Lila·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

May 2026

Summary

Interviewed for an ML Engineer role at Lila and got hit with a deep-dive system design question on using LLMs with reinforcement learning to optimize DNA sequences. It was one of the more technically sprawling questions I've faced, covering everything from tokenization to reward hacking mitigations. Felt reasonably prepared on the RL side but the biology-specific bits exposed some gaps.

Questions Asked (1)

Design a system that uses an LLM combined with reinforcement learning to optimize DNA sequences toward a target objective like binding affinity or expression level. Walk through sequence representation, model selection, reward design, the RL loop, evaluation, and safety considerations.

System DesignTechnical Trade-offsA/B Testing & Experimentation

Author's notes

This question is basically six questions stapled together.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a coherent pipeline: start with sequence representation and model choices, then detail the RL loop with reward design, and finish with evaluation and safety. Emphasize trade-offs and justify each design decision with respect to the target objective and biological constraints.

Pro tip: Highlight the importance of a well-calibrated reward function that balances multiple objectives (e.g., affinity and off-target effects) and mention using a held-out wet-lab validation set to avoid overfitting to the reward model.

1. Sequence Representation and Model Selection

Choose a representation (e.g., one-hot, k-mer, or learned embeddings) and select an LLM (e.g., DNABERT, Nucleotide Transformer) to generate or score sequences. Consider fine-tuning the LLM on relevant biological data.

2. Reward Design

Define a reward function that quantifies the target objective (e.g., predicted binding affinity from a surrogate model) and incorporates penalties for undesired properties (e.g., toxicity, off-target effects). Use a combination of learned and rule-based rewards.

3. RL Loop

Set up an RL loop where the LLM acts as the policy, generating sequences, and the reward function provides feedback. Use algorithms like PPO or REINFORCE with a baseline, and consider techniques like reward shaping and KL regularization to maintain sequence validity.

4. Evaluation and Iteration

Evaluate generated sequences using in silico metrics (e.g., predicted affinity) and, if possible, wet-lab experiments. Use A/B testing to compare different RL configurations and iterate on the reward and model.

5. Safety and Ethical Considerations

Address biosafety risks by filtering dangerous sequences, ensuring compliance with regulations, and incorporating human oversight. Discuss potential misuse and mitigation strategies.

Key Points to Mention

Choice of LLM and fine-tuning strategy for DNA sequences
Reward function design: multi-objective optimization and surrogate models
RL algorithm selection (e.g., PPO) and handling of discrete sequence space
Evaluation metrics: in silico vs. wet-lab, and A/B testing for model comparison
Safety: biosafety filters, ethical guidelines, and regulatory compliance
Trade-offs: exploration vs. exploitation, computational cost, and sample efficiency

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