Seagate Technology Machine Learning Engineer Interview Questions + Guide in 2025

Seagate Technology is a leading innovator in data storage solutions, committed to empowering humanity through data management and analytics.

As a Machine Learning Engineer at Seagate, you will be tasked with developing, fine-tuning, and deploying advanced machine learning models, particularly focusing on generative AI and large language models. Key responsibilities include working collaboratively with cross-functional teams to integrate AI innovations into various business functions, such as sales and supply chain, and implementing strategies to optimize model performance through prompt engineering and Retrieval-Augmented Generation (RAG). This role requires a strong foundation in traditional machine learning techniques, as well as proficiency in programming languages like Python and knowledge of ML frameworks such as PyTorch and TensorFlow.

Ideal candidates will possess a passion for navigating the dynamic landscape of AI technology, exhibit exceptional problem-solving skills, and demonstrate strong organizational abilities. They should also have a collaborative mindset, eager to build relationships across the organization while contributing to a culture of inclusion and innovation.

This guide will help you prepare for your interview by focusing on the essential skills and experiences that align with the role and Seagate's values, ensuring you present yourself as a strong candidate ready to contribute to the company's mission.

The interview process for a Machine Learning Engineer at Seagate Technology is structured to assess both technical expertise and cultural fit within the organization. It typically consists of several key stages:

1. Initial HR Screening

The process begins with a 30 to 60-minute phone call with an HR recruiter. This initial screening focuses on understanding your interest in the role and assessing your foundational capabilities. The recruiter will discuss the position, the company culture, and your professional background, ensuring alignment with Seagate's values and expectations.

2. Technical Interview

Following the HR screening, candidates usually participate in a technical interview, which may be conducted via video conferencing. This interview typically lasts about an hour and involves discussions around your previous projects, technical skills, and problem-solving approaches. Expect to elaborate on your experience with machine learning techniques, particularly in relation to large language models, prompt engineering, and data manipulation.

3. Behavioral Interview

Candidates may then engage in a behavioral interview, which often includes situational questions designed to evaluate your teamwork, communication skills, and ability to overcome challenges. This interview may involve multiple interviewers, including team members and managers, who will assess how your experiences align with the collaborative and innovative culture at Seagate.

4. Onsite Interview (or Final Round)

The final stage may involve an onsite interview or a comprehensive virtual interview with key stakeholders from the team. This round typically includes a series of one-on-one interviews where you will be asked to discuss your technical projects in detail, focusing on the methodologies used, data gathering processes, and the outcomes achieved. Interviewers will be particularly interested in your ability to adapt generative AI models for various applications and your understanding of the latest trends in machine learning.

Throughout the interview process, candidates should be prepared to demonstrate their technical knowledge, problem-solving skills, and ability to work collaboratively in a fast-paced environment.

Next, let’s explore the specific interview questions that candidates have encountered during this process.

Here are some tips to help you excel in your interview.

Prepare to Discuss Your Projects

Be ready to explain your past research and projects in detail, particularly those that align with the work Seagate is doing in machine learning and generative AI. Highlight the methodologies you used, the challenges you faced, and how you overcame them. The interviewers are interested in your thought process and the relevance of your experience to their current projects, so make sure to connect your past work to their needs.

Emphasize Collaboration and Teamwork

Seagate values collaboration across different teams. Be prepared to share examples of how you have successfully worked in teams, particularly in overcoming challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on your role in the team and how you contributed to achieving a common goal. This will demonstrate your ability to work well with others and your commitment to team success.

Showcase Your Technical Skills

Given the emphasis on algorithms and machine learning in this role, ensure you can discuss your technical skills confidently. Brush up on your knowledge of Python, SQL, and machine learning frameworks like PyTorch and TensorFlow. Be prepared to discuss specific algorithms you have implemented and the results you achieved. If you have experience with large language models or prompt engineering, make sure to highlight that as well.

Be Ready for Behavioral Questions

Expect a mix of technical and behavioral questions. Prepare for questions that assess your problem-solving abilities and how you handle challenges. For instance, you might be asked to describe a time when you faced a significant obstacle in a project and how you resolved it. Reflect on your experiences and be ready to share stories that illustrate your resilience and adaptability.

Understand the Company Culture

Seagate promotes a culture of innovation, integrity, and inclusion. Familiarize yourself with their core values and think about how your personal values align with theirs. During the interview, express your enthusiasm for working in a diverse environment and your commitment to contributing positively to the company culture. This will help you stand out as a candidate who is not only technically qualified but also a good cultural fit.

Practice Your Communication Skills

Effective communication is crucial in this role, especially when collaborating with cross-functional teams. Practice articulating your thoughts clearly and concisely. Consider conducting mock interviews with a friend or mentor to refine your delivery. Be prepared to explain complex technical concepts in a way that is accessible to non-technical stakeholders, as this will demonstrate your ability to bridge the gap between technical and business teams.

