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About This Role
At Prologis, we don’t just lead the industry—we define it with a 1\.3 billion square foot portfolio and an annual throughput of approximately $3\.2 trillion. We create the intelligent infrastructure that powers global commerce, seamlessly connecting the digital and physical worlds. From agile supply chains to energy solutions, our ecosystems help your business move faster, operate smarter and grow sustainably. With unmatched scale, innovation and expertise, Prologis is a category of one—not just shaping the future of logistics but building what comes next.Job Title:
Lead AI EngineerCompany:
Prologis
Title: Lead AI Engineer
Location: Denver, CO; San Francisco, CA; Phoenix, AZ; Chicago, IL; or Dallas, TX. Other Prologis office locations may be considered.
A day in the life
The Lead AI Engineer builds production AI platform capabilities that transform Prologis building, project, asset, and operational data into reusable solutions for construction, procurement, and operations teams. As a lead individual contributor, this role provides technical leadership through architecture, hands\-on implementation, pattern\-setting, and ownership of production outcomes rather than people management. The engineer partners with business, technology, security, and operations stakeholders to connect enterprise data, building models, documents, telemetry, digital twins, and workflow systems into governed AI capabilities. The work supports Prologis’s global logistics real estate portfolio of approximately 1\.3 billion square feet across 20 countries.
Key responsibilities include:
- Design and implement production AI platform capabilities, including agents, retrieval\-augmented generation, tool calling, workflow orchestration, evaluation, and human review.
- Build an AI\-native building knowledge platform that links building models, assets, documents, telemetry, and operational context.
- Develop multimodal data pipelines that convert BIM, Autodesk, geospatial, document, and operational data into structured, queryable knowledge.
- Deliver AI workflows supporting design, construction, commissioning, building operations, sustainment, and reinvestment use cases.
- Move priority solutions from prototype through pilot and production with appropriate governance, documentation, evaluation, observability, and operational readiness.
- Establish reusable engineering patterns that enable successful pilots to scale into enterprise platform capabilities.
- Partner with business, technology, security, and operations teams to define workflows, technical scope, implementation plans, rollout approaches, and post\-launch improvements.
Building blocks for success
Required:
- 6\+ years of experience in software engineering, platform engineering, data engineering, applied AI, automation, technology consulting, or a related technical delivery role.
- Experience designing, building, deploying, or maintaining AI\-assisted workflows, large language model applications, or agentic systems for business use cases.
- Experience translating ambiguous workflows, spreadsheets, document repositories, project records, and stakeholder feedback into structured, testable, and governed solutions.
- Experience leading discovery, workflow mapping, technical scoping, implementation, rollout, and post\-launch iteration with users or domain teams.
- Ability to apply context engineering, retrieval\-augmented generation, semantic layers, tool or function calling, structured outputs, orchestration, evaluation, and human\-in\-the\-loop design.
- Programming or scripting capability in Python, TypeScript, JavaScript, or a comparable language.
- Experience integrating APIs, SaaS platforms, data sources, workflow tools, and enterprise systems.
- Ability to communicate technical decisions, risks, and outcomes clearly to technical and non\-technical stakeholders.
Preferred:
- Experience with agent frameworks or orchestration patterns such as OpenAI Agents SDK, LangChain, LangGraph, LlamaIndex, MCP, AutoGen, CrewAI, n8n, or comparable platforms.
- Experience with Autodesk construction and building platforms, BIM, geospatial systems, reality capture, simulation platforms, or digital twins.
- Experience with AWS IoT, AWS Lambda, event\-driven or serverless architectures, streaming telemetry, rules engines, or cloud\-native operational data platforms.
- Experience with building lifecycle systems, operational technology, building management systems, asset management, estimating, bid management, project controls, or field support.
- Experience with data platform patterns such as knowledge graphs, entity resolution, ontologies, vector databases, Snowflake, data lakes, semantic layers, or BI and reporting.
- Demonstrates willingness and capability to leverage emerging technology, automation, and AI tools to improve efficiency, quality, and speed. Exercises sound judgment, creative thinking, and accountability for outcomes.
Hiring Salary Range of:
$147,000\.\- $202,000\. Salary and whole compensation package (bonus target) to be determined by the candidate’s location, education, experience, knowledge, skills, and abilities, as well as internal equity and alignment with market data.
\#LI\-KR1People First
Each of us working at Prologis plays an essential role in the enduring success of our company. We value people who are decisive, courageous and adaptable. While we are one company, locations and departments operate with autonomy and accountability. Individuals take the initiative here.
When you join Prologis, you work shoulder to shoulder with some of the top talent in the industry to do the best work of your career. Every employee belongs. Every employee contributes. Employees advance their careers here.
As a successful global enterprise, Prologis has never lost sight of what matters most, our strong belief that our people are the most important part of our business. And because of that, we provide a generous total rewards package and take a lot of time to focus on quality management and leadership development. People come first here.
All full\-time roles in the US come with a robust benefits package which includes healthcare, dental, and vision insurance for employees and eligible dependents. Prologis also offers several other wellness, financial, and work/lifestyle\-specific benefits. Our 401(k) retirement plan has a company match of 50% up to 12% of eligible compensation. We also offer generous PTO with a starting accrual of 22 days a year in addition to paid holidays and volunteer time.
All job offers are contingent upon successful completion of background verification. Prologis is an Equal Opportunity/Affirmative Action employer and all qualified applicants will receive consideration for employment without regard to race, color, religions, sex, national origin, sexual orientation, gender identity, disability status, protected veteran status, or any other characteristic protected by law.Employment Type:
Full timeLocation:
Denver, ColoradoAdditional Locations:
Chicago, Illinois, Dallas, Texas, Phoenix, Arizona, San Francisco, California
Salary Context
This $147K-$202K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Prologis, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($174K) sits 20% below the category median. Disclosed range: $147K to $202K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Prologis AI Hiring
Prologis has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $202K - $202K.
Location Context
AI roles in Denver pay a median of $201,050 across 48 tracked positions. That's 8% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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