Edge AI Engineer (Embedded)

TL;DR

Edge AI Engineer (Embedded): Developing and optimizing ML models for computer vision, sensor fusion, and signal processing, deployed on edge hardware platforms with an accent on quantization, DSP/NPU acceleration, and real-time analytics. Focus on building data ingestion, preprocessing, and feature-engineering pipelines for both edge and hybrid deployments.

Location: Remote

Company

hirify.global is a global leader in providing innovative technology solutions for businesses across industries, specializing in developing custom software and hardware solutions, especially connected products within consumer electronics and IoT devices.

What you will do

• Design, train, and validate ML models for computer vision, sensor fusion, signal processing, and predictive analytics.
• Develop and optimize ML pipelines for on‑device inference, including quantization, power/performance tuning, and DSP/NPU acceleration.
• Build data ingestion, preprocessing, and feature‑engineering pipelines for both edge and hybrid (Edge + Cloud) deployments.
• Extract, process, and analyse large datasets to generate actionable insights and continuously improve model performance.
• Work with cross‑functional teams to develop and integrate ML functionality into real products.
• Participate in technical discussions, document your work, and clearly explain the trade-offs and decisions behind the solutions you present.

Requirements

• Strong hands‑on experience in Python, ML frameworks such as PyTorch or TensorFlow, and classical CV libraries (OpenCV, scikit‑learn).
• Ability to build and deploy ML models for Edge or Embedded platforms, preferably with experience on Qualcomm, Nordic, NXP, or similar SoCs.
• Familiarity with quantization, model compression, benchmarking, and inference profiling on constrained hardware.
• Experience with data pipelines, including data validation, augmentation, and performance analysis.
• Understanding of end‑to‑end ML lifecycle, including experimentation, evaluation, and deployment in production environments.
• Master’s or PhD in ML, Robotics, Autonomous Systems or related fields and 2+ years of hands-on experience developing and deploying ML models in production.

Nice to have

• Knowledge of MLOps, FastAPI, Docker, CI/CD, and cloud platforms such as Azure or AWS.

Culture & Benefits

• Work with industry-defining technologies in terms of applied research and pushing functional boundaries.
• Work in a vibrant environment with technical experts from 25 nationalities.
• Maintain a healthy work-life balance with flexible work hours and remote work options.
• Enjoy a healthy work-life balance with 25 days of annual paid vacation.
• Receive a competitive salary that aligns with industry standards.
• Benefit from a dedicated yearly health and wellness allocation.