Summary

We are seeking a skilled Machine Learning Engineer with a deep understanding of generative models, MLOps, and cloud-based machine learning infrastructure. In this role, you will work on cutting-edge projects involving diffusion models and Generative Adversarial Networks (GANs) to build, optimize, and deploy machine learning models for image and text data.

You will focus on hyperparameter tuning, custom loss functions, and serverless inferencing in a cloud environment, primarily using Google Cloud Platform (GCP) services. Experience with modern machine learning frameworks, model evaluation techniques, and CI/CD best practices is essential.

Key Responsibilities

Design, train, and optimize diffusion models and GANs for image generation and captioning tasks.
Implement and fine-tune custom loss functions tailored for diffusion models.
Perform hyperparameter tuning for generative models to achieve optimal performance.
Build and manage end-to-end MLOps pipelines on GCP using Vertex AI for streamlined model deployment and monitoring.
Develop and deploy serverless inferencing solutions using Vertex AI Predictions for scalable real-time model inference.
Evaluate model performance using metrics such as BLEU and CIDEr to ensure quality in image and caption generation tasks.
Implement and maintain CI/CD pipelines to ensure smooth integration and continuous delivery of machine learning models.
Collaborate with cross-functional teams to translate business objectives into ML models and solutions.
Stay up-to-date with the latest advancements in diffusion models, GANs, and cloud-based ML technologies.

Qualifications

Proven experience in designing, training, and optimizing diffusion models and GANs.
Strong understanding of custom loss functions in machine learning models, particularly for diffusion-based models.
Proficiency in PyTorch for developing and training machine learning models.
Hands-on experience with MLOps, including building and managing pipelines using GCP Vertex AI.
Expertise in serverless inferencing and deploying machine learning models using Vertex AI Predictions.
Familiarity with model evaluation metrics for image and caption tasks, such as BLEU, CIDEr, and other relevant performance measures.
Experience in implementing and maintaining CI/CD pipelines for machine learning workflows.
Nice to have: Knowledge of TPU architecture and optimization strategies for leveraging TPUs in model training.
Excellent problem-solving skills and ability to work in a fast-paced environment.
Strong collaboration and communication skills to work effectively within cross-functional teams.

Synthetic Turing Experience Technologies

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