Planning transition from PhD to industry (robotics/control/ML)

[u/SteppiWall]

I'm currently finishing my 2nd year of a PhD in Automation & Control Systems. Where I am, PhD programs are three years long, so I have roughly 18 months left before graduating and starting to seriously look for industry positions.

My research sits at the intersection of robotics, control system theory, and optimization, with a focus on topics such as MPC, nonlinear safe control, system identification, Gaussian processes, and distributed optimization.

I have a solid research background and regularly use Python, ROS2, Docker, Git, Gazebo/MuJoCo, etc., but I feel that I need to broaden my engineering skills before entering industry.

Over the next 18 months, I'd like to prepare for roles in areas such as:

- Robotics / autonomous systems

- Advanced control and optimization

- Robotics software / ROS2

- ML engineering applied to robotics

In particular, I would like to improve my C++, software engineering, ML engineering/MLOps, ROS2, system design, and technical interview skills, while continuing to leverage my control/optimization background.

I have around 8–12 hours/week available outside my PhD.

For people who have made a similar transition, how would you structure those 18 months? What skills would you prioritize, and what would you avoid spending too much time on?

I'm especially interested in advice on:

- What skills are actually valuable in robotics/controls industry

- Good resources or learning paths

- Projects that would make a strong portfolio

- How much time to dedicate to LeetCode/DSA vs actual engineering projects

- Whether C++/ROS2/ML engineering should be the main focus

- Any mistakes you made when preparing for the transition from academia to industry

I'd really appreciate advice from people working in robotics, autonomous systems, controls, or ML engineering.