Interested in this AI/ML Engineer role at White Cap?
Apply Now →Skills & Technologies
About This Role
A position at White Cap isn’t your ordinary job. You’ll work in an exciting and diverse environment, meet interesting people, and have a variety of career opportunities.
The White Cap family is committed to Building Trust on Every Job. We do this by being deeply knowledgeable, fully capable, and always dependable, and our associates are the driving force behind this commitment.
Job Summary
Responsible for serving as the Data Science team's senior technical leader and subject matter expert for machine learning (ML) and artificial intelligence (AI) systems. Responsible for the design, development, and operationalization of machine learning and AI solutions that address the organization's most complex business challenges while establishing technical standards and architectural direction for the team. As a force multiplier, this role accelerates delivery, elevates engineering quality, and strengthens team capabilities through hands\-on technical leadership, mentorship, and support.
Major Tasks, Responsibilities and Key Accountabilities
- Serves as the principal engineering authority for enterprise\-grade systems supporting data science, machine learning, and AI in Azure and Databricks. Sets the technical direction and architectural patterns that turn analytical models and prototypes into production solutions that are scalable, secure, reliable, and maintainable.
- Leads the architecture and hands\-on development of applications, services, APIs, and reusable components that deliver and operate data science solutions, covering model execution, business workflows, user experiences, system integrations, and downstream consumption of model outputs.
- Designs systems to scale across users, datasets, models, and business processes. Owns the tradeoffs related to performance, fault tolerance, availability, security, maintainability, and cost, and surfaces architectural risks early so solutions support long\-term enterprise use.
- Establishes the engineering practices for deploying, operating, and monitoring ML/AI solutions in production. Defines standards for CI/CD, automated testing, model and code versioning, environment management, observability, rollback, and production support across the full solution lifecycle.
- Defines and enforces software engineering standards for teams building systems in support of data science.
- Leads architecture, design, and code reviews covering code quality, modularity, testing, documentation, security, and maintainability, and serves as the final escalation point for complex technical decisions.
- Mentors engineers supporting data science and AI initiatives and guides them through complex implementation challenges, promotes engineering best practices, and builds the team's depth in cloud architecture, software development, distributed systems, and MLOps.
- Partners with data scientists, data engineers, product teams, enterprise architects, information security, infrastructure teams, and business stakeholders to ensure data science solutions are built on appropriate software and platform architectures.
- Translates analytical requirements into engineering designs and communicates technical decisions, dependencies, risks, and tradeoffs clearly.
Nature and Scope
- Consults with senior management on solution development for complex strategic and technical business issues. Independently solves unique and complex problems that have a broad impact on the business. Assignments are large scope, high impact, high cost, and high importance.
- Establishes operational plans for assigned area. Acts as a strategic advisor and uses expert skills to contribute to the development of strategic company objectives. Receives general administrative and business direction as needed. Typically operates with broad latitude in a complex environment.
- Guides and mentors staff at all levels, provides advice to senior management on complex and strategic issues, and may lead or manage complex projects with dotted line responsibility.
Work Environment
- Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.
- Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.
- Typically requires overnight travel less than 10% of the time.
Education and Experience
- Typically requires a bachelor’s degree and 10\+ years of experience in a related field OR MS/MA and generally 8\+ years of experience in a related field. Maintains expert knowledge in area of responsibility with a strong understanding in adjacent areas for the development of creative solutions.
Preferred Qualifications
- Advanced degree in computer science.
- Strong ability to summarize and communicate technical challenges and solutions effectively.
If you’re looking to play a role in building America, consider one of our open opportunities. We can’t wait to meet you.
Functional Area Marketing and Communications
Work Type Remote
Recruiter Banglinti, Shilpa
Req ID WCJR\-035126
White Cap is an Equal Opportunity Minority/Female/Individuals with Disabilities/Protected Veteran and Affirmative Action Employer. White Cap considers for employment and hires qualified candidates without regard to age, race, religion, color, sex, sexual orientation, gender, gender identity, national origin, ancestry, citizenship, protected veteran or disability status or any factor prohibited by law.
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 White Cap, 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 $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.
White Cap AI Hiring
White Cap has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.