Machine Learning Engineer - Diffusion Models & Deep Learning - PetsApp : Job Details

We are seeking a highly skilled Machine Learning Engineer with hands-on experience in diffusion models, deep generative modeling, and deployment of AI systems using TensorFlow or PyTorch. The ideal candidate will work on cutting-edge projects involving denoising diffusion probabilistic models (DDPMs), latent diffusion models (LDMs), or related architectures to develop state-of-the-art generative AI applications.

This role requires a deep understanding of machine learning, model optimization, and efficient large-scale inference. The candidate will also work closely with data engineers, research scientists, and software engineers to bring production-ready AI solutions to market.

Key Responsibilities

• Design, develop, and optimize diffusion models (DDPMs, LDMs) for tasks such as image generation, text-to-image synthesis, or noise-based denoising techniques.

• Implement and fine-tune deep learning models using PyTorch or TensorFlow for generative AI applications.

• Develop scalable and efficient ML pipelines for training and inference using multi-GPU, TPU, or distributed computing environments.

• Optimize models for latency, memory efficiency, and performance through techniques such as quantization, pruning, distillation, and mixed-precision training.

• Integrate diffusion models into production systems, including API endpoints, cloud-based inference, and real-time processing.

• Collaborate with research teams to experiment with new architectures and improvements in generative AI.

• Utilize cloud services (AWS, GCP, Azure) and MLOps tools (MLflow, Kubeflow, ONNX, TensorRT) to deploy and monitor models.

• Keep up-to-date with state-of-the-art generative modeling research and implement innovative methodologies in projects.

Required Qualifications

Experience with Diffusion Models:

• Strong knowledge of denoising diffusion probabilistic models (DDPMs), stable diffusion, latent diffusion models (LDMs), or similar generative AI techniques.

• Hands-on experience implementing diffusion models from research papers and deploying them in real-world applications.

Deep Learning & Model Optimization:

• Expertise in deep learning architectures (CNNs, VAEs, GANs, Transformers, or ResNets) for generative modeling.

• Proficiency in TensorFlow or PyTorch with experience in writing custom training loops, fine-tuning, and debugging large models.

• Understanding of latent space representation, noise scheduling, and generative priors in deep generative models.

Efficient Model Training & Deployment:

• Experience with multi-GPU/TPU training, data parallelism, model parallelism, and distributed training frameworks.

• Knowledge of model acceleration techniques (e.g., ONNX, TensorRT, quantization, mixed precision training, JIT compilation, XLA optimization).

Software Engineering & MLOps:

• Strong proficiency in Python and experience with containerized deployment (Docker, Kubernetes, FastAPI, Flask, etc.).

• Experience working with cloud services (AWS, GCP, or Azure) for training and deployment.

• Familiarity with MLOps workflows, versioning, and monitoring tools such as MLflow, Kubeflow, or Weights & Biases.