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About This Role
At VF, we strive to foster a culture of belonging based on respect, connection, openness, and authenticity. So, before we get to the job details, take a minute to learn a little more about us – our values and our culture - visit VF Careers or www.vfc.com.
What will you do?
A day in the life of a Senior Engineer, Cloud AI and Data Platforms at VF looks a little like this.
The Sr. Engineer, Cloud AI and Data Platforms will be an integral member of VF’s Cloud Platform Engineering team. The Cloud Platform Engineering Team is a team of full-time cloud resources that will drive and guide cloud adoption across our organization. This team is responsible for the development and delivery of our Continuous Infrastructure Automation platform which will be leveraged across multiple cloud platforms.
In addition to participating in the development of this platform, this role will provide consultative services to help Digital Technology teams take advantage of self-service infrastructure provisioning, configuration management & governance across multi-cloud platforms.
Let’s break down that day-in-the-life a bit more.
Act as a consultative resource for our application teams to provide technical guidance, standards, knowledge and understanding of processes, and tools to deliver products and services in multi-cloud. Build CIA process automation and tooling to implement guardrails in a way that empowers our application teams to help themselves for their infrastructure provisioning and configuration needs. Develop tools, scripts and frameworks of moderate complexity that drive efficiency in automation, monitoring and management of large-scale environments.
- Build declarative provisioning & configuration templates to facilitate self-service provisioning
- Build governance, compliance & security as code templates to facilitate on the side governance
- Design and build automated deployment frameworks (CI/CD) for multi-cloud
- Architect, design, and build VF’s AI and Data Platforms in Azure Databricks and Azure AI Foundry.
- Become the change agent for the organization in all matters regarding AI and Data platforms in our Cloud Environments.
- Manage source code and build artifacts for Continuous Infrastructure Automation platform
- Implement, manage & support tools for managing multi-cloud platforms.
- CI/CD, SCM and artifact repository tools
- Monitoring and observability tools
- Cost Management tools
- Governance & Compliance tools
- Mentor team members and co-workers on cloud best practices, GitOps & DevOps principles & practices. Resident cloud infrastructure subject matter expert researching, implementing and communicating Infrastructure-as-code solutions across the enterprise.
What do you need to succeed?
We all have unique skills that we bring to work and celebrate every day. For this role, there are foundation skills you’ll need to succeed and excel. Additionally, while formal education in a related field is great to have, we are most interested in your 5+ years of experience and professional achievements.
The foundation skills you will need in this position are:
- Bachelor’s degree in Information Systems or equivalent experience in cloud platform engineering
- Bachelor’s degree in Information Systems or equivalent experience in cloud platform engineering
- At least 5 years’ experience designing, implementing and supporting cloud-based infrastructure and platform services including AWS and Azure required
- Extensive experience with public cloud offerings such as Amazon Web Services, Microsoft Azure.
- Must be able to act as a champion for a DevOps culture
- Ability to iterate quickly in an agile development process
- Excited to learn new technologies and evaluate technologies to problems
- Experience in CI/CD automation
- Experience building infrastructure automation (orchestration, provisioning and configuration) using tools such as Terraform, Packer, Ansible, etc.
- Experience with Cloud Native engineering (K8s, Containers, Docker, Serverless, etc.)
- Experience with test-driven development frameworks for application and infrastructure code
- In-depth network and system engineering experiences including DNS, ADFS, HTTPS, Firewalls, L7 routing,
- Ability to multi-task, be a strong team player, and have strong organizational skills and time management skills.
- Strong knowledge of ITIL, SDLC, DevOps and agile processes and practices. Experience following enterprise change and release management processes.
- DBA experience preferred.
- Broad knowledge of SRE framework for monitoring golden signals and applicable tools
- Must possess excellent problem-solving skills and an ability to find, troubleshoot, and resolve issues using whatever outside resources (i.e. research) are required
Special Physical and/or Mental Requirements:
- Requires some degree of sitting (for prolonged periods of time), standing, lifting, carrying pushing, pulling more than 20 lbs.
- May require extended work hours including participation in on-call rotations.
- Travel by air and overnight, 10%
Now WE have a question for YOU.
Are you in?
Hiring Range:
$104,000.00 USD - $130,000.00 USD annuallyIncentive Potential: This position is eligible for additional compensation awards that may include an annual incentive plan, sales incentive, or commission potential. Specific details of the additional compensation eligibility for this position will be provided during the recruiting and interview process.
Benefits at VF Corporation: You can review a general overview of each benefit program offered, including this year's medical plan rates on www.MyVFbenefits.com and by clicking Looking to Join VF? Detailed information on your benefits will be provided during the hiring process.
P**lease note, our hiring ranges are determined and built from market pay data. In determining the specific compensation for this position, we comply with all local, state, and federal laws.
At VF, we value a diverse, inclusive workforce and we provide equal employment opportunity for all applicants and employees. All qualified applicants for employment will be considered without regard to an individual’s race, color, sex, gender identity, gender expression, religion, age, national origin or ancestry, citizenship, physical or mental disability, medical condition, family care status, marital status, domestic partner status, sexual orientation, genetic information, military or veteran status, or any other basis protected by federal, state or local laws. If you require accommodations during the application process, please contact us at [email protected]*. VF will provide reasonable accommodations for qualified individuals to the extent required by applicable law.*
Pursuant to all applicable local Fair Chance Ordinance requirements, including but not limited to the San Francisco Fair Chance Ordinance, VF will consider for employment qualified applicants with arrest and conviction records.
Salary Context
This $104K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Timberland, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($117K) sits 45% below the category median. Disclosed range: $104K to $130K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Timberland AI Hiring
Timberland has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Greensboro, NC, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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