Internship - Machine Learning Research Engineer

## About the Role Perplexity is seeking a highly motivated and talented individual to join their team as a Machine Learning Research Engineer intern. This internship program, based in Berlin, offers the opportunity to work on complex problems in search and retrieval systems, focusing on large-scale deep learning models and representation learning. The successful candidate will be part of a dynamic team that relentlessly pushes the boundaries of what is possible in search quality.

The role entails working closely with the research and engineering teams to develop and optimize large-scale deep learning models using frameworks like PyTorch. This includes leveraging distributed training techniques and hardware acceleration to improve model performance. The intern will also conduct research in representation learning, focusing on areas such as contrastive learning, multilingual modeling, and multimodal modeling for search and retrieval.

Given the nature of the work, the ideal candidate will have a strong academic background in computer science, machine learning, or a related field, with a proven track record of publications in AI/ML conferences or workshops.

## What You Will Do - Develop and optimize large-scale deep learning models using PyTorch, focusing on retrieval and ranking models.

• Conduct research in representation learning, including contrastive learning, multilingual, evaluation, and multimodal modeling for search and retrieval.
• Build and optimize RAG pipelines for grounding and answer generation.
• Train and fine-tune models using distributed training techniques such as PyTorch Distributed, DeepSpeed, and FSDP.
• Work on improving the performance of search and retrieval systems through models, data, tools, or other means.
• Collaborate with cross-functional teams to integrate research findings into product development.
• Design, implement, and evaluate experiments to measure the effectiveness of different approaches to search and retrieval.
• Stay up-to-date with the latest developments in machine learning and natural language processing, applying this knowledge to improve Perplexity's products and services.
• Contribute to the development of the company's research agenda, identifying new areas of investigation and proposing innovative solutions.

• Proficiency in PyTorch, including experience with distributed training techniques and performance optimization for large models.
• Experience with representation learning, including contrastive learning, dense & sparse vector representations, representation fusion, cross-lingual representation alignment, training data optimization, and robust evaluation.
• Publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR).
• Excellent programming skills in Python and experience with deep learning frameworks.
• Experience with distributed computing and hardware acceleration (e.g., GPUs, TPUs).
• Strong analytical and problem-solving skills, with the ability to interpret complex data and make informed decisions.
• Excellent communication and collaboration skills, with the ability to work effectively in a team environment.

• Knowledge of natural language processing (NLP) techniques and their application to search and retrieval.
• Experience with cloud computing platforms (e.g., AWS, GCP, Azure).
• Familiarity with agile development methodologies and version control systems (e.g., Git).

• Collaborative and dynamic work environment with a team of experienced professionals.
• Professional development opportunities, including training and education in the latest machine learning techniques.
• Flexible working hours and remote work options to ensure a healthy work-life balance.
• Access to the latest hardware and software technologies to support your work.
• A stipend for professional development and conference attendance to stay up-to-date with industry trends.

- Tip: Ensure your resume and cover letter highlight specific experiences with PyTorch and distributed training techniques, as these are key requirements for the role.

• Develop a portfolio of projects that demonstrate your skills in machine learning and natural language processing, including any publications or presentations you've given.
• Interview Preparation: Be ready to talk about your experience with large-scale deep learning models and how you've optimized them for performance.
• When negotiating salary, consider the cost of living in Berlin and the average salary range for machine learning engineers in the area.
• Red Flag: If the company seems unclear about the role's responsibilities or the team's dynamics, it may be a sign of disorganization or poor communication.
• Practice explaining complex technical concepts in simple terms, as this will be an important skill for communicating with both technical and non-technical team members.