open to candidates requiring visa sponsorship*
*Our Investment in You*
*This role offers a base salary range of* *$152,300 to $190,400**. The range listed* *represents* *a good\-faith estimate of base salary compensation for this position and does not include incentive compensation, equity or benefits. Individual pay will be* *determined* *based on factors including, but not limited to, relevant experience, skills, education, certifications, and geographic location,* *in accordance with* *applicable pay transparency laws.* *This role is eligible for an additional incentive* *component* *as part of the total rewards package.*
*We provide a comprehensive suite of benefits \- subject to Envestnet’s plan eligibility rules \- that support your overall well\-being including, medical insurance, paid time off (PTO), 401k company match, paid parental leave, education reimbursement, disability coverage and mental health \& wellness support. Our investment in you means supporting you professionally, financially, and personally at every stage of your journey with us. Please visit our benefits page on our career site to learn more.*
*Our Commitment to Inclusion \& Belonging*
*Envestnet is an Equal Opportunity Employer and is committed to creating an inclusive environment for all employees and applicants. We welcome and value individuals of all backgrounds and do not discriminate based on race, color, religion, creed, sex (including pregnancy or related medical conditions), gender identity or expression, sexual orientation, national origin, ancestry, age, disability, genetic information, military or veteran status, citizenship status, or any other status protected by applicable law. We encourage individuals from all backgrounds to apply.*
*We strive to provide an inclusive application and interview process. If you are a candidate with a disability and require reasonable accommodation, please contact us at* *[email protected]. Please include your full name, the title of the role you are applying for, and the accommodation necessary toassistyou with the recruiting process.*
*Recruitment Fraud*
*At Envestnet, safeguarding the trust and safety of job seekers is a top priority. We are aware that scammers may impersonate Envestnet recruiters or create fake job opportunities to deceive candidates. Review the information on our* *recruitment fraud awareness page* *to help you recognize and avoid recruitment fraud.*
\#LI\-AQ1 \#LI\-HYBRID
Salary Context
This $152K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Envestnet, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($171K) sits 20% below the category median. Disclosed range: $152K to $190K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Envestnet AI Hiring
Envestnet has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $190K - $190K.
Location Context
AI roles in Boston pay a median of $210,000 across 166 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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