About
We are looking for software engineers with deep expertise in radar sensors and using radars in ML-first perception solutions for autonomous vehicles or mobile robots. You will be an early member of the fast-growing autonomy team in Stuttgart, which means you will have the opportunity to take broad responsibilities and impact the directions of the overall program.
In addition to your engineering contributions, by working in our dynamic and customer-focused team culture, you will contribute to and learn from best practices in the autonomy industry. We move fast and focus on excellence, for our products and for our business. If you are hands-on and looking for a place to have a multiplying effect on making autonomous systems a reality, Applied Intuition is the place for you
At Applied Intuition, you will:
Integrate data from existing or new radar sensors into our ML networks.
Utilize the strengths of radar for autonomy stacks: designing the right pre-processing & best model architectures, tuning performance on real data from our data collection & testing fleet, but also our simulation tooling.
Use and further improve Applied's excellent data , machine learning and simulation tooling.
Build real autonomy products made for real-world applications , within a fast moving and growing team.
We're looking for someone who has:
Deep expertise in understanding radar sensor and how to use them in ADAS/AD.
Experience with the end-to-end development cycle of deep learning models.
Expertise in subdomains such as modeling, input pipelines, evaluation, deployment, and model optimization.
3+ years of experience building production software using modern software practices.
Fluency in C++, or fluency in Python with intermediate experience in C++.
Deep understanding of the concepts and methods behind any frameworks or libraries that they worked with.
Experience working with production level ML and DL perception algorithms for autonomous vehicles.
Nice to have:
MSc or PhD in machine learning, ideally applied to perception, prediction, planning or closely related field.
Experience building and shipping software frameworks or tools.
Experience with driver assistance or autonomous driving systems.
Experience in evaluating and improving system-in-the-loop model performance.
Deep hands-on expertise in relevant algorithms or methods, such as non-linear optimization, computational geometry, numerical analysis, or distributed systems.
Languages
- English
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