Senior Embedded Vision Engineer at Lime

What you'd actually do

Design, train, and deploy computer vision models for real-time inference on edge devices, balancing accuracy, latency, and power constraints.

Optimize models for embedded hardware (e.g., NVIDIA Jetson, Ambarella, ARM-based SoCs), including working within vendor SDKs to meet latency, memory, and power constraints.

Identify and resolve performance bottlenecks across the full pipeline (data ingestion, preprocessing, inference, postprocessing) to meet strict real-time requirements.

Work with camera data alongside onboard sensors such as IMUs and GPS, contributing to multi-sensor fusion approaches to improve system robustness and state estimation (e.g., filtering and smoothing techniques such as Kalman filters).

Build systems that perform reliably under challenging conditions such as varying lighting, motion, occlusions, and environmental noise.

Skills

Required

Computer Vision
Machine Learning
embedded systems
C/C++
Python
edge AI
embedded platforms
model deployment and optimization frameworks
camera systems
sensor fusion
state estimation methods
computer vision techniques (detection, classification, segmentation)
real-world data challenges
debugging and problem-solving skills

Nice to have

working within vendor SDKs
SoC vendor SDKs
hardware interfaces
low-level system components
TensorRT
ONNX Runtime
OpenVINO
Kalman filters

Lime is the largest global shared micromobility business, operating in close to 30 countries across five continents. We’re on a mission to build a future where transportation is shared, affordable and carbon-free. Our electric bikes and scooters have powered more than one billion rides in cities around the world. Named a 2025 Time 100 Most Influential Company, Lime continues to set the pace for shared micromobility globally, spurring a new generation of clean alternatives to car ownership.

We are looking for a high-impact Senior Embedded Vision Engineer to help build and deploy the core perception capabilities for the Lime Vision team. In this role, you will focus on developing, optimizing, and deploying computer vision solutions that enable our vehicles to understand and interact with the real world in real time.

You will work on challenging, real-world problems in micro-mobility—such as tandem riding detection, precision parking validation, and sidewalk riding prevention—helping bring these solutions from concept to reliable deployment on edge hardware. This role requires strong expertise in Computer Vision, Machine Learning, and embedded systems, with a focus on performance, reliability, and scalability in production environments.

You will work closely with applied scientists, hardware/firmware engineers, and product teams to execute on the technical roadmap and ensure robust system performance in diverse and unpredictable real-world conditions.

This is a remote position with a requirement for candidates to reside in the United States to maintain effective collaboration across teams.

What You’ll Do:

Model Development & Deployment: Design, train, and deploy computer vision models for real-time inference on edge devices, balancing accuracy, latency, and power constraints.
Edge Optimization: Optimize models for embedded hardware (e.g., NVIDIA Jetson, Ambarella, ARM-based SoCs), including working within vendor SDKs to meet latency, memory, and power constraints.
System Performance & Profiling: Identify and resolve performance bottlenecks across the full pipeline (data ingestion, preprocessing, inference, postprocessing) to meet strict real-time requirements.
Sensor Fusion: Work with camera data alongside onboard sensors such as IMUs and GPS, contributing to multi-sensor fusion approaches to improve system robustness and state estimation (e.g., filtering and smoothing techniques such as Kalman filters).
Robustness in the Real World: Build systems that perform reliably under challenging conditions such as varying lighting, motion, occlusions, and environmental noise.
Cross-Functional Collaboration: Work closely with hardware, firmware, and applied science teams to integrate and deploy vision models on edge devices.
Evaluation & Iteration: Contribute to model evaluation and validation efforts, helping identify performance gaps and inform improvements in data collection and model design.
End-to-End Contribution: Participate in the full development lifecycle, from data understanding and model iteration to deployment and monitoring in the field.

About You:

5+ years of industry experience in Computer Vision and Machine Learning, with hands-on experience deploying models in real-world or embedded environments.
Strong programming skills in C/C++ and Python, with experience developing and optimizing applications on resource-constrained devices. Experience working with SoC vendor SDKs, hardware interfaces, and low-level system components is a plus.
Experience with edge AI and embedded platforms (e.g., NVIDIA Jetson, Ambarella, ARM-based SoCs), including performance optimization under latency and power constraints.
Familiarity with model deployment and optimization frameworks such as TensorRT, ONNX Runtime, OpenVINO, or similar.
Experience working with camera systems and additional sensors (e.g., IMU, GPS), with a practical understanding of sensor fusion techniques and state estimation methods.
Solid understanding of computer vision techniques (e.g., detection, classification, segmentation) and practical experience applying them in production.
Experience working with real-world data challenges, including noisy inputs, edge cases, and variability across environments.
Strong debugging and problem-solving skills, particularly in constrained or low-visibility environments.
Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Robotics, or a related field (or equivalent practical experience).

U.S. Based Position - What We Offer:

Comprehensive Health & Wellness: A choice of medical, dental, and vision plans. We also provide company-paid life and disability insurance and company-funded mental health benefits.
Financial & Retirement Planning: 401(k) plan with both pre-tax and Roth options, and access to a Health Savings Account (HSA) with a monthly company contribution.
Family & Fertility Support: Paid parental leave for birthing and non-birthing parents, plus fertility and family-forming benefits.
Paid Time Off: Unlimited vacation, paid leaves, and 10 company holidays.
Unique Lime Perks: Complimentary use of Lime vehicles in participating cities, a monthly phone allowance, dedicated learning and development days, and access to perks including One Medical, Wellhub, and Headspace.

The base salary range listed reflects what Lime reasonably expects to offer for this role, with the final base salary determined by objective factors such as the candidate’s location and relevant skills and experience. Depending on the position, the total compensation package may also include discretionary annual performance bonus opportunities and equity, subject to applicable plan terms and eligibility requirements.

Lime considers all qualified applicants for employment, including those with arrest or conviction records, in accordance with the San Francisco Fair Chance Ordinance, the Los Angeles Fair Chance Initiative for Hiring, the Los Angeles County Fair Chance Ordinance, the California Fair Chance Act, and all other applicable federal, state, and local laws.

#LI-PP1

#LI-Remote

If you want to make an impact, Lime is the place for you. Not sure if you meet all the qualifications? If this role excites you we encourage you to apply. Explore all opportunities on our career page.

Lime is proud to be an Equal Opportunity Employer. We believe different perspectives help us grow and achieve more. That’s why we’re dedicated to building and developing a team that reflects a wider range of backgrounds, abilities, identities, and experiences. If you require a reasonable accommodation during the application or hiring process, please email recruiting-operations@li.me for assistance.