Research Engineer L5 - Machine Learning Efficiency

Job Description

Members can watch as much as they want, anytime, anywhere, on any internet-connected screen. As Netflix continues to grow, we are venturing into exciting new frontiers of personalization and discovery to help our members find the content they'll enjoy the most.

Fast-paced innovation in the theory and practice of large language models (LLMs) and other foundation models is greatly helping to advance state-of-the-art in personalization and discovery experiences. However, cost-effective and efficient training and serving of the models at Netflix's scale is a technical challenge. Hence we are looking for an exceptional applied research engineer to help us develop the technology that would enable efficient training and serving of these models.

In this role, you will aid applied research and product development by conceptualizing, designing, and implementing engineering improvements related to large-scale deep neural networks. You would have proven expertise in efficiency optimizations using techniques such as quantization, model pruning, distillation, compute-efficient finetuning, etc. You have to be deeply knowledgeable in ML hardware and software to be successful in this role. Additionally, you need solid software development skills, a love of learning, a passion for solving problems, a bias to action, and effective collaboration with scientists.

What we are looking for:
• 5+ years of software engineering experience with a track record of delivering quality results.
• Proven expertise in training and serving infrastructure for LLMs and other large foundation models.
• Strong problem-solving skills with knowledge of statistical methods.
• Strong software development experience in languages such as Python and Java.
• Deep understanding of TensorFlow and/or PyTorch.
• Familiarity with hardware and software accelerators and GPU-based optimizations
• Great interpersonal skills.
• Strong communication skills - written and verbal.
• Graduate degree in Computer Science, Statistics, or a related field.

Preferred, but not required, additional areas of experience:
• Experience as a technical leader.
• Experience working with cross-functional teams.
• Experience in Search, Recommendations, Natural Language Processing, Knowledge Graphs, Conversational Agents, and Personalization.
• Experience with Spark or other distributed computed platforms.
• Experience with cloud computing platforms and large web-scale distributed systems.
• Experience in applied research in industrial settings.
• Open source contributions.
• Research publications at peer-reviewed journals and conferences on relevant topics.

Links to some of our published work:
• Synergistic Signals: Exploiting Co-Engagement and Semantic Links via GNN - Under review.
• Lessons Learnt From Consolidating ML Models in a Large-Scale Recommendation System
• Search Personalization at Netflix - PaRiS Workshop - WebConf 2023.
• Augmenting Netflix Search with In-Session Adapted Recommendations - RecSys 2022
• Query Facet Mapping and its Applications in Streaming Services - SIGIR 2022
• Recommendations and Results Organization in Netflix Search - RecSys 2021
• Challenges in Search on Streaming Services: Netflix Case Study - SIGIR 2019
• Netflix Research site
Our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $100,000 - $720,000.

Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more detail about our Benefits here.

Netflix is a unique culture and environment. Learn more here.

We are an equal-opportunity employer and celebrate diversity, recognizing that diversity of thought and background builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.

Jobcode: Reference SBJ-gqxbxm-3-144-17-45-42 in your application.