You'll build and optimize infrastructures that power large foundation model training on thousands of GPUs
You will profile GPU utilization, trace inference and training runs and help craft strategies for optimizing our ML model latency.
We'll work together to architect and optimize end-to-end ML pipelines, ensuring they're scalable, efficient, and robust.
You'll dive deep into data to recommend the right models, evaluation metrics, and governance approaches.
Throughout the product lifecycle, you'll engage in architecture, design, deployment, and optimizations of ML models and systems.

Requirements

Graduate, PhD, or postgraduate degree in Computer Science, Computer Engineering, or a related field—or equivalent experience.
2+ 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, will be essential.
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, and experience with cloud platforms like Azure and AWS is a plus.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

Pythondeep learningML Engineeringgenerative AILLMsPyTorchTensorFlowdistillationtransformersdiffusion models

Certifications

PhDpostgraduate degreegraduate degree