Stay Updated on Industry Trends

Given the fast-paced nature of machine learning and generative AI, it’s important to stay informed about the latest trends and advancements in the field. Be prepared to discuss recent developments and how they might impact Seagate’s work. This will show your passion for the industry and your commitment to continuous learning, which is highly valued at Seagate.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Machine Learning Engineer role at Seagate Technology. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Seagate Technology. The interview process will likely focus on your technical skills, experience with machine learning models, and your ability to work collaboratively within a team. Be prepared to discuss your past projects in detail, as well as your approach to problem-solving in the context of machine learning and data analytics.

Machine Learning

1. Can you explain the process you follow when developing a machine learning model?

Understanding the model development lifecycle is crucial for this role, as it involves various stages from data collection to deployment.

How to Answer

Discuss the steps you take, including data preprocessing, feature selection, model selection, training, evaluation, and deployment. Highlight any specific methodologies or frameworks you prefer.

Example

“I typically start with data collection and preprocessing, ensuring the data is clean and relevant. I then perform exploratory data analysis to identify key features before selecting an appropriate model. After training the model, I evaluate its performance using metrics like accuracy and F1 score, and finally, I deploy the model using a CI/CD pipeline.”

2. Describe a project where you implemented a large language model. What challenges did you face?

This question assesses your hands-on experience with large language models and your problem-solving skills.

How to Answer

Focus on the specific project details, the challenges encountered, and how you overcame them. Mention any tools or techniques used.

Example

“In a recent project, I implemented a large language model for a customer support chatbot. One challenge was ensuring the model understood context in conversations. I addressed this by fine-tuning the model with domain-specific data and implementing a feedback loop to continuously improve its responses.”

3. How do you approach prompt engineering for generative AI models?

Prompt engineering is essential for optimizing model performance, especially in generative AI.

How to Answer

Explain your understanding of prompt engineering and provide examples of how you have successfully crafted prompts to achieve desired outputs.

Example

“I approach prompt engineering by first understanding the model's capabilities and limitations. I then experiment with different prompt structures, iterating based on the outputs. For instance, in a text generation task, I found that providing context and specific instructions significantly improved the relevance of the generated content.”

4. What techniques do you use for fine-tuning machine learning models?

Fine-tuning is a critical skill for adapting pre-trained models to specific tasks.

How to Answer

Discuss the techniques you use, such as transfer learning, hyperparameter tuning, and regularization methods.

Example

“I often use transfer learning to fine-tune models, leveraging pre-trained weights to save time and resources. I also perform hyperparameter tuning using grid search or Bayesian optimization to find the best settings for the model, ensuring it generalizes well to new data.”

5. Can you explain Retrieval-Augmented Generation (RAG) and its applications?

RAG is a modern technique that combines retrieval and generation, which is relevant to the role.

How to Answer

Provide a clear definition of RAG and discuss its benefits and potential applications in real-world scenarios.

Example

“Retrieval-Augmented Generation (RAG) combines the strengths of retrieval-based and generative models. It retrieves relevant documents to provide context for generating responses. This approach is particularly useful in applications like chatbots and information retrieval systems, where accuracy and relevance are critical.”

Statistics & Probability

1. How do you assess the performance of a machine learning model?

Understanding model performance metrics is essential for evaluating effectiveness.

How to Answer

Discuss various metrics you use, such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I assess model performance using a combination of metrics. For classification tasks, I look at accuracy, precision, and recall to understand the trade-offs. I also use ROC-AUC for a comprehensive view of the model's performance across different thresholds.”

2. Can you explain the concept of overfitting and how to prevent it?

Overfitting is a common issue in machine learning that candidates should be familiar with.

How to Answer

Define overfitting and discuss techniques to prevent it, such as cross-validation, regularization, and using more data.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern. To prevent it, I use techniques like cross-validation to ensure the model generalizes well. Additionally, I apply regularization methods like L1 and L2 to penalize overly complex models.”

3. What is the importance of feature selection in machine learning?

Feature selection can significantly impact model performance and interpretability.

How to Answer

Discuss the role of feature selection in improving model accuracy and reducing complexity.

Example

“Feature selection is crucial as it helps improve model accuracy by eliminating irrelevant or redundant features. It also reduces the risk of overfitting and enhances model interpretability, making it easier to understand the factors driving predictions.”

4. How do you handle missing data in a dataset?

Handling missing data is a common challenge in data preprocessing.

How to Answer

Explain the strategies you use to address missing data, such as imputation, removal, or using algorithms that support missing values.

Example

“I handle missing data by first analyzing the extent and pattern of the missingness. Depending on the situation, I may use imputation techniques like mean or median substitution, or I might remove rows or columns with excessive missing values. I also consider using models that can handle missing data natively.”

5. Can you explain the difference between supervised and unsupervised learning?

This fundamental concept is essential for any machine learning role.

How to Answer

Define both types of learning and provide examples of each.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as classification tasks. In contrast, unsupervised learning deals with unlabeled data, aiming to find patterns or groupings, like clustering algorithms.”