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

Applicant Tracking System Keywords

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

Hard Skills & Tools

machine learning engineeringPythondeep learningPyTorchTensorFlowmodel optimizationdata analysismodel evaluationgenerative AIGPU utilization

Soft Skills

problem-solvinganalytical skillscommunicationcross-functional collaborationdata-driven decision making

Certifications

PhDpostgraduate degree