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About This Role
At C.H. Robinson, we're transforming the way data powers decisions across our global business. As a Principal Analytics Engineer \- Analytics and AI, you'll play a key role in advancing our analytics strategy by designing modern, scalable business intelligence solutions that combine data, automation, and AI to deliver faster, more trusted insights.
You'll operate at the intersection of software engineering, data architecture, business intelligence, and artificial intelligence—building enterprise\-scale analytics products that are secure, scalable, and designed for the future. You'll serve as both a technical leader and strategic partner, translating complex business challenges into enterprise analytics products that enable self\-service reporting, intelligent automation, and AI\-powered decision support. You'll lead the design and evolution of trusted data assets, semantic models, dashboards, and automated workflows while helping shape the future of how data is accessed, governed, and consumed across the organization.
Leveraging deep expertise in Power BI, SQL, Snowflake, data modeling, and analytics architecture, you'll partner with cross\-functional teams to build scalable solutions that improve operational efficiency and business outcomes. You'll also help drive adoption of emerging technologies—including Microsoft Copilot, AI\-assisted development, workflow automation, and conversational analytics—to enhance productivity and create next\-generation user experiences.
If you're passionate about combining business intelligence, data architecture, automation, and AI to solve complex business problems, this is an opportunity to make a lasting impact on one of the world's largest logistics and supply chain organizations while helping shape the future of data\-driven innovation at C.H. Robinson.
Duties \&Responsibilities:
The duties and responsibilities of this position consists of, but are not limited to, the following:
- Design analytics solutions using software engineering best practices to ensure scalability, maintainability, and long\-term supportability.
- Build reusable analytics components, semantic models, and shared data services that accelerate enterprise adoption.
- Lead adoption of AI\-assisted development, automation, and engineering practices within the Analytics organization.
- Partner with software engineering and platform teams to integrate analytics into enterprise applications through APIs and event\-driven architecture.
- Extract and interpret data to answer ambiguous questions while working directly with personnel across the network
- Determine reporting, analytics, and additional analysis needed to support initiatives and measurement
- Proactively analyze topics, identify insights, and create recommendations supported by facts to change behaviors and drive operational improvements across the team and business
- Clearly translate and communicate complex concepts into understandable, professional presentations
- Consult, support, and inform leadership using data\-driven insights
- Translate business needs into technical specifications and scalable analytical solutions
- Analyze, design, develop, document, test, and deploy BI, analytics, and data solutions
- Design and maintain semantic models, KPI definitions, and governed data assets that enable consistent reporting, analytics, and AI\-driven insights
- Develop and execute SQL across enterprise and cloud data platforms, including Snowflake, to power analytics and reporting solutions
- Create engaging visualizations, dashboards, and self\-service analytics products for internal and external stakeholders
- Enhance, modernize, and support existing solutions, including migration of legacy assets where appropriate
- Identify opportunities to automate analytics workflows and reduce manual effort through AI\-enabled development, workflow orchestration, and self\-service solutions
- Contribute to the design of conversational analytics and natural\-language data experiences that increase accessibility to trusted business insights
- Evaluate and implement emerging technologies that improve analytics delivery, stakeholder experience, and team productivity
- Lead the adoption of modern analytics practices, including AI\-assisted development, automated testing, documentation, and governance standards
- Establish best practices for data quality, governance, lineage, and validation to ensure trust in analytics and AI\-generated outputs
- Develop and maintain technical documentation to support long\-term maintainability and knowledge sharing
- Implement best practices for optimum use, performance, scalability, and maintainability of BI and analytics solutions
- Provide technical leadership to the team and help build the skills of other team members
- Mentor team members on modern BI, analytics engineering, and AI\-enabled delivery practices
- Mentor, train, and support the development of other analysts across the network
- Present and deliver analytical value through business intelligence, storytelling, and cross\-functional collaboration
- Build relationships and consult across the network to bring solutions to life
- Collaborate with internal analytics teams to develop solutions in tandem
- Partner with business and technology stakeholders to develop scalable analytics products and data solutions that support strategic objectives
- Partner with internal departments to understand drivers of trends and the impact of business changes
- Collaborate with leadership to develop and maintain robust performance reporting and analytics
- Advocate for and build data\-fueled products that help our Commercial Organization improve business outcomes
- Lead project plans, process flows, and metrics; facilitate project meetings, communicate progress, and escalate blockers when needed
- Engage with internal and external customers to identify and prioritize areas of analysis and opportunity
Required Qualifications:
- 8\+ years of analytics experience developing business solutions using comprehensive, robust data assets
- Experience with AI\-enabled analytics, conversational analytics, natural\-language querying, or intelligent workflow solutions.
