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About This Role
Company Information
PACCAR is a global technology leader in the design, manufacture and customer support of high\-quality light\-, medium\-, and heavy\-duty trucks under the Kenworth, Peterbilt and DAF nameplates. PACCAR also designs and manufactures advanced diesel engines, provides financial services and information technology, and distributes truck parts related to its principal business. Whether you want to design the transportation technology of tomorrow, support the staff functions of a dynamic, international leader, or build our excellent products and services — you can develop the career you desire with PACCAR. Get started!
Kenworth Truck Company is the manufacturer of The World’s Best® heavy and medium duty trucks. Kenworth is an industry leader in providing fuel\-saving technology solutions that help increase fuel efficiency and reduce emissions. The company’s dedication to the green fleet includes aerodynamic trucks, compressed and liquefied natural gas trucks, and medium duty diesel\-electric hybrids. Kenworth is the first truck manufacturer to receive the Environmental Protection Agency’s Clean Air Excellence award in recognition of its environmentally friendly products.
Requisition Summary
PACCAR is seeking an experienced AI Engineer with a strong ability to build, optimize, and operationalize machine learning and AI systems across diverse data sources and business domains. The ideal candidate is enthusiastic about learning new AI technologies and applying them to empower internal customers and scale our intelligent solutions platform. The ideal candidate demonstrates strong business and communication skills and the ability to partner closely with Data Scientists, Data Engineers, Research teams, and business owners across both technical and non\-technical groups to define key business problems, then develop, deploy, and maintain the AI models that solve them.
In this role, you will serve as the expert in designing, implementing, and operating stable, scalable, and cost‑efficient machine learning pipelines—from feature engineering through model training, evaluation, deployment, and monitoring. Above all, you should be excited about leveraging advanced models, including classical ML, deep learning, and large language models, to answer business questions and drive measurable impact.
The AI Engineer will design, develop, implement, test, document, and maintain large‑scale, high‑performance AI systems that support analytics, automation, and intelligent applications. You will build and manage production‑grade model pipelines using best practices in MLOps across Azure ML, Databricks, AWS SageMaker, or similar platforms. You will write efficient, scalable code and optimize model performance and inference workloads operating on large, complex datasets.
The person in this position should be analytical, have an extremely high level of customer focus, and a passion for continuous improvement. The AI Engineer should be a motivated self‑starter who can work independently in a fast‑paced, ambiguous environment and who brings excellent communication skills to collaborate with business stakeholders in defining problems, designing solutions, and validating real‑world outcomes.
Job Functions / Responsibilities
- Design, implement, and support AI/ML infrastructure using Azure ML, Databricks, and related cloud services.
- Build, deploy, and maintain machine learning pipelines, including data preprocessing, feature engineering, model training, validation, and monitoring.
- Develop and operationalize models for prediction, classification, NLP, computer vision, or recommendation systems based on business needs.
- Use notebooks, experiment tracking, and visualization tools (e.g., Azure ML Studio, MLflow, Jupyter) to enable transparent, reproducible AI workflows.
- Provide support for Agile projects and deliver models through CI/CD and MLOps pipelines.
- Ensure responsible AI practices, including security, compliance, fairness, and model explainability, are embedded in all solutions.
- Work with business stakeholders to translate requirements into AI solutions and measurable outcomes.
- Optimize model performance, inference latency, training efficiency, and compute cost.
- Improve foundational AI/ML procedures, standards, and documentation across experimentation, deployment, and monitoring.
- Comply with change control and model governance processes.
- Maintain audit compliance for data, model lineage, and experiment traceability.
- Support scheduled after\-hours maintenance as needed.
- Ability to participate in an on\-call rotation for production AI systems.
- Perform additional AI\- and ML\-related tasks.
- Must meet physical requirements of the position with or without accommodation.
- Other duties as assigned.
Qualifications Required:
- Bachelor’s degree in Computer Science, Data Science, Engineering, or related field.
- 2–5 years of experience in machine learning engineering, AI systems design, or applied ML.
- Strong knowledge of supervised/unsupervised learning, model evaluation, and feature engineering.
- Proficiency in Python and common ML frameworks (e.g., PyTorch, TensorFlow, Scikit\-learn).
- Experience deploying and monitoring models in cloud environments (Azure ML, AWS SageMaker, or GCP Vertex AI).
- Experience building automated ML pipelines using MLOps tools (MLflow, Kubeflow, Azure ML pipelines, GitHub Actions).
- Familiarity with containerization and orchestration (Docker, Kubernetes).
- Understanding of prompt engineering, LLM workflows, and vector databases (e.g., Azure AI Search, Pinecone, Weaviate) is a strong plus.
- Experience integrating AI services such as Azure OpenAI, cognitive services, or custom model endpoints.
- Familiarity with Jira, DevOps, or other project tracking tools.
- Expertise in GitHub or DevOps continuous delivery pipelines.
- Strong software engineering fundamentals, including testing, logging, and performance tuning.
- Knowledge of data processing tools and distributed compute frameworks (Spark, Databricks, Pandas).
- Excellent problem\-solving skills and ability to investigate complex model or pipeline issues.
- Experience with model explainability, monitoring, responsible AI frameworks, or governance is a plus.
Education/Training: Bachelor’s degree in computer science or related field required. Azure AI Engineer certification or equivalent preferred.
PACCAR is an Equal Opportunity Employer/Protected Veteran/Disability
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At PACCAR, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
PACCAR AI Hiring
PACCAR has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chillicothe, OH, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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