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Career Area:
EngineeringJob Description:
Your Work Shapes the World at Caterpillar Inc.
When you join Caterpillar, you're joining a global team who cares not just about the work we do – but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here – we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it.
The Senior Statistician – AI Product Assurance is responsible for providing rigorous statistical evidence to support the design, implementation and deployment of safety\-related AI\-enabled products. This role is part of a newly established AI Product Assurance team focused on demonstrating that complicated, complex, agentic based products meet defined safety objectives throughout their lifecycle.
Statistical validation is a core element of the assurance approach, alongside functional safety and Safety of the Intended Functionality (SOTIF). The Senior Statistician serves as a technical authority, translating safety intent into quantitative evidence that can be reviewed, audited, and defended.
What You Will Do:
- Lead the development and execution of statistical validation plans that support AI product safety claims for release and in\-service operation.
- Define quantitative safety metrics, acceptance criteria, and confidence levels aligned with internal governance and external standards.
- Design and analyze experiments, simulations, and operational data sets to assess risk, uncertainty, and residual hazards, including rare\-event and edge\-case behavior.
- Apply advanced statistical techniques such as Bayesian inference, reliability analysis, survival analysis, uncertainty quantification, and causal methods to complex systems.
- Evaluate system performance under uncertainty, distributional shift, and changing operational conditions.
- Collaborate with the engineering teams to integrate statistical evidence into safety cases and assurance artifacts.
- Review and interpret large\-scale test, simulation, and field data to identify safety\-relevant trends, failure modes, and emergent behaviors in agentic systems.
- Clearly document assumptions, limitations, and conclusions to support internal decision\-making and external review.
- Provide technical guidance and mentoring to engineers and analysts on sound statistical practices for safety\-critical applications.
- Support post\-deployment monitoring, re\-validation, and change impact assessment as AI systems evolve.
What You Have:
- Business Statistics: Knowledge of the statistical tools, processes, and practices to describe business results in measurable scales; ability to use statistical tools and processes to assist in making business decisions.
+ Significant experience applying statistical methods to complex, real\-world systems where safety, risk, or reliability are critical.
+ Strong foundation in statistical inference, experimental design, and uncertainty analysis.
+ Experience with Bayesian methods, reliability engineering, rare\-event estimation, and analysis of non\-stationary data
- Programming Languages: Knowledge of basic concepts and capabilities of programming; ability to use tools, techniques and platforms in order to write and modify programming languages.
- Query and Database Access Tools: Knowledge of data management systems; ability to use, support and access facilities for searching, extracting and formatting data for further use.
- Accuracy and Attention to Detail: Understanding the necessity and value of accuracy; ability to complete tasks with high levels of precision.
- Bachelor’s Degree in Statistics, Applied Mathematics, Data Science or related quantitate discipline.
Consideration for Top Candidates:
- Advanced Degree in Statistics, Applied Mathematics, Data Science or related quantitate discipline.
- Experience working with AI, machine learning, autonomous, or agentic systems.
- Familiarity with functional safety standards and/or SOTIF concepts.
- Experience with Earth\-Moving Machinery Products
- Experience contributing statistical evidence to safety cases, assurance cases, or regulatory submissions.
- Background in safety\-critical industries such as autonomy, robotics, automotive, aerospace, industrial systems, or medical devices.
Demonstrated ability to communicate complex statistical concepts to non\-statistical stakeholders.
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Additional Information:
- This position requires the candidate to work full\-time at the Pittsburgh, PA office.
- Alternative locations are Mossville IL, Irving TX, and Tucson AZ.
- Domestic relocation assistance is available for this position.
- Visa sponsorship is available with this position.
- This position will require up to 10% travel
- \#LI
Summary Pay Range:
$112,710\.00 \- $183,140\.00
Compensation and benefits offered may vary depending on multiple individualized factors, job level, market location, job\-related knowledge, skills, individual performance and experience. Please note that salary is only one component of total compensation at Caterpillar.
Benefits:
Subject to plan eligibility, terms, and guidelines. This is a summary list of benefits.
- Medical, dental, and vision benefits\*
- Paid time off plan (Vacation, Holidays, Volunteer, etc.)\*
- 401(k) savings plans\*
- Health Savings Account (HSA)\*
- Flexible Spending Accounts (FSAs)\*
- Health Lifestyle Programs\*
- Employee Assistance Program\*
- Voluntary Benefits and Employee Discounts\*
- Career Development\*
- Incentive bonus\*
- Disability benefits
- Life Insurance
- Parental leave
- Adoption benefits
- Tuition Reimbursement
- These benefits also apply to part\-time employees
This position requires working onsite five days a week.
Relocation is available for this position.
Visa sponsorship is available for eligible applicants.Posting Dates:
June 5, 2026 \- June 16, 2026
Any offer of employment is conditioned upon the successful completion of a drug screen.
Caterpillar is an Equal Opportunity Employer, Including Veterans and Individuals with Disabilities. Qualified applicants of any age are encouraged to apply.
Not ready to apply? Join our Talent Community.
Salary Context
This $112K-$183K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Caterpillar, 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 in Demand for This Role
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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($147K) sits 17% below the category median. Disclosed range: $112K to $183K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Caterpillar AI Hiring
Caterpillar has 4 open AI roles right now. They're hiring across AI/ML Engineer, Prompt Engineer, Data Scientist. Positions span Pittsburgh, PA, US, Irving, TX, US, Chicago, IL, US. Compensation range: $183K - $258K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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