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About This Role
May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. Based in Ann Arbor, Michigan, May develops and deploys autonomous vehicles (AVs) powered by our innovative Multi\-Policy Decision Making (MPDM) technology that literally reimagines the way AVs think.
Our vehicles do more than just drive themselves \- they provide value to communities, bridge public transit gaps and move people where they need to go safely, easily and with a lot more fun. We're building the world's best autonomy system to reimagine transit by minimizing congestion, expanding access and encouraging better land use in order to foster more green, vibrant and livable spaces. Since our founding in 2017, we've given more than 500,000 autonomous rides to real people around the globe. And we're just getting started. We're hiring people who share our passion for building the future, today, solving real\-world problems and seeing the impact of their work. Join us.
Job Summary
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The Autonomy Mapping \& Localization group builds the spatial intelligence, semantic and topological mapping, and state estimation that let our autonomous vehicles understand where they are and what the world looks like, and we scale that technology safely and reliably to commercial operations serving riders and consumers. We are looking for a Lead ML Engineer to join our team and architect the next generation of our localization stack. As the Lead ML Engineer for Localization, you will build the production\-grade feature extraction and state estimation that lets our autonomous vehicles precisely navigate the world's most challenging roads at scale.
Essential Responsibilities
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- Architect and drive the technical roadmap for a production\-grade localization machine learning stack, spanning map and sparse landmark\-based localization (vision/LiDAR/radar), optimized for real\-time performance, robustness against sensor degradation, and integration with the broader autonomy system across diverse Operational Design Domains (ODDs).
- Lead the research, design, training, and validation of advanced neural architectures. This includes object detection, classification, segmentation, tracking, depth estimation, and 3D reconstruction to extract and model localization features (e.g., traffic signs, pole\-like objects, keypoints, edges, signals, and road markings), for robust localization.
- Drive major feature development from inception to deployment. This includes high\-level architecture design, rigorous code reviews, automated testing, mentorship of junior engineers, and technical resolution.
- Own the end\-to\-end data strategy for the localization feature extraction domain. You will define data curation, auto\-labeling, synthetic data, and active learning pipelines to capture and resolve long\-tail scenarios.
- Develop robust metrics and evaluation frameworks for localization performance, including feature extraction accuracy, temporal consistency, and system\-level reliability across diverse ODDs.
- Define and validate failure mode and degradation criteria for localization features across ODDs, ensuring safety case coverage and graceful fallback behavior under sensor or model failure.
- Evaluate, adapt, and integrate frontier techniques, including multimodal localization and vision/fusion foundation models, translating research advances into production\-ready solutions.
- Drive cross\-functional alignment, translating complex autonomy goals into clear software and system requirements.
Qualifications and Experience
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Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience:
### Required
- Ph.D. or Master's degree in Computer Science, Electrical Engineering, Robotics, or a related field with a strong mathematical and engineering foundation.
- 7\+ years of industry experience developing and deploying ML/DL models for computer vision or localization at scale.
- Deep expertise in several of the following areas:
- + Computer Vision Foundations: Object detection, classification, segmentation, tracking, depth estimation, 3D reconstruction, and feature detection/description (e.g., SIFT, ORB, SuperPoint).
+ Vectorized landmark and feature detection networks, BEV\-based scene representation, and temporal modeling.
+ Self\-supervised/semi\-supervised learning, open\-vocabulary detection, and vision/fusion Foundation Models.
- Experience with feature extraction and/or fusion from imagery, LiDAR, and/or radar.
- Expertise in ML/DL development using PyTorch or TensorFlow, including experience with synthetic data generation, large\-scale dataset handling, data curation, and active learning strategies.
- Strong programming skills in Python and/or C\+\+ with experience in modular software design and Linux\-based development.
- Expertise in ML optimization for real\-time products with limited compute, such as quantization and pruning of large transformer models.
- Proven leadership in developing technical roadmaps, mentoring engineers, and driving measurable improvements in model performance and system reliability.
### Desirable
- 10\+ years of experience in ML/DL for autonomous driving or ADAS systems.
- Experience utilizing Vision\-Language Models (VLMs) and/or Foundation Models for auto\-labeling and long\-tail (edge\-case) detection.
- Working knowledge of localization and state estimation concepts (e.g., SLAM, sensor fusion).
- A proven record of inventions and/or publication record at top\-tier conferences (e.g., CVPR, NeurIPS, ICCV, ECCV, ICLR).
Physical Requirements
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- Standard office working conditions which includes but is not limited to:
- + Prolonged sitting
+ Prolonged standing
+ Prolonged computer use
- Travel required? \- Moderate: 11%\-25%
Benefits and Perks
- Comprehensive healthcare suite including medical, dental, vision, life, and disability plans. Domestic partners who have been residing together at least one year are also eligible to participate.
- Health Savings and Flexible Spending Healthcare and Dependent Care Accounts available.
- Rich retirement benefits, including an immediately vested employer safe harbor match.
- Generous paid parental leave as well as a phased return to work.
- Flexible vacation policy in addition to paid company holidays.
- Total Wellness Program providing numerous resources for overall wellbeing
Don't meet every single requirement? Studies have shown that women and/or people of color are less likely to apply to a job unless they meet every qualification. At May Mobility, we're committed to building a diverse, inclusive, and authentic workforce, so if you're excited about this role but your previous experience doesn't align perfectly with every qualification, we encourage you to apply anyway! You may be the perfect candidate for this or another role at May.
*Want to learn more about our culture \& benefits? Check out our* *website**!*
*May Mobility is an equal opportunity employer. All applicants for employment will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity or expression, veteran status, genetics or any other legally protected basis. Below, you have the opportunity to share your preferred gender pronouns, gender, ethnicity, and veteran status with May Mobility to help us identify areas of improvement in our hiring and recruitment processes. Completion of these questions is entirely voluntary. Any information you choose to provide will be kept confidential, and will not impact the hiring decision in any way. If you believe that you will need any type of accommodation, please let us know.*
*Note to Recruitment Agencies:* *May Mobility does not accept unsolicited agency resumes. Furthermore, May Mobility does not pay placement fees for candidates submitted by any agency other than its approved partners.*
*May Mobility uses automated tools to support — but not replace — human judgment in our recruiting process, to find out more please click* *here*
Salary Context
This $235K-$285K range is above the 75th percentile 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 May Mobility, 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 ($260K) sits 21% above the category median. Disclosed range: $235K to $285K.
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.
May Mobility AI Hiring
May Mobility has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $285K - $285K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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