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JOB DESCRIPTION
Employee Compute is evolving from endpoint operations to an AI\-enabled compute control plane that supports a global workforce at scale. As Executive Director for Agentic Validation, Trust \& Evaluation, you will own and lead the function that verifies AI\-enabled employee experiences are safe, explainable, policy\-compliant, and operationally ready before and during production use.
This role sits within Risk \& Controls and is accountable for setting the strategy for, building, and running the validation, trust, and evaluation operating model for agentic capabilities used across Employee Compute journeys. You will partner across engineering, cybersecurity, architecture, and control teams to establish measurable confidence in AI\-assisted workflows through strong evaluation frameworks, auditable evidence, and clear governance outcomes aligned to a highly regulated environment.
This role holds executive\-level individual accountability for the effectiveness of the agentic validation and control environment within Employee Compute, including timely identification, escalation, and remediation of control gaps. The Executive Director is the senior named control owner for go/no\-go release decisions on AI\-enabled employee experiences and is answerable to senior leadership, audit, and regulators for the integrity of the evidence supporting those decisions.
Job Responsibilities
- Set and own the enterprise strategy for Agentic Validation, Trust \& Evaluation for Employee Compute, translating policy and risk expectations into practical validation and control mechanisms for AI\-enabled employee workflows.
- Design and operationalize end\-to\-end evaluation frameworks for agentic capabilities, including pre\-release validation, post\-release monitoring, drift detection, and periodic control attestations.
- Build standardized trust criteria for AI\-assisted outcomes such as identity integrity, authorization correctness, data\-handling boundaries, provenance, explainability, and reproducibility of decisions and actions.
- Establish and govern quality gates and go/no\-go decision practices for AI\-enabled releases, ensuring evidence\-based readiness across security, controls, resiliency, and operational support.
- Own the mandate that validation and control requirements are embedded in design and delivery lifecycles, with authority to block releases that fail to meet engineered\-in control standards (controls engineered in, not retrofit after launch).
- Create and own KPI/OKR and scorecard structures for trust and evaluation outcomes, including control health, issue closure velocity, incident learnings, and model/tool performance against policy expectations.
- Own and personally attest to examiner\-ready evidence packages and control\-testing results presented at risk and control forums, including executive communication on material risks, decisions, and mitigations to senior technology and control leadership.
- Be accountable for approving or rejecting AI supplier capabilities for broad enablement, based on documented validation against internal standards and operational realities; retain sign\-off authority and evidence of that decision.
- Ensure timely and transparent escalation of material control weaknesses, validation failures, and AI\-risk incidents to senior leadership and relevant risk committees.
- Own the risk issues and corrective actions logged for the function in the firm's risk system of record, ensuring accuracy, supporting evidence, and on\-time remediation.
- Build, lead, and develop a high\-performing team of validation and controls practitioners with strong technical fluency and disciplined risk\-management execution, including managing managers and senior individual contributors.
- Be accountable for control\-culture outcomes within the function — where trust, control integrity, and speed coexist through clear standards, automation, and continuous improvement — evidenced by control\-testing results, issue\-closure performance, and audit/examiner feedback.
Required Qualifications, Capabilities, and Skills
- Formal training or certification on software engineering concepts and 10\+ years applied experience
- 12\+ years of technology, risk, controls, or governance experience, with significant time in regulated enterprise environments and at least 5 years leading programs, teams, or cross\-functional workstreams.
- Proven experience designing or operating control frameworks for modern technology platforms, such as cloud, endpoint, identity, cybersecurity, or AI\-enabled systems.
- Demonstrated experience as a senior named control owner or risk owner accountable for audit/examiner outcomes, including managing findings to closure.
- Experience making and defending go/no\-go / risk\-acceptance decisions at an executive level, including documenting rationale and standing behind decisions under audit and regulatory scrutiny.
- Hands\-on experience with validation and evaluation approaches, including test design, scenario\-based assessments, evidence generation, and measurable acceptance criteria.
- Strong understanding of AI/agentic risk dimensions such as identity attribution, authorization boundaries, data governance, model/tool behavior variability, and operational misuse pathways.
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 JPMorganChase, 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.
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.
JPMorganChase AI Hiring
JPMorganChase has 141 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Jersey City, NJ, US, New York, NY, US, Seattle, WA, US. Compensation range: $120K - $450K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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