Hardware, Edge Ml Engineer

About Mecka AIMecka AI is building the data infrastructure layer for robotics and embodied AI.We design and operate 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.We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware.
The RoleYou'll put advanced computer vision into the hardware fleet that captures experience for robot learning. You'll train models, make them run on embedded devices, and carry their accuracy from controlled evaluation into real operating conditions, all within strict limits on latency, memory, and power. The role can include technical leadership of model development and deployment, and either way you'll be directly responsible for implementation and evaluation.
What You'll Do
Train and evaluate computer-vision models for deployment on embedded devices.
Build efficient C++ inference pipelines and integrate them with embedded Linux software.
Optimize models and runtime performance for latency, memory, power consumption, and sustained operation.
Test performance on representative data and hardware, and investigate errors across preprocessing, inference, and integration.
For the lead position: set architecture and evaluation standards while continuing to develop models and software.
What You BringMust-have:
Strong Python and C++ programming skills, including debugging and performance profiling.
Experience training and evaluating computer-vision models with a framework 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.
A Note on ApplyingStudies show women and candidates from underrepresented groups often only apply when they meet 100% of the listed qualifications, while others apply after meeting 60%.
Inclusive Hiring at MeckaWe are committed to creating an inclusive and supportive candidate experience. Should you require any accommodation whatsoever during the interview process, please inform us without any hesitation. Mecka is dedicated to ensuring equal treatment and opportunity in all phases of recruitment, selection, and employment, in compliance with employment law. We do not discriminate based on gender, race, religion, national origin, ethnicity, disability, gender identity/expression, sexual orientation, veteran or military status, or any other protected category. Mecka is proud to be an equal opportunity employer, fostering a culture of inclusivity and maintaining a work environment that is free from discrimination, harassment, and retaliation.
Use of Artificial Intelligence in RecruitmentMecka uses artificial intelligence (AI) responsibly to support administrative and efficiency-focused aspects of our recruitment process. This includes activities such as drafting job descriptions, generating interview questions, note-taking and recordings, and supporting sourcing and scheduling workflows. All candidate evaluations, interviews, and hiring decisions are made by members of the Mecka team. While AI tools may assist with screening and assessment, they do not replace human judgment in selection decisions. Our use of AI is intended to streamline routine tasks, improve consistency, and enhance the overall candidate experience. We are committed to upholding principles of fairness, transparency, and accountability in all hiring activities. Mecka regularly reviews its recruitment practices to mitigate bias and to ensure alignment with applicable laws and evolving best practices.
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