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$15

$28

$52

How much do cycling infrastructure jobs pay per hour?

As of Jul 16, 2026, the average hourly pay for cycling infrastructure in the United States is $28.01, according to ZipRecruiter salary data. Most workers in this role earn between $21.88 and $30.29 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Cycling Infrastructure Planner, and why are they important?

To thrive as a Cycling Infrastructure Planner, you need expertise in urban planning, civil engineering, and transport policy, often supported by a relevant degree or professional certification. Familiarity with GIS software, CAD tools, and traffic modeling systems is commonly required. Strong communication, stakeholder engagement, and problem-solving skills help in addressing community needs and coordinating with multidisciplinary teams. These competencies are crucial for designing safe, efficient, and sustainable cycling networks that promote active transportation and enhance urban mobility.

What are some common challenges faced by professionals working in cycling infrastructure planning and how can applicants prepare to address them?

Professionals in cycling infrastructure often encounter challenges such as balancing the needs of cyclists with existing traffic, securing funding for projects, and navigating local regulations or public resistance. Applicants can prepare by familiarizing themselves with urban planning principles, learning about stakeholder engagement strategies, and staying updated on best practices for designing safe, accessible cycling networks. Experience collaborating with transportation agencies, advocacy groups, and community members is also valuable, as these projects typically require cross-disciplinary teamwork.

What is cycling infrastructure?

Cycling infrastructure refers to the network of physical structures and facilities designed to support and promote safe and efficient cycling. This includes bike lanes, cycle tracks, bike parking, signage, traffic signals for cyclists, and bike-sharing stations. Well-designed cycling infrastructure encourages more people to cycle by improving safety, connectivity, and accessibility. It also helps reduce traffic congestion and supports healthier, more sustainable urban environments.

What is the difference between Cycling Infrastructure vs Bicycle Planner?

AspectCycling InfrastructureBicycle Planner
CredentialsEngineering, urban planning, transportation planningUrban planning, transportation planning, civil engineering
Work EnvironmentConstruction sites, urban areas, government agenciesOffice, field surveys, community engagement
Industry UsageDesign and development of bike lanes, paths, and facilitiesPlanning and policy development for cycling networks

While Cycling Infrastructure focuses on the physical creation of bike facilities, Bicycle Planners develop strategies and plans to integrate cycling into urban transportation systems. Both roles often collaborate but differ in scope: one designs the infrastructure, the other plans its implementation and policy.

More about Cycling Infrastructure jobs
What job categories do people searching Cycling Infrastructure jobs look for? The top searched job categories for Cycling Infrastructure jobs are:
Cycling Infrastructure jobs near you
Senior Machine Learning Infrastructure Engineer, Simulation

Senior Machine Learning Infrastructure Engineer, Simulation

Waymo

San Diego, CA

$115K - $156K/yr

Other

Re-posted 14 days ago

Job description

The Simulation ML Infrastructure team builds scalable AI/ML infrastructure to accelerate the Simulator team in sustainably innovating and building state of the art simulations of realistic environments for the testing and training of the Waymo Driver. To increase the fidelity and steerability of the simulations, we employ large foundation models trained on massive datasets to model the real world, including but not limited to, realistic agents (vehicles, pedestrians, cyclists, motorcyclists etc.), roads, traffic control systems, and weather etc.

We seek an experienced Senior Machine Learning Infrastructure Engineer to lead the development of advanced AI/ML infrastructure for multi-billion parameter foundation models in ML accelerator-friendly simulations. Your expertise in massive model scaling, ML accelerators, and distributed training will be required for designing and scaling our systems.

This role reports to an Engineering Manager.

You will:

  • Be part of a world-class, high-performing research engineering team to advance the state of the art of ultra realistic multi-agent simulations using foundation models.

  • Collaborate closely with the core Google DeepMind and Waymo Realism Modeling teams in London, and Waymo Oxford to use the large models to improve sim realism.

  • Provide deep technical leadership on large-scale ML model architectures, especially for autonomous vehicle models. Work at the intersection of data engineering, model development, and deployment, and provide guidance on architectural decisions and technical directions. Own large, complex systems, driving architectures that meet technical and business objectives.

  • Design and scale large distributed systems covering the ML lifecycle, supporting planet-scale dataset generation and model training.

  • Collaborate cross-functionally to derive performance and system-level requirements for large ML systems. Translate product/business goals into measurable technical deliverables, ensuring system component alignment.

  • Mentor junior engineers, growing their expertise and fostering a collaborative culture.

You have:

  • BS in Computer Science, Robotics, similar technical field of study, or equivalent practical experience

  • 5+ years of professional software engineering experience, with at least 3 years in machine learning infrastructure such as developing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.

We prefer:

  • 10+ years of professional software engineering experience, with at least 5 years in machine learning infrastructure such as developing, designing, scaling, training, deploying, and optimizing large-scale machine learning systems from data to model.

  • Solid experience in the development and optimization of machine learning infrastructure tools like DeepSpeed, PyTorch, TensorFlow, or similar frameworks.

  • Strong expertise in distributed training techniques, including gradient sharding and optimization strategies for scaling large models across ML accelerator profiling tools to uncover performance bottlenecks.

  • Deep understanding of state-of-the-art machine learning models such as auto-regressive transformers and familiarity with custom-kernels for diverse h/w compute based efficiency.

  • Excellent communication skills, both verbal and written, with the ability to translate complex technical concepts for a broad audience.

  • Practical familiarity in Autonomous Driving, Simulations, and ML accelerators is a plus.

Apply