Interested in this AI/ML Engineer role at Esri?
Apply Now →Skills & Technologies
About This Role
Overview
Join our Indoor GIS team and help shape the future of how people understand, navigate, and interact with the built environment. We are building AI\-powered capabilities that bring intelligence to indoor spaces, extracting meaning from 3D scans, floor plans, imagery, and spatial data to automate workflows and unlock new possibilities for our customers.
This role sits at the intersection of computer vision, AI/ML, 3D graphics, and geospatial software. Depending on the problem, your work may span applied research and prototyping, production engineering, or integration with other Esri teams. You will help define what's possible and figure out the best path to get there.
If you are energized by ambiguous, high\-impact problems and want to work across a wide technology surface, from LLMs and Gaussian Splatting to image processing and semantic search, this team offers a uniquely broad and forward\-looking challenge.
Esri has a Relocation Assistance Program and can provide support with relocating to the Redlands, CA area for this position.
Responsibilities
Research, prototype, and develop AI\-powered features across a diverse problem space, including:
- Develop software components and tools for indoor feature extraction, Gaussian Splatting, PDF floor plan extraction, indoor reality workflows, and AI\-assisted validation
- Prototype and integrate algorithms for image/video analysis, geometric calculations and transformations, model evaluation, semantic search, and visual positioning under guidance from senior engineers
- Write clean C\+\+, .NET/C\#, and Python code to process spatial data, imagery, video frames, floor plan geometry, and user\-reviewed AI/ML outputs
- Contribute to experiments with LLMs, computer vision models, embeddings/vector search, and AI coding workflows, documenting results and limitations clearly
- Collaborate with cross\-functional engineering and product teams across Esri
- Participate in code reviews and software quality processes
- Learn and apply GIS concepts, floor\-aware mapping patterns, ArcGIS data models, and indoor facility workflows to ensure features solve real user problems
- Present prototypes, demos, and findings to the team and incorporate feedback into iterative development
- Participate as an individual contributor to a Scrum software development team
Requirements
- 2\+ years of professional software development experience
- Proficiency in Python and working knowledge of C\+\+ or .NET/C\#; willingness to deepen expertise in all three as required by product work
- Experience prototyping or integrating AI/ML technologies into real software
- Strong understanding of software algorithms and data structures
- Familiarity with image analysis, computer vision, or 3D/graphics programming
- Experience working with APIs and services
- Strong analytical, problem\-solving, and communication skills
- Experience or exposure using AI to assist in code development (Copilot or spec driven development with an LLM like Claude, Codex, or similar)
- Bachelor's degree in Computer Science, Software Engineering, or a related STEM field
Recommended Qualifications
- Experience with OpenCV, ONNX, image/video feature extraction, object detection, segmentation, or model evaluation
- Exposure to Gaussian splats, 3D mesh processing, graphics programming, game engines, WebGL/OpenGL/DirectX, or reality capture workflows
- Familiarity with GIS concepts, ArcGIS, CAD/BIM/Revit, LiDAR, 360 imagery, indoor positioning, or indoor mapping workflows
- Experience with semantic search, embeddings, vector databases, RAG, LLM APIs, Claude/ChatGPT/Gemini/Llama, or AI\-assisted coding tools such as GitHub Copilot/Copilot Chat
- Experience creating clear technical specs, unit tests, benchmark scripts, data analysis notebooks, or prototype documentation
- Master's degree or advanced coursework in Computer Science, AI/ML, Computer Vision, Graphics, Robotics, GIS Science, or a related STEM field
\#LI\-JJ2
\#LI\-ONSITE
The Company
At Esri, diversity is more than just a word on a map. When employees of different experiences, perspectives, backgrounds, and cultures come together, we are more innovative and ultimately a better place to work. We believe in having a diverse workforce that is unified under our mission of creating positive global change. We understand that diversity, equity, and inclusion is not a destination but an ongoing process. We are committed to the continuation of learning, growing, and changing our workplace so every employee can contribute to their life's best work. Our commitment to these principles extends to the global communities we serve by creating positive change with GIS technology. For more information on Esri's Racial Equity and Social Justice initiatives, please visit our website here.
If you don't meet all of the preferred qualifications for this position, we encourage you to still apply!
Esri is an equal opportunity employer (EOE) and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. If you need reasonable accommodation for any part of the employment process, please email askcareers@esri.com and let us know the nature of your request and your contact information. Please note that only those inquiries concerning a request for reasonable accommodation will be responded to from this e\-mail address.
Esri Privacy Esri takes our responsibility to protect your privacy seriously. We are committed to respecting your privacy by providing transparency in how we acquire and use your information, giving you control of your information and preferences, and holding ourselves to the highest national and international standards, including CCPA and GDPR compliance.
Salary Context
This $101K-$167K range is in the lower quartile 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 Esri, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($134K) sits 39% below the category median. Disclosed range: $101K to $167K.
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.
Esri AI Hiring
Esri has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in Redlands, CA, US. Compensation range: $133K - $202K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.