Senior Applied Machine Learning Engineer, NEMO Microservices

at  NVIDIA

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
We are looking for a Senior Applied Machine Learning Engineer to help build NeMo Microservices Suite Platform. Our team is building next-generation AI services and interfaces for training and fine-tuning machine learning models and deploying AI at scale.We are dedicated to developing speech, vision, and NLP technologies that tackle real problems. We contribute to all steps of the machine learning lifecycle: from conceptualization, to applied research, engineering for optimized inference, and deployment.

What you’ll be doing:

• Development of new generation of Compound AI Systems platform with reasoning capabilities that supports working across multiple modalities including but not limited to images, videos, audio, and text.
• Development of distributed cloud applications, microservices and MLOps platforms able to scale up to huge models
• Creating microservices for task-specific AI cloud services
• Implementing core infrastructure for cloud-native AI training and inference
• Relentlessly pursue speed of light performance under high load

What we need to see:

• BS, Masters, or equivalent experience in computer science, computer architecture, or related field
• 5+ years of experience
• Exceptional coding skills, striving for creating high-quality software
• Ability to work independently, define project goals and scope, interact directly with open-source community, and manage your own development effort
• Experience implementing microservices and cloud-native applications using HTTP REST, gRPC, protobuf, JSON and related technologies
• Experience deploying application on Kubernetes platform, familiarity with helm charts, kustomize, k8s operator.
• Understanding of performance, security, and reliability in complex distributed infrastructure
• Excellent Python or Golang programming and software design skills, including debugging, performance and service health analysis, and test design.

Ways to stand out from the crowd:

• Experience deploying machine learning or statistical models into production environments, especially experience with frameworks such as PyTorch, Tensorflow, ONNX Runtime, and TensorRT
• Background with deep learning frameworks such as Megatron Core, NeMo, HuggingFace Accelerate, HuggingFace Transformers, DeepSpeed, and similar
• Experience with MLOps orchestration platforms such as Seldon Core, Kserve, BentoML and similar
• Experience with inference engines such as VLLM, TensorRT-LLM and similar
• Knowledge of or experience with developing production NLP systems as well as experience working with high availability environments

The base salary range is 148,000 USD - 276,000 USD. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.
You will also be eligible for equity and
benefits
. NVIDIA accepts applications on an ongoing basis.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

• Development of new generation of Compound AI Systems platform with reasoning capabilities that supports working across multiple modalities including but not limited to images, videos, audio, and text.
• Development of distributed cloud applications, microservices and MLOps platforms able to scale up to huge models
• Creating microservices for task-specific AI cloud services
• Implementing core infrastructure for cloud-native AI training and inference
• Relentlessly pursue speed of light performance under high loa