Senior ML Engineer

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. In addition to the above responsibilities and qualifications, this role offers competitive compensation, the opportunity to work on cutting-edge AI products at scale, a high-impact role with ownership across the ML lifecycle, a collaborative and fast-paced work environment, and continuous learning and growth opportunities. As a highly skilled Senior Machine Learning Engineer, your role will involve building and scaling next-generation generative AI systems that contribute to cutting-edge generative video and multimodal AI use cases. You will work on scalable, low-latency systems used by millions of users globally, focusing on taking advanced models from experimentation to reliable, high-performance production systems. Key Responsibilities:

Design, train, fine-tune, and evaluate generative and multimodal models such as text-to-video, image-to-video, lip-sync, and 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 like 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 the long-term AI strategy, including foundational model training and fine-tuning pipelines. Qualifications Required:

410 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.