Figma interview questions

Interface: one keyphrase per text or several, overlapping or not, labelled as offsets or strings?

Tokenizer: which vocabulary, cased or not. Map character labels to token start and end with the fast tokenizer's offset mapping, and decide what happens when a label cuts through a subword. Texts over 512 tokens need sliding windows.

Valid spans: end not before start, a maximum length, a no-answer case. Outputs: top spans as character offsets with scores.

Split tasks by verifiability. Exact requests, like setting a padding, get hard assertions on the document diff: the target property changed, and nothing else did.

Open-ended requests get a multimodal judge over before and after renders, scored per rubric criterion: intent followed, content preserved, layout intact. Calibrate each judge against designer labels and track agreement over time.

The platform: versioned files and instructions, sandboxed runs, stored traces, per-slice results, and a regression gate.

A SQuAD-style head: two logits per token, for start and end.

import torch
import torch.nn as nn
import torch.nn.functional as F

class SpanModel(nn.Module):
    def __init__(self, encoder, hidden):
        super().__init__()
        self.encoder = encoder
        self.head = nn.Linear(hidden, 2)

    def forward(self, ids, attn):
        out = self.encoder(input_ids=ids,
                           attention_mask=attn)
        s, e = self.head(out.last_hidden_state).unbind(-1)
        neg = torch.finfo(s.dtype).min
        pad = attn == 0
        s = s.masked_fill(pad, neg)
        return s, e.masked_fill(pad, neg)

def train_step(model, opt, b):
    model.train()
    s, e = model(b["ids"], b["attn"])
    loss = (F.cross_entropy(s, b["start"])
            + F.cross_entropy(e, b["end"])) / 2
    opt.zero_grad()
    loss.backward()
    opt.step()
    return loss.item()

Interviewers check that pads are masked, labels are token indices mapped from character offsets, and validation runs under no_grad and eval mode. Decode the best pair with end not before start and a length cap, then score exact match and token F1 against the gold character spans.

Feed: optimize meaningful engagement, like opens and remixes, checked against retention. Retrieve, then rank; cold-start users get content embeddings and popular-by-segment items, plus a small exploration slot run as a bandit. Evaluate offline with NDCG, then A/B.

HTML: an LLM with design-system components in context and constrained output. Check that the output parses and renders, then compare rendered screenshots against references with a calibrated judge. Online, track how often users keep the result.