# Axial Search — Vice President, AI/ML

- Generated: 2026-09-14 04:25:24 PM EDT
- Original/canonical posting: https://www.linkedin.com/jobs/view/4466105912/
- Provider: LinkedIn
- Posted: approximately 2026-09-14 03:25 PM EDT (from '1 hour ago')
- Elapsed since posting: represented by the provider's relative posting label and approximate Eastern timestamp when available
- Applicants: Be among the first 25 applicants
- Work model/location: United States; remote eligibility and end employer are not verified
- Compensation: $200,000-$310,000 market range
- Travel: Not disclosed
- Positioning track: Technical manager
- Fit outcome: FAIL — 63%

## Direct-match strengths

AI engineering leadership, production platforms, MLOps, deployment, monitoring, reliability, cost controls, cross-functional delivery, technical roadmaps, and team building.

## Hard or material gaps

Hard required-depth gap: the listing seeks eight-plus years of hands-on ML-systems engineering with deep ownership of model training, feature engineering, training pipelines, and production serving. Keith's strongest recent evidence is LLM application/platform engineering, RAG, agents, evaluation, and cloud AI rather than eight years of classical model-training and feature-engineering ownership. This is also a talent-pipeline posting rather than a verified employer requisition.

## Evidence map

1. 8+ years hands-on ML systems engineering (weight 3, evidence 1/3) — Long AI background, but not eight documented years of hands-on ML systems engineering.
2. Model training and feature engineering (weight 3, evidence 1/3) — Applied modeling is supported; deep recent training/feature-pipeline ownership is not.
3. Deployment, monitoring, and MLOps (weight 3, evidence 3/3) — Direct production deployment, evaluation, monitoring, governance, and incident practices.
4. Lead teams of 5-15+ (weight 3, evidence 3/3) — Built and led multiple teams in the requested range.
5. Scale production ML for latency, reliability, and cost (weight 2, evidence 3/3) — Direct production AI optimization, 99.9% availability, routing, latency, and cost controls.
6. End-to-end system ownership (weight 2, evidence 3/3) — Repeated architecture-through-operations ownership.
7. Verified employer requisition (weight 3, evidence 0/3) — Executive-search market profile does not identify the hiring employer.

## Keyword diagnostic

Strong production AI platform and MLOps adjacency; mandatory eight-year classical ML engineering, model-training, and feature-engineering depth is unsupported.

## Full normalized job description

Axial Search is a specialist executive search firm built for one kind of hire: leaders who help organizations navigate AI transformation. Apply today to express your interest in roles like this one.
Visit our website to learn more about our process and explore free tools for your job search, including our live job market dashboard with salary, skills and hiring trend data from thousands of AI transformation roles.
What The Market Looks Like
We've tracked 49 senior-level ML engineering postings across the US in the last six months, with strongest demand in California, Washington, and New York. The role concentrates in technology, financial services, and professional services, where organizations are moving beyond proof-of-concept to production ML systems. Compensation for this seniority typically ranges from $200K to $310K annually. The strongest candidates bring 8+ years of hands-on ML systems experience, deep ownership of model training and deployment pipelines, and a track record of leading small to mid-sized engineering teams through complex infrastructure and scaling challenges.
Job Responsibilities
Lead the ML engineering organization—hiring, mentoring, and retaining engineers; setting technical direction and standards across the team
Own the design and execution of core ML systems and platforms, from data pipelines and feature engineering through model deployment and monitoring
Drive infrastructure and tooling decisions that reduce friction in model training, experimentation, and production serving
Partner with product, data science, and analytics teams to translate business requirements into robust ML solutions and integrate models into customer-facing applications
Build and scale MLOps practices—establishing governance, reproducibility, model versioning, and incident response processes
Manage technical roadmap and resource allocation; prioritize between technical debt, capability-building, and business-critical delivery
Advocate for ML engineering maturity and best practices internally; represent the function in cross-functional leadership conversations
Candidate Requirements
8+ years of hands-on ML engineering or machine learning systems engineering experience, with at least 3 years in a leadership or principal-level role
Deep proficiency in model training, feature engineering, and deployment pipelines; hands-on experience with MLOps tooling, containerization, and production serving frameworks
Proven experience building and leading ML engineering teams of 5–15+ people; track record of hiring, coaching, and developing strong technical talent
Demonstrated ability to design and own complex systems end-to-end, balancing technical rigor with business pragmatism and delivering production results
Strong communication and stakeholder management skills; comfortable translating between technical and business contexts and influencing without direct authority
Experience scaling ML systems for production use—addressing latency, reliability, monitoring, and cost at meaningful scale

## Artifact metadata

- Resume: Not generated because the fit gate returned FAIL.
- Cover letter: Not generated because the fit gate returned FAIL.
- LinkedIn note: Not generated for a FAIL role.
- Google Drive used: No