Introduction: A Career at HARMAN Automotive

We’re a global, multi-disciplinary team that’s putting the innovative power of technology to work and transforming tomorrow. At HARMAN Automotive, we give you the keys to fast-track your career.

• Engineer audio systems and integrated technology platforms that augment the driving experience
• Combine ingenuity, in-depth research, and a spirit of collaboration with design and engineering excellence
• Advance in-vehicle infotainment, safety, efficiency, and enjoyment

About the Role:

This position is part of AES within HARMAN Automotive’s Ready Care project, focused on delivering production‑ready in‑cabin camera features (such as driver monitoring, child presence detection, and occupant classification) that enhance safety and well‑being for drivers and passengers. We are seeking a hands-on and versatile engineer to develop advanced image processing and AI-based features. We expect natural technological curiosity, strong initiative, and a commitment to taking full end-to-end product ownership—from initial concept to deployment. Experience as a Product or Feature Owner is highly desirable, allowing you to seamlessly bridge the product vision with technical execution. You will work closely with domain experts, data scientists, embedded developers, and product managers to build complex machine learning (ML) systems that solve real-world problems in production.

What You Will Do:

• Lead end-to-end product feature development: Take ownership of ML-based systems and algorithms from early research to production delivery, ensuring alignment with overall product goals.
• Act as a Feature Owner: Drive the scope, vision, and execution of specific camera-based features, serving as the primary point of contact for technical and product decisions in your area.
• Drive innovation and IP creation: Lead the development and maturation of new machine/deep learning technologies and regularly drive the creation of intellectual property.
• Architect complex ML systems: Own and align the ML architecture for multiple sub-features and common functions across the organization.
• Collaborate cross-functionally: Work closely with subject matter experts, software developers, and product managers to evaluate new model architectures, training approaches, and compute resource requirements.
• Ensure product success & accountability: Take direct accountability for meeting target performance KPIs based on customer, safety, and product requirements.
• Shape the technology vision: Own the ML technology roadmap and continuously refine the ML development process for your area.
• Foster external collaborations: Drive technical partnerships with cross-functional teams, external partners, and hardware/software vendors.

What You Need to Be Successful:

• Education: Master’s degree in a relevant field (Electronics, Computer Science, Electrical Engineering, or related technical discipline).
• Experience: 8+ years of relevant industry experience in machine learning and software engineering.
• Ownership mindset: Proven track record of taking full accountability for deliverables, exhibiting high curiosity about what and why features are built, and proactively driving solutions to real-world production challenges.
• Domain expertise: Strong conceptual understanding of image/video processing, computer vision, and deep learning techniques.
• Edge deployment: Demonstrated knowledge of ML/DL algorithms optimized for edge execution (efficient architectures, model quantization, pruning, etc.).
• Coding proficiency: Strong programming skills in Python and C++.
• ML stack: Solid hands-on familiarity with PyTorch and standard data science libraries.

Bonus Points if You Have:

• Product ownership experience: Formal experience or background as a Product Owner, Feature Owner, or System Architect within Automotive or IoT domains.
• Advanced degree: Master’s by Research or PhD in Computer Vision, Machine Learning, or related fields.
• Publications & patents: Track record of authoring peer-reviewed publications or patents.
• Automotive AI context: Experience in automotive AI projects, including managing data collection requirements, edge validation, and safety-critical standards.
• Hardware awareness: Understanding of computer architecture, embedded systems, and hardware accelerators (GPUs, NPUs, SoCs).
• Target platform deployment: Hands-on experience deploying and optimizing deep neural networks on edge platforms from Qualcomm, NVIDIA, Samsung, or similar vendors.

What Makes You Eligible:

• Willingness to work in a hybrid model, 3 days per week from our Łódź office.