Research Engineer L5 - Machine Learning Efficiency
$100,000–$720,000 year
On-siteLos Gatos, California, United States
Job Summary
Conceptualize, design, and implement engineering improvements for large-scale deep neural networks to enable efficient training and serving of models at Netflix's scale. Execute efficiency optimizations using techniques such as quantization, model pruning, distillation, and compute-efficient finetuning while leveraging deep knowledge of ML hardware and software. Collaborate with scientists and cross-functional teams to deliver quality results using Python, Java, TensorFlow, and PyTorch, with a focus on GPU-based optimizations and distributed computing platforms. Requires 5+ years of software engineering experience, a graduate degree in Computer Science or Statistics, and proven expertise in LLM infrastructure. Compensation ranges from $100,000 to $720,000 annually, with comprehensive benefits including stock options, health plans, and 35 days of paid time off.
Required Qualifications
- 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
Desired Qualifications
- 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
Hiring someone like this?
Get your role in front of qualified candidates on Sorce.