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About This Role
The Principal Technical Product Manager I, Machine Learning leads high\-impact initiatives at the intersection of machine learning, telematics, and product innovation within CMT's DriveWell Fusion Platform. This role is responsible for shaping and executing a data\-centric roadmap—from signal processing and data cleansing to AI\-powered insights—while working closely with engineering, customers, and cross\-functional teams. With deep technical expertise and strong product instincts, you'll turn complex data into actionable features that improve user safety, experience, and engagement. This is a strategic, hands\-on role for a product leader passionate about solving real\-world problems through data.
CMT is looking for a Principal Technical Product Manager I, Machine Learning to help us change the world. CMT has helped protect over 65 million drivers and prevent over 126,000 crashes worldwide. We build AI to solve some of the most difficult challenges in mobility — understanding and reducing risk, detecting crashes, and getting people life\-saving help. The problems are hard. The impact is real. No matter your role, your work will matter at CMT.
Responsibilities:
- Use independent judgment and discretion to own and manage the development and execution of product roadmaps within the DriveWell Fusion
- Platform, with a strong emphasis on features related to telematics data cleansing, filtering, and advanced signal processing
- Spearhead the design and implementation of AI/ML models and algorithms that generate actionable insights about driving and user behavior, mode of transportation, and other key metrics
- Collaborate with cross\-functional teams to integrate and optimize map data (e.g., OpenStreetMaps) for enhanced location\-based services and analysis within the platform.
- Serve as the primary point of contact for your product, interfacing with customers, the go\-to\-market team, and engineering to gather requirements and feedback, ensuring alignment of priority and scope
- Develop a deep understanding of customer needs and leverage independent judgment to create a compelling vision for addressing those needs through innovative product solutions
- Collaborate closely with engineering and design teams, utilizing your technical expertise to guide the development and delivery of high\-impact features within the DriveWell Fusion Platform
- Conduct in\-depth analysis using tools such as Databricks and Tableau to derive insights that inform product decisions and identify new opportunities
- Maintain a customer\-centric, data driven approach, continuously seeking feedback and insights to enhance the user experience and drive product adoption
- Establish yourself as a trusted advisor to customers, providing guidance and expertise on leveraging telematics solutions to achieve their goals
- Proactively address emerging challenges and opportunities, adapting quickly to evolving business needs and priorities
- Complete any additional tasks as they arise
Qualifications:
- Bachelor's degree or equivalent years of experience and/or certification in a technical or quantitative field
- 7\+ years in Product Management, Data Science, Analytics or related field
- Proven track record in big data analytics within the mobility sector, particularly experience and familiarity with telematics and geospatial data
- Strong communication and negotiation skills with the ability to influence stakeholders
- Extensive experience in data consulting or advisory roles in front of customers
- Working knowledge of distributed compute and data pipelines used in training large transformer models.
- Strong project management / program management skills to keep initiatives on track
- Ability to adapt to changing business needs and prioritize efforts against the challenges at hand
- Scrappy, positive "impact\-focused" attitude: you don't hesitate to take initiative and address something hands\-on
- A passion for using data to make our streets and highways safer
Compensation and Benefits:
- Fair and competitive salary based on skills and experience, and annual performance bonus
- Equity may be awarded in the form of Restricted Stock Units (RSUs)
- Medical, Dental, Vision and Life Insurance, matching 401k, short\-term \& long\-term disability and parental leave
- Unlimited Paid Time Off including vacation, sick days \& public holidays
- Flexible scheduling and work from home policy depending on role and responsibilities
Base Salary Range
- The base salary range for this position is: $141,100 to $ 176,400\. This range is specifically for Cambridge, MA
Additional Perks:
- Work on a mission with real impact: crashes prevented, injuries avoided, lives protected around the world
- Join an industry leader — 65 million drivers protected, powering 140\+ programs across 25 countries
- Recognized innovator in mobility AI, earning top honors including the TIME Industry Leader in AI, a Gold Edison Award, and the Artificial Intelligence Excellence Award for AI for Social Good. CMT is also Great Place to Work Certified
- Be part of the team inventing the future of mobility and road safety
- Move fast, own outcomes, do work that matters
- High ownership, small teams, and direct access to leadership — no layers between your work and its impact
- Unlimited PTO, flexible scheduling, competitive salary, annual performance bonus, RSUs, and full benefits including medical, dental, vision, and 401k match
- Summer Fridays provide team members with half days to recharge
- Join one of our employee resource groups: Black, AAPI, LGBTQIA\+, Women, Book Club, and Health \& Wellness
- Comprehensive wellness, education, and employee assistance programs
Commitment to Diversity and Inclusion:
At CMT, we believe the best ideas come from a mix of backgrounds and perspectives.
We are an equal\-opportunity employer committed to creating a workplace and culture where everyone feels valued, respected, and empowered to bring their unique talents and perspectives. Diversity is essential to our success, and we actively seek candidates from all backgrounds to join our growing team.
We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status or disability state. CMT is headquartered in Cambridge, MA. To learn more, visit www.cmtelematics.com and follow us on Instagram @cmt.ai
About Cambridge Mobile Telematics:
Cambridge Mobile Telematics (CMT) is the world's largest telematics and AI company for safer mobility. Its mission is to make the world's roads and drivers safer. The company's AI\-driven platform, DriveWell Fusion®, proactively identifies and reduces driving risk, leading to fewer crashes and injuries. To date, CMT's technology has helped prevent over 126,000 crashes worldwide. CMT enables partners to measure risk, detect crashes, provide life\-saving assistance, and streamline claims. Headquartered in Cambridge, MA, CMT operates globally with offices in Budapest, Hungary; Chennai, India; Seattle, Washington; Tokyo, Japan; and Zagreb, Croatia. Learn more at www.cmt.ai.
Salary Context
This $141K-$176K 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 Cambridge Mobile Telematics, 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 ($158K) sits 26% below the category median. Disclosed range: $141K to $176K.
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.
Cambridge Mobile Telematics AI Hiring
Cambridge Mobile Telematics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cambridge, MA, US. Compensation range: $176K - $176K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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