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Radar Algorithm and Embedded AI/ML R&D Engineer
vor 4 Monaten
Ready to join the Next-Gen Radar Innovations at NXP?
Become part of a highly innovative and skilled R&D innovation team developing advanced algorithmic, software, hardware, and system level solutions for future automotive radars. Be part of a growing team collaborating with team members across United States and Europe to produce pioneering technologies for solving the most challenging problems faced by our global automotive radar tier-one and car OEM customers and the emerging autonomous vehicle industry.
Job Summary:
In this role, you will be collaborating with your team leader and fellow team members to develop differentiating algorithms running on automotive radar processors. You will demonstrate excellent understanding of the nature of the problems given and are able to frame engineering problems as mathematical models in novel ways, develop efficient and effective strategies to solve them, leveraging sound analytical and theoretical derivation processes or data-driven approaches, and validate them in simulated and/or real environment. You will then take identified solutions, work with broader product teams to benchmark the performance and throughput and specify the requirements for implementing high-potential algorithmic solutions in the form of embedded software or in the form of a dedicated hardware running on existing or future NXP radar processors.
Key Challenges:
Success in this role means that you are or can quickly become:
- Well versed in Automotive Radar knowledge space including use cases, problem domains, functional requirements, system, analog/digital hardware, and software architecture, signal processing challenges and solutions, and the general algorithm space.
- Comfortable in identifying high-value open challenges and develop differentiating, novel, and practical algorithmic solutions in the areas such as high-resolution array angle estimation problem, MIMO waveform multiplexing/de-multiplexing algorithms, radar-on-radar interference mitigation, object detection and classification, scene understanding, semantic and instance segmentation as well as NN model optimization, etc.
- Fluent in developing working, functional, and testable prototypes using suitable modeling languages such as Matlab, Python, C/C++ and Machine Learning framework such as PyTorch and TensorFlow.
- A proficient algorithm architect who can fluently convert mathematical algorithms and models into abstract design requirements suitable for being implemented as an embedded software module or as an application specific hardware accelerator
- An effective communicator and thinker who elaborates very well the key problems and solutions to and with peers, stakeholders, collaborators, management, and customers; can bridge the knowledge gap between cross-functional team members; and can comprehend and anticipate customer needs and prescribe appropriate actions and convincing solutions.
- A proactive team player who always does own parts well, steps up to the plate when a need is perceived, do not let the buck pass without trying, and is respected and appreciated by the teammates.
- A proficient innovator who has a growing record in producing novel solutions, pioneering research proposals, peer-reviewed publications, and patents.
Cross functional aspects:
In this role you are expected to:
- Work closely with senior technical leads in carrying out approved research plans and identified team visions and assist in the development of new plans and visions and be accountable to the team leads.
- Support Software R&D team to properly hand-off algorithms for performance and throughput benchmarking and software productization.
- Support Digital IP team to map mathematical algorithms and Neural Net models to hardware and new accelerator IPs.
- Support System Solution team to integrate modular algorithms with a full system stack.
- Support external collaboration activities with university research teams and industry partners.
Job Qualifications:
Competencies we are looking for:
- MSc in Electrical Engineering, Electronics Engineering, Computer Science, Computer Engineering, Applied Mathematics, Physics, etc… plus 3 years of relevant working experience or a PhD graduate in related fields.
- Experience with radar algorithm optimizations in simulation environments.
- Experience with FMCW radar signal processing techniques
- Experience with DSP, advanced linear algebra, detection and estimation theories, array processing, and modern sparse estimation techniques
- Fluent in Matlab.
- Strong communications, documentation, and presentation/public speaking skills.
- Attentive to details, self-motivated, enjoying solving difficult challenges, and able to be a team player while working independently.
- Experienced with and flexibility to work with multi-site, multi-time zone, and multi-cultural environments and able to travel as needed.
Additional competencies you may bring to stand out as an Embedded DSP specialist:
- Experience in C/C++, Embedded C
- Experience in multi-disciplinary areas (Arm, SoC, Software enablement, Tools, Platform-HW, Linux, RTOS, Drivers etc.)
- Experience in developing software on embedded systems with an HLOS, cross-compilation.
- Experience with setting up custom toolchains with IDEs (Eclipse based), Compilers (GCC etc), and debuggers (GDB) including HIL testing (JTAG)
- Experience with HIL testing and embedded system software development using custom APIs
- Experience with Software Development in embedded Linux environment
Additional competencies you may bring to stand out as an Embedded AI/ML specialist:
- Knowledge in Machine learning/neural network principles and programing models.
- Knowledge in state-of-the-art NN architecture including CNN, LSTM, Transformers, MLPs, hybrid NN, unrolled NN, and physics driven NN designs.
- Knowledge of AI model optimization techniques like quantization, pruning etc.
- Knowledge in modern NPU architecture and building blocks for hardware acceleration for AI/ML.
- Fluent in Python and ML frameworks like PyTorch and Tensorflow
- Fluent in C/C++
- Experience with embedded ML tools like TFlite, TFlite micro, ONNX toolchains / runtime
- Experience with Ubuntu or any Debian OS
- Experience with Robot Operating System (ROS)
- Experience with NXP’s custom ML toolchains (NXP eIQ Auto)
- Experience in deploying AI model on embedded hardware
More information about NXP in Germany...
