Jazmia Henry

AI Research Engineer (Microsoft, Morgan Stanley, Motley Fool, Stanford)

Cohort 1

Models are only as smart as the worlds they train in. Learn to build those worlds, and become the hire labs pay top dollar for.

RL/AI Engineer building worlds for AI

<pre><code class="language-markdown">AI labs are pouring money into RL environments. Anthropic alone has discussed
spending over a billion dollars on them in a single year, and a whole class of
startups now exists to sell labs the worlds their models train in. Here's the part
that matters for you: the skill they're buying is not "run PPO." It's designing
the tasks, tools, rewards, and verifiers that make RL post-training work. That
skill is software engineering plus evaluation design. No PhD required. I build
this layer for a living, and in four weeks I'll teach you to build it too.


One more thing before you scroll to the schedule. You will not build eight
disconnected demos in this course. You'll build one environment that compounds:
created in week 2, hardened against reward hacking that same week, used to
post-train a real model in week 3, evaluated with statistics that survive scrutiny
in week 3, and extended to a team of collaborating agents in week 4. By the
showcase, you'll have shipped an environment you can publish to Prime Intellect's
Environments Hub and point to in an interview. Not a certificate. An artifact.</code></pre>

Learn to build RL environments and verifiers for AI in 4 weeks. No PhD required.

Ship a working RL environment in 4 weeks

Post-train a real model against your own verifier

Design rewards that agents can't game

Prove your model got better with evidence that survives scrutiny

Build environments where agents learn to collaborate

<pre><code class="language-markdown">ML engineers and senior software engineers interested in post-training and RL environment roles.</code></pre>

<p>Engineers whose prompt-orchestrated systems have plateaued and who need training-based gains: verifiers, rejection sampling, GRPO.</p>

<pre><code class="language-markdown">Applied scientists bringing RL fine-tuning to domain and enterprise models.</code></pre>

Jazmia Henry Building worlds that train more capable models

Jazmia Henry

Courses

Post-Training Foundation Models: Environments, Rewards, and Verifiers