Interview Experience: Software Engineer Intern (Doubao Seed) - Machine Learning System at ByteDance (2025 Summer Internship)

I recently interviewed for the Software Engineer Intern (Doubao Seed) - Machine Learning System position at ByteDance for the 2025 Summer internship (PhD), and I’d like to share a detailed breakdown of my experience, including the job responsibilities, interview process, and the types of questions I encountered. This internship is highly competitive and provides a fantastic opportunity for PhD students who are passionate about machine learning systems, especially in the context of ByteDance’s wide-reaching AI products such as TikTok. Below is a comprehensive overview of the process and insights to help you prepare if you’re interviewing for a similar role.

Job Overview

The Software Engineer Intern - Machine Learning System (Doubao Seed) role at ByteDance focuses on building and optimizing machine learning systems. As an intern, you’ll be involved in developing and deploying ML algorithms, working with large-scale data, and enhancing systems that power ByteDance’s products. The Doubao Seed team is at the forefront of machine learning innovations, particularly related to foundational models (such as LLMs and transformers), distributed training systems, and AI research.

Key Responsibilities

• Research and Development: Work on cutting-edge machine learning algorithms and systems that power ByteDance’s products.
• Distributed Systems: Build and optimize distributed systems for training machine learning models at scale.
• Data Processing: Develop data pipelines for training and evaluating large machine learning models.
• Optimization: Focus on improving the efficiency and scalability of training and inference processes in production.
• Collaboration: Work with experienced machine learning researchers and engineers to integrate algorithms into production environments.

Qualifications

Required:

• Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• Strong background in machine learning algorithms, distributed systems, and software engineering.
• Proficiency in programming languages such as Python, C++, CUDA (for GPU programming).
• Familiarity with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
• Understanding of parallel computing and scalable data processing.

Preferred:

• Experience with large-scale systems, cloud-based infrastructure, and distributed training (e.g., DeepSpeed, Megatron, Horovod).
• Knowledge of AI foundations, such as transformer models, reinforcement learning, and natural language processing (NLP).

Interview Process

The interview process for this role is thorough and includes multiple stages, primarily focused on evaluating your technical skills, machine learning knowledge, and problem-solving abilities. Here’s a detailed breakdown of what I experienced:

1. Application Screening

ByteDance begins by reviewing your resume and academic qualifications. The key areas they focus on include:

• Machine Learning Research: Make sure to highlight any research you’ve conducted on machine learning algorithms, especially if you have worked on foundational models or optimization techniques.
• Programming Skills: Experience with languages like Python, C++, or CUDA is a plus. Be sure to emphasize your proficiency in these areas.
• Publications: Any research papers or academic contributions in top conferences (e.g., NeurIPS, ICML) related to machine learning or AI systems will strengthen your application.

2. HR Interview

The first technical round I encountered was an HR interview. This interview is more focused on cultural fit and communication skills. The recruiter will ask about your background, motivations for applying, and your interest in ByteDance.

Example HR Questions:

• “What excites you about the opportunity to work at ByteDance, and why this specific internship?”
• “Tell us about a recent research project you’ve worked on that involved machine learning. What challenges did you face, and how did you solve them?”
• “How do you stay uppublishDated with the latest advancements in machine learning?”

This round is mainly about gauging your interest in the role, motivation, and communication skills.

3. Technical Interview - Machine Learning Knowledge

The next round was a technical interview where I was assessed on my knowledge of machine learning and distributed systems. The interviewer focused on evaluating my understanding of deep learning models, scalable systems, and data processing techniques.

Example Technical Questions:

Machine Learning Algorithms:

• “What are the differences between supervised and unsupervised learning? Can you give examples of when each is used?”
• “Explain gradient descent and how it is used in training machine learning models. What are some challenges you might face when using this method on large datasets?”

Deep Learning:

• “Can you explain the architecture of a transformer model? How does it differ from traditional RNNs and LSTMs?”
• “How would you address the vanishing gradient problem in deep networks?”

Distributed Systems:

• “Describe how you would design a distributed system for training large machine learning models (e.g., LLMs) across multiple GPUs. How would you handle model parallelism and data parallelism?”
• “What is Horovod or DeepSpeed? How do they help in distributed training?”

This round tested my understanding of theory, practical ML techniques, and my ability to explain complex concepts clearly. Be prepared to discuss distributed training, GPU utilization, and scalable machine learning infrastructure.

4. Coding Test

ByteDance also had me complete a coding challenge to test my problem-solving and coding skills in a real-world machine learning context. The problem was focused on data manipulation, algorithms, and implementing a simple ML task.

Example Coding Problem:

• “Write a Python function to implement K-means clustering from scratch. Given a dataset of points, implement the algorithm, and output the final centroids and cluster assignments.”

In this challenge, I had to demonstrate my coding ability, efficient use of algorithms, and clear implementation of machine learning concepts.

5. System Design Interview

In the system design interview, I was asked to design a scalable data pipeline for a video processing system that collects, stores, and analyzes video metadata at ByteDance scale. This round focused on how well I could design a system that integrates machine learning, big data frameworks, and cloud infrastructure.

Example System Design Question:

• “Design a system to process and store video metadata for millions of TikTok videos uploaded daily. The system must be scalable, fault-tolerant, and able to serve real-time queries. How would you design this system?”

To answer, I suggested:

• Data Ingestion: Use Apache Kafka for streaming video metadata in real-time.
• Data Storage: Store processed metadata in a NoSQL database (e.g., Cassandra or DynamoDB) for fast access.
• Processing Framework: Use Apache Spark or Flink for real-time data processing and transformation.
• Machine Learning Integration: Integrate with a recommendation engine or search system that uses processed metadata to personalize user feeds.

This round is focused on designing a robust, scalable system while integrating security, data integrity, and machine learning components.

6. Behavioral Interview

In the final round, ByteDance assessed my fit with their team and collaborative skills. I was asked about my previous teamwork experience, leadership capabilities, and how I handle high-pressure situations.

Example Behavioral Questions:

• “Tell us about a time when you had to overcome a technical challenge. How did you approach solving it, and what was the result?”
• “How do you manage conflicts within a team, especially when there are disagreements over technical solutions?”
• “Can you describe a project where you collaborated with cross-functional teams (e.g., data scientists, product managers)? How did you ensure successful collaboration?”

This round is meant to assess teamwork, communication, and your problem-solving mindset in real-world environments.

Final Thoughts

The Software Engineer Intern (Doubao Seed) - Machine Learning System role at ByteDance is an excellent opportunity for PhD students to work on cutting-edge machine learning systems at scale. The interview process is challenging but provides a great opportunity to demonstrate your technical expertise, problem-solving ability, and ability to work with large-scale data systems. By preparing for technical questions, system design, and behavioral interviews, you will be well-equipped to succeed in the process.

Tips for Success:

• Machine Learning Knowledge: Make sure you have a solid understanding of deep learning models, distributed systems, and optimization techniques.
• System Design: Practice designing large-scale systems that integrate machine learning, big data technologies, and cloud infrastructure.
• Communication: Be clear in explaining complex technical concepts to non-technical stakeholders or interviewers.
• Collaborative Skills: Highlight your ability to work in cross-functional teams, as this is critical in a product-driven organization like ByteDance.