Interested in this AI/ML Engineer role at Cyvl?
Apply Now →Skills & Technologies
About This Role
ABOUT CYVL
The best infrastructure is invisible. You only notice it when it fails. For decades, the people responsible for our roads, bridges and sidewalks have been asked to manage the physical world with tools that can't see it.
Cyvl is the Physical AI company changing that. We build purpose-built sensors, computer vision and AI that turn every drive into current data: what exists, where it is and what condition it's in. 500+ cities and towns use our Infrastructure Intelligence Platform to direct the work and fix problems before they fail. 1 in 30 Americans drive on roads managed with Cyvl data.
The proof isn't a dashboard. It's a smoother commute, a safer walk to school and public dollars going where they matter most. We're a fast-moving team in Boston building hardware, software and AI for the real world. If you believe technology's highest calling is public good, come build with us.
THE ROLE
Cyvl has one of the largest proprietary datasets of US infrastructure: LiDAR and imagery from hundreds of cities. As an AI Engineer, you'll turn that data into intelligence, from computer vision models that detect and assess assets to LLM-powered agents and tools that let customers and partners ask questions of the physical world. You'll work from prototype to production, shipping models and AI features customers use every day. This role is based in Somerville, MA, or remote from San Francisco, CA.
WHAT YOU'LL DO
- Build and improve computer vision and 3D perception models that detect, classify, and assess infrastructure (pavement, signs, sidewalks, trees) from LiDAR and imagery.
- Build LLM-powered products: agentic workflows, our MCP server, and AI copilots that put Cyvl data to work inside customer tools.
- Own models from data and training through evaluation, deployment, and monitoring in production.
- Design evaluation sets and metrics that tie model performance to what customers actually need.
- Stay close to the state of the art and bring the right new tools into our products and our own workflows.
WHAT YOU BRING
- 3+ years building and shipping ML or AI systems in production
- Strong Python and a modern deep learning framework (PyTorch preferred)
- Hands-on experience with computer vision, 3D perception, or LLM applications (RAG, tool use, agents, evals)
- Solid software engineering fundamentals: testing, code review, and cloud deployment
- Ability to go from an ambiguous problem to a working prototype quickly, then make it production-grade
NICE TO HAVE
- Point cloud or multimodal work (LiDAR and imagery fusion)
- Model Context Protocol (MCP), foundation model APIs, or agent frameworks
- Geospatial or remote sensing background
- MLOps: model serving, GPU infrastructure, experiment tracking
- Advanced degree in CS, ML, robotics, or a related field, or equivalent experience
WHY CYVL
Hard tech. Real streets. Your name on it.
- Hard problems in the real world. You build end-to-end: our own hardware, software and AI, with work that shows up on the street in the real world.
- Ownership that compounds. You own outcomes, not tasks. You work directly with customers and across our team, and you'll own more, sooner, than almost anywhere else.
- Intense, with heart. We're building a category, so it's not a place to coast. We also take care of each other, have genuine fun together, and believe in what we are doing.
WHAT WE ASK
- Cross lanes. "Not my job" doesn't exist here.
- Own without a playbook. The plan is still being written, often by you.
- Try, miss, try again. We make big bets. Some won't land, and we learn fast.
- Be in the room. We're on-site in Somerville about 90% of the time. If you're based in San Francisco, this role is remote, and you'll be with the team in Somerville for the moments that matter like onboarding, kick-offs, and milestones.
We're a startup building a category. If that sounds like the draw, not the risk, we'd love to talk.
BENEFITS
- Equity. You'll own a piece of what you're building.
- Health, dental, and vision. Cyvl covers most of the premium for you and your family.
- 401k with a real match, vested day one. Your money is yours from the start.
- Generous PTO & Paid Parental leave. Doing hard work well includes stepping away from it.
- Learning stipend. Spend it on whatever makes you sharper.
- Paid to live close. A monthly bonus for living near our Boston office.
- Team events. Time together in Boston with a team you’ll love working with.
OUR VALUES
- Real: Authentic and transparent. Focused on solving real problems with simple, effective solutions.
- All Out: Bold, driven, and committed to delivering results that matter.
- Accelerating: Always learning, growing, and moving with intention.
- Reliable: We follow through and deliver at a consistently high bar.
- Empowering: We lift each other up and help governments build infrastructure they can be proud of.
SEE OUR WORK
- How Atlanta cut downtown potholes by 60%
- How Buffalo turned a campaign promise into a paving plan
- How to rebuild US infrastructure smarter
We build for every community, so we hire from all of them. The best people for this work come from different backgrounds, paths, and places, and many of them don't check every box. If this role excites you, apply anyway. If you need an adjustment to our interview process, tell us and we'll make it work.
Compensation Range: $125K - $225K
Salary Context
This $125K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Cyvl, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($175K) sits 17% below the category median. Disclosed range: $125K to $225K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Cyvl AI Hiring
Cyvl has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Somerville, MA, US. Compensation range: $225K - $225K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.