Lila

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Lila Sciences is a company developing artificial intelligence and autonomous laboratory systems to accelerate scientific research and discovery. It aims to build 'scientific superintelligence' by combining AI with automated experimentation across fields like life sciences, chemistry, and materials science.

7 interview notes · updated Jul 2026

Lila·Machine Learning Engineer·Technical Phone Screen

Jun 2026

This was a technical round at Lila for an ML Engineer role where I had to present a research paper and one of my own projects back to back, then field deep follow-up questions on both. Felt like a lot to pack into one session and the time management aspect was genuinely stressful.

Walk us through the research paper you were given: what motivated the work, how the method works, what the key results were, and what you think its strengths and limitations are.
Present one of your own projects or publications: describe the problem, your specific contribution, the methodology, results, and what impact it had.

“The paper presentation part I felt okay about but the critical assessment section tripped me up.”

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Lila·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Interviewed for an ML Engineer role at Lila. The whole thing was basically one long deep-dive into a single LLM project, which sounds manageable until you realize they want every layer of the onion: business context, architecture choices, trade-offs, metrics, and then follow-ups on whatever you glossed over.

Walk me through an LLM project you've worked on end to end. Cover the business problem, why you went with an LLM approach, the architecture (data pipeline, model choice, prompting or fine-tuning or RAG), how you evaluated it, the key technical decisions you owned, the hardest problem you hit and how you solved it, and what the actual impact was. Expect follow-ups on any of it.

“This is the kind of question that feels fine to prep for and then humbles you in the room.”

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Lila·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Technical phone screen for an ML Engineer role at Lila, pretty deep on LLM post-training specifically RLVR. One big question that kept branching into sub-topics for a solid chunk of the call. Not a bad experience but it was dense.

Walk me through RLVR as it's used in modern LLM post-training. What makes a reward 'verifiable', how does it compare to preference-based approaches, what optimizers are commonly used, how do you set up rollouts, what failure modes should you worry about, and when would you pick RLVR over SFT or DPO?

“This was basically six questions duct-taped together and I didn't realize that until I was already three minutes into explaining verifiable vs learned rewards.”

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Lila·Machine Learning Engineer·Onsite - Behavioral / Leadership

May 2026

Interviewed for an ML Engineer role at Lila. One behavioral question about working styles, which sounds easy until you're actually sitting there trying to not sound like a jerk.

What types of people do you find it difficult to work with, and why?

“I stumbled here more than I expected.”

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Lila·Machine Learning Engineer·Technical Phone Screen

May 2026

Lila gave me a live code review round where they handed me a full Transformer implementation and asked me to find what was broken. Brutal but fair, honestly the most technically dense interview I've done for an MLE role.

You're given a from-scratch Transformer implementation in a GitHub repo covering multi-head attention, positional encoding, layer norm, residuals, FFN, and a training loop. Find the bugs, explain what each one breaks, and show the corrected code.

“This was a single question that ate the entire session.”

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Lila·Machine Learning Engineer·Onsite - Behavioral / Leadership

May 2026

Behavioral round for an ML Engineer role at Lila. Just the one question but it had a lot of moving parts and I don't think I nailed it.

Describe a type of colleague you find difficult to work with, give a real example, and walk through how you managed the relationship and made the collaboration work.

“I had a decent story ready but the question asked for three things at once and I kind of lost the thread halfway through.”

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

May 2026

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.

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.

“This question is basically six questions stapled together.”

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