Preparing for an interview at Altos Labs requires a strategic approach. The evaluation is rigorous and highly specialized, focusing not just on your ability to write code, but on your capacity to build models that can handle the noise, scale, and complexity of biological data.

To succeed, you must demonstrate strength across several key evaluation criteria:

• Scientific Rigor and ML Foundations – Interviewers evaluate your depth of understanding in modern machine learning, particularly deep learning, generative AI, and foundation models. You can demonstrate strength here by explaining the mathematical intuitions behind your architectural choices and how you handle edge cases in model training.
• Engineering Excellence and Scale – This criterion focuses on your ability to translate theoretical models into scalable, robust pipelines. You will be assessed on your proficiency with distributed training, optimization, and deploying models into production environments where reliability is critical.
• Problem-Solving in Ambiguous Domains – Biological data is inherently messy and unstructured. Interviewers want to see how you approach novel problems, structure your hypotheses, and iterate on your models when standard out-of-the-box solutions fail.
• Cross-Functional Collaboration – At Altos Labs, you will work daily with computational biologists, wet-lab scientists, and software engineers. You must show that you can translate complex ML concepts to non-experts and absorb domain knowledge rapidly to inform your technical decisions.

The interview process for Machine Learning Engineers and Scientists at Altos Labs is designed to evaluate both your technical depth and your ability to thrive in a research-driven environment. You will typically begin with a recruiter screen to align on your background, research interests, and the specific team's needs (e.g., Multi Modality vs. AI Agents). This is followed by a technical screen, which often involves a mix of algorithmic coding and deep-dive discussions into your past ML projects.

For onsite rounds, the process mirrors top-tier research institutions and deep-tech companies. Candidates are frequently asked to deliver a research or technical presentation to a cross-functional panel. This presentation is a critical component, allowing the team to assess your communication skills, scientific rigor, and how you defend your technical choices during Q&A. Following the presentation, you will face a series of 1:1 or 2:1 interviews covering ML architecture, coding, and behavioral alignment.

The company values candidates who are driven by the mission and can navigate the unique challenges of biotech. Expect interviewers to probe deeply into how you handle unstructured problems and whether you possess the collaborative mindset necessary to bridge the gap between AI and biology.

This visual timeline outlines the typical progression from initial screening through the technical deep dives and the final onsite presentation stages. Use this to structure your preparation, ensuring you dedicate ample time to practicing your technical presentation and preparing for cross-functional behavioral interviews. Note that the exact sequence may vary slightly depending on your seniority and the specific team you are interviewing for.

Your interviews will test a spectrum of skills, from low-level engineering to high-level model architecture. Below are the primary areas where you will be evaluated.

Machine Learning and Foundation Models

This is the core of the technical evaluation. Interviewers need to know that you can design, train, and fine-tune large-scale models, particularly in the context of self-supervised learning and generative AI. Strong performance means you can discuss the tradeoffs of different architectures and understand how to adapt them for novel data types.

Be ready to go over:

• Transformer Architectures – Deep understanding of attention mechanisms, positional encoding, and scaling laws.
• Generative AI – Diffusion models, VAEs, and LLMs, particularly how they can be applied to non-text modalities.
• Optimization Techniques – Gradient clipping, learning rate scheduling, and handling vanishing/exploding gradients in deep networks.
• Advanced concepts (less common) – Parameter-efficient fine-tuning (LoRA, QLoRA), contrastive learning frameworks (CLIP equivalents for biology), and mechanistic interpretability.

Example questions or scenarios:

• "Walk me through how you would design a self-supervised learning objective for a dataset where the underlying ground truth is partially unknown."
• "How do you mitigate catastrophic forgetting when fine-tuning a foundation model on a highly specialized, narrow dataset?"
• "Explain the tradeoffs between using a graph neural network versus a standard transformer for relational data."

Multimodal and Relational Data

Because Altos Labs deals with complex biological systems, you will be evaluated on your ability to fuse different data types (e.g., text, imaging, genomic sequences). You must show that you understand how to build shared latent spaces and model complex relationships.

Be ready to go over:

• Multimodal Fusion – Early vs. late fusion, cross-attention mechanisms, and aligning disparate modalities.
• Graph Neural Networks (GNNs) – Message passing, graph attention, and modeling relational data (highly relevant for cellular and molecular networks).
• Dimensionality Reduction – Techniques for handling high-dimensional, sparse data typical in single-cell biology.
• Advanced concepts (less common) – Equivariant neural networks, multi-task learning across competing modalities, and handling missing modalities during inference.

