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About This Role
The ML Engineer is responsible for the overall development, deployment, and support of our machine learning operations Harbor Freight. This includes the architecture and implementation of tools for model training, model monitoring, feature stores, model deployments, and model maintenance.
This role requires working with multiple levels of the organization, data science teams, application teams, security, software engineering, and business partners. It requires an experienced machine learning engineer with excellent business acumen, very strong technical skills, and data modeling / data warehousing expertise.
This position is technical and analytical in nature.
Duties and Responsibilities* Work closely with data scientists and IT in the development and implementation of our Enterprise AI platform.
- Build and maintain an industry leading MLOps tech stack.
- Mentor data scientists within the business, ensuring we’re building best\-in\-class models.
- Optimize the scalability, performance, and reliability of our models by implementing best practices and leveraging industry\-standard technologies.
- Streamline data ingestion, pre\-processing, feature engineering, and model training workflows to improve efficiency and reduce latency.
- Design, build, and maintain a secure and scalable CI/CD framework for data science teams.
Scope* Staff supervision and development: No
- Decision making: Recommends policy and resolves problems
- Travel: Up to 5%
- Flex Designation: Anywhere
The anticipated salary range for this position is $110,800 – $166,100 depending on location, knowledge, skills, education and experience. This position is also eligible for an annual discretionary bonus. In addition, we offer comprehensive and competitive benefits to Associates (and their families) such as medical, dental, vision, life insurance, short\-term and long\-term disability. Eligible Associates are able to enroll in our company’s 401k plan. Associates will accrue paid time off up to 236 hours per year (inclusive of PTO, floating holidays, and paid holidays). Paid sick time up to 80 hours per year unless otherwise required by law.
Requirements
Education and Experience
Education Requirements* Bachelor’s in Computer Science, Mathematics, Statistics, Engineering, or a related field.
- Master’s degree or Ph.D. is a plus.
Years of Experience* 2\-4 years experience as ML engineer or data scientist in a Big Data ecosystem, with a desire to assume greater responsibilities as a leader and mentor, while still being hands\-on.
- 2\-4 years experience developing, tuning, operationalizing, and monitoring enterprise ML models at scale.
- 2\-4 years experience with public cloud platforms \& systems (AWS, GCP, Azure).
Skills* Strong knowledge of distributed computing, data structures, data mapping, data warehouse, data mining, business analytics, software development, replication, and distributed/relational databases.
- Strong technical expertise in scripting (Python) database languages (SQL), and PySpark for model development.
- Excellent time management and planning skills, organized with the ability to multi\-task, exceptional follow\-up skills and able to meet deadlines.
- Excellent written, oral, and interpersonal communication skills, with ability to communicate effectively.
- Experience to tracking projects and goals to successful completion (with visible metrics).
- Ability to stay abreast of significant technological developments that may impact the business.
- Equipped to effectively prioritize, collaborate and excel in a fast\-paced, high\-pressure environment.
- Highly self\-motivated, self\-directed, and attentive to detail, with an emphasis on accuracy, detail, and timeliness.
- Understanding and experience with project management methodologies.
- Ability to manage multiple projects concurrently.
Physical Requirements
General office environment requiring ability to:* Stand, walk, sit for extended periods of time.
- Speak and listen to others in person and over the phone and video conferencing.
- Use keyboard and read from computer screen and reports.
- The ability to lift up to 15 lbs.
Safety* Must be able to perform this job safely in accordance with standard operating procedures and good manufacturing practices, without endangering the health or safety of self or others.
About Harbor Freight Tools
We’re a 45 year\-old, $8 billion national tool retailer with the energy, enthusiasm, and growth potential of a start\-up. We have over 1,600 stores in 48 states across the country and are opening several new locations every week. We offer our customers more than 7,000 tools and accessories, from hand tools and generators to air and power tools, from shop equipment to automotive tools. We provide our customers with the right tool for the right job at the right price, always delivering quality and value.
Salary Context
This $110K-$166K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Harbor Freight Tools, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($138K) sits 24% below the category median. Disclosed range: $110K to $166K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Harbor Freight Tools AI Hiring
Harbor Freight Tools has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Calabasas, CA, US. Compensation range: $166K - $166K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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