1 min lesson

Technical phone screens

Say what this means in practice: "The technical screens mix RL/ML fundamentals questioning with a hands-on coding or research-reasoning exercise."

The technical screens mix RL/ML fundamentals questioning with a hands-on coding or research-reasoning exercise. They are looking for depth, not trivia: can you derive the core math, reason about an experiment and stay coherent when you are wrong.

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Advanced table

Expect a subset, not all of it, in any single 60-minute screen

What they probe: RL fundamentals
What good looks like: Derive a policy gradient; explain advantage, KL control, on- vs off-policy and async RL.

What they probe: PPO / GRPO
What good looks like: State each objective, why the clip or group baseline exists and a failure mode of each.

What they probe: RLHF vs RLVR
What good looks like: Reward models from human preferences vs verifiable rewards from tests; tradeoffs and where each breaks.

What they probe: Transformer internals
What good looks like: Q/K/V and scaled dot-product attention, multi-head, KV cache, decoding and sampling.

What they probe: Coding exercise
What good looks like: Implement or debug a small component: a loss, a sampling loop or a KV cache.

What they probe	What good looks like
RL fundamentals	Derive a policy gradient; explain advantage, KL control, on- vs off-policy and async RL.
PPO / GRPO	State each objective, why the clip or group baseline exists and a failure mode of each.
RLHF vs RLVR	Reward models from human preferences vs verifiable rewards from tests; tradeoffs and where each breaks.
Transformer internals	Q/K/V and scaled dot-product attention, multi-head, KV cache, decoding and sampling.
Coding exercise	Implement or debug a small component: a loss, a sampling loop or a KV cache.

Expect a subset, not all of it, in any single 60-minute screen.

The coding portion is usually a focused component rather than a sprawling system. You might be asked to write the GRPO advantage from a batch of rewards or to find the bug in a decoding loop that silently breaks the KV cache.

Be ready to write something this concrete live - GRPO-style group-normalized advantage

import torch

def group_advantages(rewards, group_size, eps=1e-6):
    # rewards: (B,) flat tensor of B = num_groups * group_size samples
    r = rewards.view(-1, group_size)               # (G, group_size)
    baseline = r.mean(dim=1, keepdim=True)          # per-prompt baseline
    std = r.std(dim=1, keepdim=True)
    adv = (r - baseline) / (std + eps)              # normalize within group
    return adv.view(-1)                             # back to (B,)

Interview move

Think out loud the entire time. Interviewers score your reasoning path and how you handle being corrected far more than a flawless first answer. When you hit a fork, say which option you are taking and why; when you are unsure, say what you would measure to find out. Silence reads as either stuck or hiding.

Reciting beats understanding only until the follow-up

You can recite the PPO objective and still fail the next question: why does the clip exist and what happens to the gradient when the ratio leaves the clip range. Prep the second and third question deep on each topic, because that is where a memorized answer collapses and a real one holds.

QIn a 60-minute technical screen you blank on a derivation midway. What is the highest-signal way to handle it?

Technical phone screens

Step 1 of 3

Expect a subset, not all of it, in any single 60-minute screen