Example questions or scenarios:

• "Design an architecture that takes both high-resolution cellular images and sparse genomic text as inputs to predict a cellular state."
• "How would you handle a scenario where one modality dominates the loss during the training of a multimodal model?"
• "Describe how you would construct a graph representation of interacting proteins and train a model to predict novel interactions."

ML Engineering and Scale

Theoretical knowledge must be backed by engineering execution. Interviewers will assess your ability to write clean, efficient code and scale your models across distributed compute clusters.

Be ready to go over:

• Distributed Training – Data parallelism, tensor parallelism, and pipeline parallelism (e.g., FSDP, DeepSpeed).
• PyTorch Optimization – Profiling bottlenecks, custom CUDA kernels, and memory management during training.
• MLOps and Pipelines – Data ingestion at scale, model versioning, and reproducible training pipelines.
• Advanced concepts (less common) – Inference optimization (TensorRT, quantization), fault-tolerant training setups, and managing multi-node GPU clusters.

Example questions or scenarios:

• "You are running out of GPU memory while training a large foundation model. What sequence of steps do you take to diagnose and resolve this?"
• "Write a PyTorch script to implement a custom attention layer, ensuring it is optimized for memory usage."
• "How do you design a data loader that can handle petabytes of image data without bottlenecking the GPUs?"

Scientific Communication and Culture Fit

As a scientist or engineer at Altos Labs, you are part of a multidisciplinary team. You will be evaluated on your ability to communicate complex ML concepts to non-ML experts and your resilience in the face of scientific ambiguity.

Be ready to go over:

• Cross-Functional Collaboration – Gathering requirements from biologists and translating them into technical specifications.
• Handling Ambiguity – Pivoting your approach when initial experiments fail or data proves too noisy.
• Mission Alignment – Demonstrating genuine interest in the biological applications of your work.

Example questions or scenarios:

• "Tell me about a time you had to explain a complex machine learning failure to a stakeholder without a technical background."
• "Describe a project where the data was significantly noisier than expected. How did you adapt your modeling strategy?"
• "Why are you interested in applying machine learning to cellular rejuvenation rather than traditional tech industry problems?"

As a Machine Learning Engineer at Altos Labs, your day-to-day work revolves around pushing the boundaries of what AI can do in the life sciences. You will be responsible for designing, training, and deploying large-scale foundation models that ingest multimodal biological data. This involves writing highly optimized PyTorch code, managing distributed training runs on massive GPU clusters, and constantly iterating on model architectures to improve performance.

Beyond coding, you will spend a significant portion of your time collaborating with computational biologists and wet-lab scientists. You will act as the bridge between raw biological data and actionable insights, helping to define the computational strategy for projects like the Virtual Cell or deploying AI Agents to assist in research. This requires you to deeply understand the biological context of the data you are modeling.

You will also drive the engineering of robust ML pipelines, ensuring that models can be trained reproducibly and deployed efficiently. Whether you are leading the development of a novel graph neural network to map relational biology or optimizing a transformer to handle multi-omic sequences, your deliverables will directly accelerate the company's research milestones.

To be a competitive candidate for this role, you must bring a strong mix of deep technical expertise and engineering discipline. Altos Labs hires across various levels (from Engineer to Principal Scientist), so expectations scale with seniority.

• Must-have technical skills – Expert-level proficiency in Python and PyTorch. Deep understanding of modern neural network architectures (Transformers, GNNs, Diffusion models). Experience with distributed training frameworks (e.g., DeepSpeed, PyTorch FSDP) and scaling models on cloud infrastructure or on-premise GPU clusters.
• Must-have experience – A Ph.D. or Master's degree in Computer Science, Machine Learning, Computational Biology, or a highly related field, coupled with significant industry experience building and deploying ML models. You must have a proven track record of tackling unstructured, complex datasets.
• Soft skills – Exceptional scientific communication skills. The ability to advocate for technical best practices while remaining open to feedback from domain experts in biology. Strong project management skills, particularly in driving ambiguous research projects to completion.
• Nice-to-have skills – Prior experience working with biological datasets (e.g., single-cell RNA sequencing, proteomics, high-content imaging). Familiarity with multi-omics integration and systems biology concepts.