Systems software expert in perception fusion and deep learning for autonomous vehicles at NVIDIA. Designing and prototyping multimodal perception solutions for enabling autonomous driving features across OEM partners.
About the role
- ML infra engineer tasked with building large-scale infrastructure for generative AI at Adobe. Involves extensive experience in deep learning, PyTorch, and cloud platforms.
Responsibilities
- Build and optimize infrastructures that power large foundation model training on thousands of GPUs
- Profile GPU utilization, trace inference and training runs and help craft strategies for optimizing ML model latency.
- Architect and optimize end-to-end ML pipelines, ensuring they're scalable, efficient, and robust.
- Dive deep into data to recommend the right models, evaluation metrics, and governance approaches.
- Engage in architecture, design, deployment, and optimizations of ML models and systems throughout the product lifecycle.
Requirements
- Graduate, PhD, or postgraduate degree in Computer Science, Computer Engineering, or a related field—or equivalent experience.
- 5+ years ML Engineering experience, specializing in generative AI like LLMs.
- Strong Python and deep learning engineering skills, paired with experience in training and inferencing with PyTorch or TensorFlow.
- Familiarity with distillation, transformers, and diffusion models.
- Experience with generative image and video is a plus.
- Knowledge of deployment technologies such as Docker, ML Ops, and ML services is valuable.
- Experience with cloud platforms like Azure and AWS is a plus.
- Excellent problem-solving abilities and your capacity to analyze complex issues and drive solutions with a data-driven approach.
- Strong verbal and written communication skills and success in cross-functional team environments.
Benefits
- Health insurance
- 401(k) retirement plans
- Paid time off
- Professional development opportunities
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