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.

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.

1. 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."

The word cloud above highlights the most frequently discussed concepts in our interviews. Notice the prominence of Transformers, Python, Generative, and Production. Use this as a checklist: if you are comfortable with these terms, you are on the right track.

Key Responsibilities

As a Machine Learning Engineer at Cohere, your daily work will be dynamic and impact-driven.

• Model Development: You will independently design and develop clinical ML models. This involves performing in-depth analysis of healthcare data, selecting the right architecture (often NLP-focused), and training models to extract or predict relevant findings.
• Production Engineering: You are responsible for the lifecycle of your code. You will build reliable, scalable production systems, ensuring your models run efficiently in a real-world environment. This includes improving model runtime and resource usage.
• Cross-Functional Collaboration: You will work closely with product managers to define requirements and with clinicians to validate that your model's outputs make medical sense. You will also provide guidance to junior engineers (for senior roles) and align technical solutions with business objectives.

Role Requirements & Qualifications

To succeed in this process, you should benchmark your background against these expectations:

• Technical Skills:

  • Advanced Python: Non-negotiable mastery of Python for both scripting and application development.
  • ML Frameworks: Deep experience with PyTorch is highly preferred.
  • NLP Specialization: Hands-on experience with Transformers, LLMs, and context engineering.

• Experience Level:

  • Engineer I/II: Typically 1–4 years of full-time professional experience in ML. A Master’s degree in CS, Math, or Statistics is often expected.
  • Lead Engineer: 7+ years of experience (or 5+ with a PhD), with a track record of leading projects and mentoring others.

• Soft Skills:

  • Scientific Rigor: Ability to independently perform data collection and interpret results scientifically.
  • Communication: Ability to explain complex ML concepts to non-technical stakeholders (clinicians, PMs).

• Nice-to-have:

  • Prior experience in the healthcare domain.
  • PhD in a relevant field (required for some Lead roles).

Common Interview Questions

These questions are drawn from candidate data and our evaluation rubrics. They represent the types of inquiries you will face, though specific wording may vary.

Technical & Domain Knowledge

• "How do you handle class imbalance in a dataset containing rare clinical diagnoses?"
• "Explain the difference between encoder-only, decoder-only, and encoder-decoder architectures. When would you use each?"
• "Describe a time you had to optimize a model for inference speed. What techniques did you use?"
• "How does 'attention' actually work mathematically? Can you write the equation?"
• "What are the challenges of using Generative AI in a high-stakes field like healthcare?"

Behavioral & Experience

• "Tell me about a project where you had to deal with very messy, unstructured data."
• "Why are you interested in Cohere and the healthcare space specifically?"
• "Describe a situation where you disagreed with a product manager or stakeholder on a technical decision."
• "Why does your current company use approach X instead of approach Y?" (Note: Be prepared for questions that test your understanding of your current company's business or technical strategy.)

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.

Frequently Asked Questions

Q: Is this role fully remote? Yes, Cohere offers fully remote opportunities within the United States, with occasional travel (approx. 5%) for team meetups or planning sessions.

Q: How difficult are the coding rounds? The difficulty is generally rated as Medium to Hard. You should be comfortable with standard algorithms, but specific emphasis is placed on tensor manipulation and vectorization logic rather than just generic LeetCode-style puzzles.

Q: What is the timeline for the interview process? The process can move quickly, but it depends on scheduling. Typically, you can expect a few weeks from the initial screen to the final decision.

Q: Do I need healthcare experience to apply? While healthcare experience is a "nice-to-have," it is not strictly required for all levels. However, you must demonstrate a strong interest in the domain and the ability to learn complex clinical contexts quickly.

Other General Tips

• Refresh Linear Algebra: Since tensor manipulation is a known interview component, ensure your linear algebra fundamentals (matrix multiplication, dimensions, broadcasting) are sharp.
• Know Your Resume Deeply: Interviewers, especially Hiring Managers, may drill down into the "why" behind your past projects. Be ready to justify your architectural choices and business impact.
• Prepare for Ambiguity: You may be asked questions that seem broad or even slightly irrelevant to immediate coding (e.g., questions about market competitors or business strategy). Treat these as tests of your holistic understanding of the tech industry.
• Show Professionalism: In the rare event of a disengaged interviewer, maintain your composure and professionalism. Treat every interaction as a demonstration of how you handle friction in the workplace.
• Focus on "Why Healthcare": We are mission-driven. Candidates who can articulate a genuine passion for improving patient outcomes via technology often stand out over those who only care about the tech stack.

Summary & Next Steps

Joining Cohere as a Machine Learning Engineer means stepping into a role where your code has real-world consequences. You will be working with cutting-edge Generative AI and Transformers to solve complex problems in healthcare. The bar is high, requiring deep technical knowledge of Python and PyTorch, along with the strategic mindset to deploy these models into production.

To succeed, focus your preparation on tensor operations, transformer architectures, and system design. Review your past projects to ensure you can explain not just what you built, but why you built it that way. Approach the process with confidence—your skills in ML have the potential to reshape how healthcare is delivered.

The salary data above represents the base range for these positions. Compensation at Cohere is competitive and commensurate with experience, often including equity components and comprehensive benefits. Ensure you discuss your specific expectations with your recruiter early in the process.