I work in robotics. I am currently enrolled in a top ~3 robotics program in the US, working on robot learning. Previously I worked on different robotics problems including: heuristic based path-planning, multi-robot systems, UAVs, SLAM etc. Through my research, I've gained experience in Python and to a lesser extent in C++ as well. Prior to joining the grad program I worked as a Research Intern in a US semiconductor company's research division, on developing algorithms for SLAM (I developed kalman filters, keypoint extraction and map storage/retrieval). Most of these projects were "systems projects", implying that the primary innovation was combining exisiting algorithms/methods into a complete end-to-end-system, rather than novel research contributions, but hey I'm not complaining! I really enjoy systems work turns out, which brings me to my point...
This brief stint showed me the stark contrast between academia and industry. It has me wondering if I'm much better for industry, since I feel my short and long-term incentives are much more aligned in industry rather than academia.
So I want to ask HN: How do I prepare for similar robotics/deep learning/computer-vision/machine learning jobs in industry? There seem to be a ton of advice for people venturing into SWE/SDE roles but not that much for "Research Engineer" positions.
Do I hit LeetCode? Do I improve my webpage? Do I start a blog? If I need to grind at LeetCode, what kind of challenges should I be focusing on? The keywords that I'd use to describe my skillset would include: Python/C++/TensorFlow2/Pytorch/OpenCV/ROS
Any and all advice is highly appreciated!
PS: If you think I would fit well with your organization, please let me know! I often feel like organizations wouldn't want "generalists" and instead prefer "specialists".
-transitionAcad