About the Role

We are looking for a highly skilled Senior Machine Learning Engineer to build and scale next-generation generative AI systems. This role sits at the intersection of machine learning and backend infrastructure, focusing on taking advanced models from experimentation to reliable, high-performance production systems.

You will work on cutting-edge generative video and multimodal AI use cases, contributing to scalable, low-latency systems used by millions of users globally.

Key Responsibilities

• Design, train, fine-tune, and evaluate generative and multimodal models (e.g., text-to-video, image-to-video, lip-sync, character consistency)
• Build and manage end-to-end ML pipelines, including data ingestion, preprocessing, training, evaluation, and model versioning
• Deploy and maintain scalable ML systems, including model serving, containerization, and GPU-optimized inference
• Implement MLOps best practices such as experiment tracking, model monitoring, drift detection, and A/B testing
• Optimize inference systems for low latency, high throughput, and cost-efficient GPU utilization
• Develop batching and caching strategies to meet production SLAs
• Collaborate with backend and platform teams to integrate ML services into distributed systems
• Contribute to long-term AI strategy, including foundational model training and fine-tuning pipelines

Required Qualifications

• 4–10 years of experience in Machine Learning or Applied ML Engineering
• Strong fundamentals in deep learning, Transformers, and generative model architectures
• Hands-on experience with large-scale model training and fine-tuning (e.g., LoRA, full fine-tuning)
• Proven experience in deploying and scaling ML models in production environments
• Strong understanding of MLOps practices and tools (e.g., MLflow, Weights & Biases)
• Experience with model serving frameworks such as Triton, TorchServe, vLLM, or similar
• Proficiency in Python and frameworks like PyTorch
• Experience working with cloud platforms (AWS, GCP, or Azure), including GPU provisioning and autoscaling
• Ability to work in fast-paced, ambiguous environments with cross-functional teams

Preferred Qualifications

• Experience with video generation, diffusion models, or multimodal architectures
• Familiarity with LoRA/IC-LoRA techniques for character or identity consistency
• Knowledge of inference optimization techniques such as quantization (FP8/INT8), batching, and GPU memory management
• Experience with audio/video systems (e.g., TTS, voice cloning, lip-sync pipelines)
• Background in media, OTT, or large-scale content platforms

What We Offer

• Competitive compensation
• Opportunity to work on cutting-edge AI products at scale
• High-impact role with ownership across the ML lifecycle
• Collaborative and fast-paced work environment
• Continuous learning and growth opportunities