Application Guide
How to Apply for Senior Embedded Vision Engineer
at Lime
🏢 About Lime
Lime is at the forefront of sustainable urban mobility, operating a vast network of shared e-scooters and e-bikes in over 200 cities globally. As a mission-driven company, Lime offers a unique opportunity to directly impact how people move in cities, reducing car dependency and carbon emissions. Working here means contributing to a tangible, environmentally positive product that millions rely on daily.
About This Role
As a Senior Embedded Vision Engineer at Lime, you will be responsible for developing and deploying computer vision models that run in real-time on the scooters' embedded hardware. Your work will enable features like object detection, path planning, and rider assistance, directly enhancing safety and user experience. This role is critical for advancing Lime's autonomous capabilities and making shared micro-mobility smarter and safer.
💡 A Day in the Life
A typical day might start with a stand-up to sync with the remote team, then diving into optimizing a YOLO model for the Ambarella platform using TensorRT. You might spend the afternoon analyzing telemetry data from field tests to identify a latency spike, collaborating with a firmware engineer to adjust camera triggering, and end the day documenting your findings for the team.
🚀 Application Tools
🎯 Who Lime Is Looking For
- Has 5+ years of industry experience deploying computer vision models on embedded platforms like NVIDIA Jetson or Ambarella, with proven ability to optimize for real-time inference under strict latency and power constraints.
- Is proficient in C/C++ and Python, with deep experience in performance tuning (e.g., using CUDA, TensorRT, or OpenCL) on resource-constrained devices.
- Understands multi-sensor fusion (camera, IMU, GPS) and has experience integrating vision with other sensors for robust state estimation in dynamic outdoor environments.
- Is comfortable working remotely and collaborating across time zones, with strong communication skills and a track record of delivering complex embedded systems projects.
📝 Tips for Applying to Lime
Tailor your resume to highlight specific embedded deployment projects: mention the hardware (e.g., Jetson TX2, Ambarella CV2), the model optimization techniques used (quantization, pruning, TensorRT), and the performance metrics achieved (latency, FPS, power draw).
In your cover letter, explicitly connect your experience to Lime's mission: explain how your work on edge AI can improve scooter safety (e.g., pedestrian detection, sidewalk riding prevention) and reduce accidents.
If you have open-source contributions or a portfolio of embedded vision projects (e.g., on GitHub), include links. Showcasing a project that runs on a low-power device will stand out.
Prepare to discuss trade-offs between accuracy and latency: Lime's models must run in milliseconds on battery-powered hardware. Be ready to explain how you've balanced these in past roles.
Research Lime's current scooter models and any public information about their onboard sensors. Mentioning specific hardware (like a particular IMU or camera) in your application shows genuine interest.
✉️ What to Emphasize in Your Cover Letter
['Emphasize your hands-on experience deploying CV models on embedded platforms, especially under real-time and power constraints—this is the core of the role.', "Show passion for sustainable transportation and how your skills can directly contribute to Lime's safety and autonomy goals.", "Highlight cross-functional collaboration: you'll work with hardware, firmware, and backend teams. Give an example of such collaboration in a past project.", 'Mention any experience with multi-sensor fusion or state estimation, as the job description specifically calls out combining camera data with IMU/GPS.']
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Read about Lime's 'Lime Safety' initiatives and any publicly announced AI features (e.g., sidewalk detection, rider behavior monitoring) to understand their current tech stack.
- → Look up Lime's hardware partnerships (e.g., with Segway or Okai) to get a sense of the embedded platforms they use. Check for teardowns or specs of their latest scooters.
- → Review Lime's engineering blog or tech talks (if available) for insights into their approach to edge computing and real-time systems.
- → Understand the regulatory landscape for e-scooters in key cities (e.g., speed limits, parking rules) to appreciate the constraints your vision system must handle.
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Don't focus solely on training models without discussing deployment. This role is about productionizing on edge devices—emphasize optimization and hardware constraints.
- Avoid being vague about performance metrics. Provide specific numbers for latency, throughput, power consumption, or model size from past projects.
- Don't overlook the multi-sensor aspect. Even if your strength is pure vision, show willingness to learn sensor fusion. Ignoring IMU or GPS could signal lack of fit.
📅 Application Timeline
This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.
Typical hiring timeline:
Application Review
1-2 weeks
Initial Screening
Phone call or written assessment
Interviews
1-2 rounds, usually virtual
Offer
Congratulations!