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Mecka AI is building the data infrastructure layer for robotics and embodied AI. The company designs and operates global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
In this role, you will deploy advanced computer vision into embedded hardware that captures experience for robot learning. You will train models, optimize them for embedded devices, and ensure their accuracy transfers from controlled evaluation into real operating conditions—all within strict constraints on latency, memory, and power consumption.
Key responsibilities include:
- Training and evaluating computer-vision models for deployment on embedded devices
- Building efficient C++ inference pipelines and integrating them with embedded Linux software
- Optimizing models and runtime performance for latency, memory, power consumption, and sustained operation
- Testing performance on representative data and hardware, investigating errors across preprocessing, inference, and integration
- For the lead track: setting architecture and evaluation standards while continuing hands-on model and software development
This role offers a path toward technical leadership of model development and deployment, with direct responsibility for both implementation and evaluation.
REQUIREMENTS
Must-have:
- Strong Python and C++ programming skills, including debugging and performance profiling
- Experience training and evaluating computer-vision models with frameworks such as PyTorch
- Experience developing on embedded Linux and deploying models on resource-constrained hardware
- Understanding of quantization, model conversion, and inference optimization on CPUs, GPUs, or NPUs
- Ability to evaluate accuracy, false positives, and missed detections across changing lighting, motion, and operating conditions
- For the lead position: experience making architecture decisions and guiding engineers through model development and deployment
Nice-to-have:
- Experience with accelerator runtimes, custom operators, or camera and video pipelines
- Experience with dataset curation and rigorous evaluation of computer-vision models
