Lila Sciences·Software Engineer·Technical Phone Screen·Junior

Junior

Apr 2026

Summary

Research-heavy interview for the AI Resident role at Lila Sciences, focused almost entirely on ML force fields. Two meaty technical questions with an expectation that you can go deep on both the theory and the messy practical side of training these models.

Questions Asked (2)

When designing an ML force field model, how do you decide between building a specialist model for a narrow domain versus a generalist model that covers a broader chemical space?

Technical Trade-offsAdaptability & Ambiguity

Author's notes

This one tripped me up more than I expected.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the specific use case and constraints, then systematically compare specialist vs. generalist models across data availability, required accuracy, computational resources, and deployment flexibility. Emphasize that the decision is often iterative—begin with a specialist model for quick wins and expand to a generalist model as data and needs grow.

Pro tip: Highlight that in practice, a hybrid approach—such as a generalist model fine-tuned on domain-specific data—often balances accuracy and coverage, and mention that you'd validate with a clear metric like out-of-domain error or transfer learning performance.

1. Clarify Requirements and Constraints

Ask about the target chemical space, required accuracy, available data, and computational budget to understand the problem context.

2. Assess Data Availability and Diversity

Evaluate whether you have sufficient high-quality data for a specialist model or diverse data for a generalist model, considering data generation costs.

3. Compare Trade-offs

Weigh accuracy, generalization, training cost, inference speed, and maintenance effort for both approaches, using metrics like MAE or RMSE on held-out sets.

4. Consider Hybrid or Transfer Learning Approaches

Explore fine-tuning a generalist model on domain-specific data or using multi-task learning to get the best of both worlds.

5. Plan for Iteration and Scalability

Recommend starting with a specialist model for rapid iteration, then expanding to a generalist model as data and requirements evolve, with a clear evaluation plan.

Key Points to Mention

Data efficiency and availability: specialist models require less data but may not generalize; generalist models need large, diverse datasets.
Accuracy vs. coverage trade-off: specialist models excel in narrow domains but fail outside; generalist models offer broader coverage at potentially lower accuracy.
Computational resources: generalist models are typically larger and more expensive to train and deploy, while specialist models are lighter.
Transfer learning and fine-tuning: leveraging pre-trained generalist models and adapting them to specific domains can mitigate data scarcity.
Evaluation metrics: define clear metrics (e.g., force error, energy error) and test on out-of-domain data to measure generalization.
Deployment and maintenance: consider inference latency, model size, and ease of updating as the chemical space expands.

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

How would you correct for systematic energy prediction bias when your ML force field encounters non-equilibrium configurations during inference?

Root Cause AnalysisTechnical Trade-offs

Author's notes

Blanked for a second.

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AI HintsAI Generated

Suggested Approach

Start by clarifying that systematic bias on non-equilibrium configurations is a distribution shift problem, then propose a diagnostic-first approach: quantify the bias as a function of relevant order parameters (e.g., forces, strain, coordination) and choose a correction method that is both physically motivated and computationally efficient. Emphasize validation on held-out non-equilibrium data and discuss trade-offs between accuracy, speed, and generalizability.

Pro tip: Frame the solution as a lightweight, inference-time correction (e.g., a learned residual or an ensemble-based uncertainty trigger) rather than retraining the entire force field, because in production you often cannot afford to retrain for every new non-equilibrium regime. Also, mention that you would log the correction magnitude to detect when the model is extrapolating too far.

1. Diagnose the bias

Characterize the systematic error by comparing predictions to reference data (e.g., DFT) across a range of non-equilibrium configurations, and identify which physical descriptors (e.g., bond lengths, angles, energy gradients) correlate with the bias.

2. Choose a correction strategy

Select a correction method that fits the diagnosed pattern: options include a learned residual model, a linear/nonlinear calibration on relevant order parameters, or an ensemble-based uncertainty-aware adjustment. Consider whether the correction should be applied to energies, forces, or both.

3. Implement with efficiency in mind

Design the correction to be lightweight and compatible with inference constraints (e.g., a small neural network or a lookup table). Ensure it can be applied on-the-fly without significant overhead and that it preserves differentiability for downstream tasks like MD.

4. Validate and stress-test

Evaluate the corrected model on held-out non-equilibrium configurations, including extreme cases, and compare against baselines. Use metrics like mean absolute error, force error distributions, and energy conservation in MD to ensure the correction does not introduce artifacts.

5. Monitor and iterate

Deploy with logging of correction magnitudes and input descriptors to detect when the model encounters out-of-distribution configurations. Use this feedback to refine the correction or trigger retraining when necessary.

Key Points to Mention

Distribution shift: non-equilibrium configurations are out-of-distribution relative to training data, so standard error metrics may not capture systematic bias.
Physical constraints: corrections should respect known invariances (e.g., translational, rotational, permutation) and avoid breaking energy conservation.
Trade-offs: accuracy vs. inference speed, simplicity vs. generalizability, and the cost of retraining vs. adding a correction layer.
Uncertainty quantification: use ensemble variance or other uncertainty measures to decide when to apply a correction or fall back to a more expensive method.
Validation on non-equilibrium data: ensure the correction is tested on configurations that mimic real inference scenarios, not just equilibrium data.
Monitoring in production: log correction statistics to detect drift and inform when the model needs updating.

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