Lila Sciences·Software Engineer·Technical Phone Screen·Junior

Junior

Jun 2026

Summary

Interviewed for the AI Resident role at Lila Sciences. The technical depth they expected was no joke. One question basically ate up the whole session and I left feeling like I'd only scratched the surface.

Questions Asked (1)

How do you quantify uncertainty in an active learning pipeline aimed at data efficiency, specifically when trying to cover a large phase space while minimizing expensive DFT calculations? Walk through uncertainty estimators, acquisition functions, and how you balance diversity against uncertainty.

Technical Trade-offsAlgorithms & Data StructuresSystem Design

Author's notes

This one sprawled fast.

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AI HintsAI Generated

Suggested Approach

Start by framing the problem as a trade-off between exploration (covering phase space) and exploitation (reducing uncertainty in promising regions). Then, systematically discuss uncertainty estimators (e.g., ensemble variance, Gaussian process variance), acquisition functions (e.g., BALD, max entropy, expected improvement), and how to combine them with diversity (e.g., via batch selection with determinantal point processes). Finally, emphasize practical considerations like computational cost and scalability.

Pro tip: Mention that in practice, you'd start with a cheap surrogate model (e.g., random forest or ensemble) to estimate uncertainty, and only use DFT for the most informative points. Also, highlight that diversity can be enforced by clustering or core-set selection to avoid redundant calculations.

1. Define the goal and constraints

Clarify that the objective is to minimize expensive DFT calculations while efficiently covering a large phase space. Emphasize the need for a balance between uncertainty reduction and diversity.

2. Choose uncertainty estimators

Discuss options like ensemble variance (e.g., from random forests or deep ensembles), Gaussian process posterior variance, or Bayesian neural networks. Mention that the choice depends on data size, dimensionality, and computational budget.

3. Select acquisition functions

Explain common acquisition functions: uncertainty sampling (max variance), BALD (mutual information), expected improvement, and entropy-based methods. Note that some naturally incorporate diversity (e.g., batch BALD with diversity regularization).

4. Incorporate diversity

Describe methods to ensure diversity: clustering in feature space, core-set selection, determinantal point processes (DPPs), or greedy selection with a diversity penalty. Emphasize that diversity prevents redundant sampling in already-explored regions.

5. Balance and iterate

Propose a combined acquisition score (e.g., uncertainty + λ * diversity) or a two-stage approach: first filter by uncertainty, then select a diverse batch. Highlight the need to tune λ and validate on a hold-out set.

Key Points to Mention

Uncertainty estimators: ensemble variance, GP variance, Bayesian NN
Acquisition functions: BALD, max entropy, expected improvement, Thompson sampling
Diversity techniques: DPPs, core-sets, clustering, greedy diversity
Batch selection strategies: batch BALD, greedy with diversity penalty
Trade-off tuning: hyperparameter λ, validation, and computational cost
Scalability: using cheap surrogates, parallel DFT, and active learning loops

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.