Senior ML Engineer

We're looking for a hands-on ML Engineer who combines strong machine learning fundamentals with the backend infrastructure instincts needed to take models from research into reliable, scalable production systems. This role sits at the intersection of cutting-edge generative video AI and the MLOps discipline required to run it at scale. You'll work closely with backend, platform, and content teams to deliver high-performance ML components under strict quality, latency, and throughput requirements.

Responsibilities:

• Train, fine-tune, and evaluate generative video and multimodal models (image-to-video, text-to-video, lip-sync, character consistency).
• Build and manage end-to-end ML pipelines: data ingestion, preprocessing, training, evaluation, and versioning.
• Own model deployment and serving infrastructure containerization, GPU-optimized inference, model registries, and rollout strategies.
• Implement MLOps best practices: experiment tracking, model monitoring, drift detection, A/B testing, and observability.
• Design and maintain scalable inference systems optimized for low latency, high throughput, and cost-efficient GPU utilization.
• Develop caching and batching strategies to meet SLA targets in production video generation workflows.
• Collaborate with backend engineering teams on integrating ML services into distributed systems.
• Contribute to the long-term roadmap: foundational model training strategies, LoRA fine-tuning pipelines, and multi-character generalization.

Requirements:

• 4-10 years of experience in Machine Learning / Applied ML Engineering.
• Strong fundamentals in deep learning, Transformers, and generative model architectures.
• Hands-on experience with model training at scale, including fine-tuning large models (LoRA, full fine-tune) on custom datasets.
• Solid MLOps experience: experiment tracking (MLflow, W& B), CI/CD for ML, model versioning, and serving frameworks (Triton, TorchServe, vLLM, or equivalent).
• Strong Python skills and fluency with PyTorch and the modern ML stack.
• Experience deploying and operating ML systems in distributed cloud environments (GCP, AWS, or Azure); GPU provisioning, autoscaling, and cost management.
• Comfort working on ambiguous, high-impact problems with cross-functional teams.

Preferred Qualifications:

• Experience with video generation, diffusion models, or multimodal architectures (DiT, U-Net, audio-video joint models).
• Familiarity with LoRA/IC-LoRA fine-tuning workflows for character or identity consistency.
• Experience in media, OTT, sports, or large-scale content platforms.
• Knowledge of inference optimization techniques: quantization (FP8/INT8), batching, async orchestration, and GPU memory management.
• Exposure to TTS/voice cloning systems or audio-video synchronization pipelines.