Senior ML Engineer

Senior Machine Learning Engineer

Responsibilities:

• Research & Develop

  : Design and implement

  state-of-the-art generative models

  , including

  Diffusion Models, 3D VAEs and GANs

  for

  AI-powered media synthesis

  .

• End-to-End Content Generation

  : Build and optimize AI pipelines for

  high-fidelity image/video generation and lipsyncing

  using diffusion and autoencoder models.

• Speech & Video Synchronization

  : Develop advanced

  lipsyncing and multimodal generation models

  that integrate

  speech, video, and facial animation

  for hyper-realistic AI-driven content.

• Real-Time AI Systems

  : Implement and optimize models for

  real-time content generation and interactive AI applications

  using

  efficient model architectures and acceleration techniques

  .

• Scaling & Production Deployment

  : Work closely with software engineers to

  deploy models efficiently on cloud-based architectures (AWS, GCP, or Azure)

  .

• Collaboration & Research

  : Stay ahead of the latest trends in

  deep generative models, diffusion models, and transformer-based vision systems

  to enhance AI-generated content quality.

• Experimentation & Validation

  : Design and conduct experiments to evaluate model performance, improve

  fidelity, realism, and computational efficiency

  , and refine model architectures.

• Code Quality & Best Practices

  : Participate in code reviews, improve model efficiency, and document research findings to enhance

  team knowledge-sharing and product development

  .

Qualifications:

• Bachelor's or Master’s degree

  in Computer Science, Machine Learning, or a related field.

• 3+ years

  of experience working with

  deep generative models

  , including

  Diffusion Models, 3D VAEs, GANs and autoregressive models

  .
• Strong proficiency in

  Python

  and deep learning frameworks such as

  PyTorch.

• Expertise in

  multi-modal AI, text-to-image, and image-to-video generation

  ,

  audio to lipsync

• Strong understanding of machine learning principles and statistical methods.
• Good to have experience in

  real-time inference optimization, cloud deployment, and distributed training

  .
• Strong problem-solving abilities and a research-oriented mindset to stay updated with the latest AI advancements.
• Familiarity with

  generative adversarial techniques, reinforcement learning for generative models, and large-scale AI model training

  .

Preferred Qualifications:

• Experience with

  transformers and vision-language models

  (e.g., CLIP, BLIP, GPT-4V).
• Background in

  text-to-video generation, lipsync generation and real-time synthetic media applications

  .
• Experience in

  cloud-based AI pipelines (AWS, Google Cloud, or Azure)

  and

  model compression techniques (quantization, pruning, distillation)

  .
• Contributions to open-source projects or published research in

  AI-generated content, speech synthesis, or video synthesis

  .