- Experience applying software engineering principles to analytics solutions, including Git\-based source control, CI/CD pipelines, automated testing, and code review practices.
- Experience developing reusable analytics frameworks, semantic models, APIs, or shared data services.
- Experience integrating AI and Large Language Models (LLMs) into analytics workflows using technologies such as Microsoft Copilot, Azure AI, OpenAI, or similar platforms.
- Experience with Python or C\# for analytics automation, data processing, or AI solution development.
- Experience building cloud\-native analytics solutions on Azure using services such as Azure Data Factory, Azure Functions, Logic Apps, or Fabric.
- Experience with relational and non\-relational (NoSQL) databases (e.g., MongoDB, Cosmos DB, Cassandra, DynamoDB) and modern cloud data platforms.
- Experience designing data products and enterprise\-scale analytics platforms rather than standalone dashboards.
- Familiarity with Infrastructure as Code, DevOps practices, and modern deployment automation.
- Experience partnering with software engineering teams to build scalable data and analytics solutions.
- Mastery proficiency with Microsoft Power BI, and strong working proficiency with the broader Microsoft Power Platform Suite of programs (Power Apps, Power Automate)
- Advanced knowledge and practical application of a programming language
- Bachelor’s degree or a minimum of 4 years of equivalent work experience and a high school diploma/GED
Preferred Qualifications:
- Logistics and supply chain experience
- Mastery understanding of data fundamentals, SQL Querying, and passion for data integrity, testing, and validation
- Experience working with product teams in an iterative environment
- Positive, collaborative working style
- Effective communicator and data/reporting/business liaison through multiple mediums
- Values a diverse and inclusive work environment
We will review applications for this role on an ongoing basis and encourage all interested candidates to apply at their earliest convenience.
Compensation Range
$84,800\.00 \- $191,000\.00
The base pay range displayed on each job posting reflects the minimum and maximum base pay for the position across all U.S. locations. Your individual base pay within this range is determined by work location, which takes into account geographic cost of labor, and additional factors, including job\-related skills, experience, and relevant education or training. Compensation details listed in this posting reflect the base pay only and do not include additional variable compensation.
Questioning if you meet the mark? Studies have shown that some individuals may be less likely to apply unless they match the job description exactly. Here at C.H. Robinson, we’re building an inclusive workplace where all employees feel they belong. If this position excites you, we welcome you to apply whether you check all the preferred qualifications or just a few. You may just be our next great fit!
Equal Opportunity
C.H. Robinson is proud to be an Equal Opportunity Employer. We are committed to a workplace and performance culture that reflects the strengths of our worldwide marketplace. We value unique experiences and diverse backgrounds of our people within our company, our business relationships, and our communities. We’re committed to providing an inclusive environment, free from harassment and discrimination, where all employees feel welcomed, valued and respected.
EOE\\Disabled\\Veteran
Benefits
Your Health, Wealth and Self
Your total wellbeing is the foundation of our business, and our benefits support your financial, family and personal goals. We provide the top\-tier benefits that matter to you most, including:
- Three medical plans which include
- + Prescription drug coverage
+ Enhanced Fertility benefits
- Flexible Spending Accounts
- Health Savings Account (including employer contribution)
- Dental and Vision
- Basic and Supplemental Life Insurance
- Short\-Term and Long\-Term Disability
- Paid observed holidays
- 2 paid floating holidays for U.S. hourly employees
- Flexible Time Off (FTO) offered to U.S. salaried employees — no accruals and no caps. Paid Time Off (PTO) offered to all other employees in the U.S. and Canada
- Paid parental leave
- Paid time off to volunteer in your community
- Charitable Giving Match Program
- 401(k) with 6% company matching
- Employee Stock Purchase Plan
- Plus a broad range of career development, networking, and team\-building opportunities
Learn more about our benefit offerings on our BENEFITS \& WELLBEING page
Salary Context
This $84K-$191K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At C.H. Robinson, 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. This role's midpoint ($137K) sits 36% below the category median. Disclosed range: $84K to $191K.
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.
C.H. Robinson AI Hiring
C.H. Robinson has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $191K - $191K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national 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 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
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