We believe that the future of transportation is automated. Automated travel will be safer, more comfortable, more efficient and a powerful economic enabler for our communities. However, automating driving is a massively complex engineering challenge, requiring vehicles to navigate social norms, regional traffic patterns, unpredictable weather incidents, and a host of anomalous events. While billions of dollars have already been spent trying to solve this problem, a comprehensive answer remains frustratingly elusive. We believe that the final answer lies with roadway infrastructure.

Join us in building the roads of the future. Cavnue, which in April 2022 announced the closing of its Series A at $130M, is bridging technology and road infrastructure to realize a safer, more efficient, and more accessible future for automated transportation. Cavnue’s experienced team sits at the intersection of technology, infrastructure, and government—working together to develop and deploy the world’s most advanced roads. We are incorporating physical and digital infrastructure that unlock the full spectrum of capabilities of current and future automated vehicle technologies. We believe in a world in which road infrastructure shares in the complexity of autonomy and, instead of being another problem to solve for, becomes a core part of the solution.

The Role 

As a Senior Machine Learning Engineer at Cavnue, you’ll bring critical skills to a team working on changing the built environment to create safe, reliable, and secure mobility experiences. We’re looking for someone who has developed machine learning models into real applications and you can help us build and solve some of the most complex, near real-time coordination problems at scales that matter to everyday people using our streets.

Role overview: 

  • Develop applied solutions for real-world, complex problems in autonomous robotics and road transportation
  • Build real-world, production-scale AI capabilities to help solve practical but critical engineering challenges. You will work with machine learning frameworks as well as modern programming languages
  • Design for each stage in the ML model lifecycle: development, training, evaluation, and deployment.
  • Work with the Product and Systems Engineering teams to ensure that the right data sets are being collected for relevant tasks at varying geospatial and temporal scales
  • Design, build and work with  high-quality ML infrastructure and data pipelines, define production code standards, conduct code reviews, and work alongside infrastructure, reliability, and hardware engineering teams.
  • Expand Cavnue’s competitive advantage by identifying, investigating, understanding and applying emerging AI and AI-adjacent technologies or developing novel ML and CS techniques.


  • Bachelor’s Degree, or higher-level degree, in Computer Science, Engineering, or related quantitative field that demonstrates knowledge of AI, machine learning, optimization, random processes, sensor signal processing, or mathematical modeling
  • 5+ of work experience, preferably in the autonomous vehicle or robotics space
  • Familiarity with latest in productionized computer vision technologies and algorithms, particularly deep learning, reinforcement learning, classification, and pattern recognition.
  • Experience developing reliable production-level applications
  • Ideal candidates will also have experience in statistical signal processing and its application to sensor data or experience leveraging the latest techniques in deep learning for computer vision
  • Experience with large-scale data structures and data pipelines, data modeling, software architectures, and the latest ML libraries and frameworks e.g. TensorFlow, Pytorch.
  • Python experience required.

Salary Range: The salary range for this position is specific to the location(s) listed below and is the range Cavnue reasonably and in good faith expects to pay for the position taking into account the wide variety of factors that are considered in making compensation decisions, including job-related knowledge; skillset; experience, education and training; certifications; and other relevant business and organizational factors.

Remote - $151,000 - $205,000

Additional Compensation: The successful candidate may be eligible to participate in Cavnue's equity program and/or a discretionary annual incentive program, subject to the rules governing such programs. (Cash or equity incentive awards, if any, will depend on various factors, including, without limitation, individual and company performance.) Note: Cavnue's benefit, compensation and incentive programs are subject to eligibility requirements and other terms of the applicable plan or program.

  • Medical, dental, and vision benefits
  • Life insurance and disability insurance
  • 401(k) with 4% company contribution - no waiting period
  • Parental and adoption leave
  • Fertility and infertility benefits
  • Wellness perks including access to on-demand primary care, virtual health appointments, and online mental health therapy
  • Peloton App One Membership
  • Home office reimbursement stipend
  • Generous PTO bank, including paid year-end holiday shutdown
  • Company-sponsored lunches twice weekly (in office)
  • Learning and development opportunities
  • Top-of-the-line equipment



We are building an incredible team of employees with diverse backgrounds and experiences. We believe that great work can occur anywhere and are open to considering candidates who meet our needs who reside outside our geographic footprint. We also value the impact that can result from co-located teams and some roles may require regular presence in one of our offices.

Cavnue is an Equal Opportunity Employer and prohibits discrimination or harassment of any kind. All employment decisions at Cavnue are based on business needs, job requirements, and individual qualifications, without regard to race, color, national origin, sex, gender, age, religion or belief, disability, sexual orientation, family or parental status, veteran status, or any other status protected by law.

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