What is a Machine Learning Engineer?

At Cohere, the Machine Learning Engineer role is pivotal to our mission of transforming healthcare through intelligent automation. You are not just building models; you are designing engines that digest complex clinical data to automate burdensome administrative practices. This role sits at the intersection of advanced Natural Language Processing (NLP), generative AI, and healthcare operations.

You will work with a high-caliber team of engineers, statisticians, and clinical experts to deploy production-grade models. Whether you are extracting clinical findings from unstructured notes or fine-tuning Small Language Models (SLMs) for specific healthcare tasks, your work directly impacts the efficiency of patient care. You will tackle challenges ranging from feature engineering on messy real-world data to building scalable systems that serve predictions in real-time. This is a role for builders who want to apply state-of-the-art AI, including Transformers and LLMs, to solve tangible problems in a critical industry.

Common Interview Questions

These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Getting Ready for Your Interviews

Preparation for Cohere requires a balance of strong theoretical knowledge and practical engineering capability. We look for engineers who can bridge the gap between research concepts and reliable production software.

Key Evaluation Criteria:

• Technical Depth in NLP & GenAI – We evaluate your understanding of modern architectures, specifically Transformers, LLMs, and generative models. You need to demonstrate not just how to use libraries, but how the underlying math (tensor manipulation) and mechanisms (attention) work.
• Production Engineering – A model is only as good as its deployment. We assess your ability to write clean, reusable Python code and your familiarity with deploying models in a scalable environment.
• Problem Solving & Adaptability – You will face ambiguous problems involving unstructured healthcare data. We look for candidates who can independently design experiments, interpret results, and pivot when initial approaches fail.
• Domain & Business Acumen – While deep healthcare knowledge is a plus, you must show an aptitude for understanding the "business logic" of the problem. We value candidates who question why a solution matters and how it fits into the broader market landscape.

Interview Process Overview

The interview process at Cohere is designed to be rigorous but reflective of the actual work you will do. It typically begins with an initial screening to align on your background and interest in the healthcare AI space. This is followed by a technical screen with a hiring manager or senior engineer. Unlike generic coding screens, this round often digs into your specific past projects and may touch on your understanding of the broader tech or healthcare landscape.

If you pass the screening stage, you will move to a series of deep-dive interviews. These sessions are split between hands-on technical assessments and conceptual discussions. You should expect a mix of coding tasks—such as manipulating tensors or implementing specific model components—and architectural discussions regarding generative models. The process is designed to verify that you are "hands-on" with the code while also possessing the theoretical depth to innovate.

This timeline illustrates the typical flow from application to offer. Note that the Technical Deep Dives are the most intensive part of the process, often involving multiple back-to-back sessions focusing on different competencies like coding, ML theory, and system design. Pacing yourself and reviewing your core concepts before the onsite stage is critical.

Deep Dive into Evaluation Areas

Based on recent candidate experiences and our engineering requirements, the following areas are the core pillars of our evaluation.

5. NLP and Generative Models

This is the cornerstone of the role. You must demonstrate a deep familiarity with Transformers, Large Language Models (LLMs), and Small Language Models (SLMs). We are interested in how you handle context engineering, fine-tuning, and the architecture of generative systems.

Be ready to go over:

• Transformer Architecture – The mechanics of self-attention, positional encoding, and encoder-decoder structures.
• Generative Approaches – Techniques for text generation, retrieval-augmented generation (RAG), and model compression.
• Fine-tuning Strategies – How to adapt pre-trained models (e.g., BERT, GPT variants) to specific clinical domains with limited data.
• Advanced concepts – Knowledge of model efficiency, quantization, or distilling large models into smaller, faster ones.

Example questions or scenarios:

• "Explain how you would fine-tune a foundation model to extract specific clinical entities from unstructured doctor notes."
• "Compare the trade-offs between using a massive LLM versus a fine-tuned SLM for a latency-sensitive application."
• "Walk me through the attention mechanism mathematically."

2. Coding and Tensor Manipulation

We value engineers who are fluent in Python and PyTorch. Interviews often involve live coding that goes beyond standard algorithms; you may be asked to manipulate high-dimensional data structures directly. This tests your intuition for how data flows through a neural network.

Be ready to go over:

• PyTorch/NumPy Proficiency – Slicing, broadcasting, and reshaping tensors without relying on documentation.
• Vectorization – Writing efficient code that avoids loops where matrix operations suffice.
• Data Preprocessing – converting raw text or structured data into model-ready inputs.

Example questions or scenarios:

• "Implement a specific layer of a neural network from scratch using only tensor operations."
• "Given a 3D tensor representing a batch of sequences, how would you mask specific tokens efficiently?"
• "Write a function to compute the pairwise distance between two sets of vectors without using a loop."

3. System Design and Productionization

Building a model is the first step; running it in production is the goal. We evaluate your ability to design systems that are scalable, reliable, and maintainable. This is especially relevant for Senior and Lead roles.

Be ready to go over:

• ML Ops – Strategies for model versioning, monitoring drift, and automated retraining pipelines.
• Scalability – Serving models with high throughput and low latency.
• Experimental Design – How to set up A/B tests or offline evaluations to measure model impact.

Example questions or scenarios:

• "How would you architect a system to process millions of patient records daily?"
• "What metrics would you track to ensure a deployed clinical model isn't degrading over time?"
• "Design a pipeline for continuous delivery of ML models."