[D] Physicist-turned-ML-engineer looking to get into ML research. What's worth working on and where can I contribute most?

After years of focus on building products, I'm carving out time to do independent research again and trying to find the right direction. I have stayed reasonably up-to-date regarding major developments of the past years (reading books, papers, etc) ... but I definitely don't have a full understanding of today's research landscape. Could really use the help of you experts :-)

A bit more about myself: PhD in string theory/theoretical physics (Oxford), then quant finance, then built and sold an ML startup to a large company where I now manage the engineering team.
Skills/knowledge I bring which don't come as standard with Physics:

• Differential Geometry & Topology
• (numerical solution of) Partial Differential Equations
• (numerical solution of) Stochastic Differential Equations
• Quantum Field Theory / Statistical Field Theory
• tons of Engineering/Programming experience (in prod envs)

Especially curious to hear from anyone who made a similar transition already!