Senior ML Engineer aiming for RL research in ~1.5 years — roadmap, DSA prep, and time management?

Hi everyone,

I’m a Senior Machine Learning Engineer planning a focused transition into Reinforcement Learning research over the next 12–18 months, and I’d really value advice from people who’ve done this alongside full-time work.

Background (brief):

• B.Tech + M.Tech (strong math/PDEs) • ~2+ years in ML/DS (forecasting, optimization, CNNs) • Currently building LLM-based agents & multi-agent systems in fintech (orchestration, tools, OpenAI/Anthropic, knowledge graphs),via AI automation 

I’m comfortable with Python, PyTorch, probability, linear algebra, and optimization.

Why RL:

I work daily with prompting, tool use, and frozen policies, and I want to move toward agents that actually learn via interaction and long-horizon objectives.

What I’m doing now:

• Learning RL from first principles (MDPs, Bellman equations, policy/value iteration) • Implementing algorithms from scratch • Enrolled in Prof. Balaraman Ravindran’s NPTEL RL course (IIT Madras) 

Looking for guidance on:

1. What really separates knowing RL from doing RL research? 2. What’s a realistic research output in ~18 months without being in a lab? 3. How much theory is “enough” early on to be productive? 4. What actually works to break into RL research from industry? 5. DSA interviews: how important are LeetCode-style rounds for applied/research ML roles, and what’s the minimum effective prep? 6. Time management: how do you realistically balance deep RL study/research with a full-time ML job without burning out? 

1. How relevant is RL with AI agents that have learn to use tools effecctively?

I’m trying to balance deep RL learning, research credibility, and staying interview-ready.

Blunt, experience-based advice is very welcome. Thanks!