Filtr uses AI to scan 1000+ jobs and finds postings that perfectly matches your resume

HeyGen aims to make visual storytelling accessible by building technology that equips more people with the power to reach, captivate, and inspire audiences. The company is seeking a Software Engineer to build and scale the foundational compute infrastructure that powers their AI models, addressing the challenges of costly and difficult-to-scale video creation.

Requirements

• 5+ years of full-time industry experience in large-scale MLOps, AI infrastructure, or HPC systems.
• Experience with data frameworks and standards like Ray, Apache Spark, LanceDB
• Strong proficiency in Python and a high-performance language such as C++ for developing core infrastructure components.
• Deep understanding and hands-on experience with modern orchestration and distributed computing frameworks such as Kubernetes and Ray.
• Experience with core ML frameworks such as PyTorch, TensorFlow, or JAX.
• Prior experience building infrastructure specifically for Generative AI models (e.g., diffusion models, GANs, or large language models) where cost and latency are critical.
• Expertise in GPU acceleration and deep familiarity with low-level compute programming, including CUDA, NCCL, or similar technologies for efficient inter-GPU communication.

Responsibilities

• Optimize GPU Utilization: Design and implement mechanisms to aggressively optimize GPU and cluster utilization across thousands of devices for inference, training, data processing and large-scale deployment of our state-of-art video generation models.
• Develop Large-Scale AI Job Framework: Build highly scalable, reliable frameworks for launching and managing massive, heterogeneous compute jobs, including multi-modal high-volume data ingestion/processing, distributed model training, and continuous evaluation/benchmarking.
• Enhance Observability: Develop world-class observability, tracing, and visualization tools for our compute cluster to ensure reliability, diagnose performance bottlenecks (e.g., memory, bandwidth, communication).
• Accelerate Pipelines: Collaborate closely with AI researchers and AI engineers to integrate innovative acceleration techniques (e.g., custom CUDA kernels, distributed training libraries) into production-ready, scalable training and inference pipelines.
• Infrastructure Management: Champion the adoption and optimization of modern cloud and container technologies (Kubernetes, Ray) for elastic, cost-efficient scaling of our distributed systems.

Other

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• Demonstrated Tech Lead experience, driving projects from conceptual design through to production deployment across cross-functional teams.
• Proven background in building and operating large-scale data infrastructure (e.g., Ray, Apache Spark) to manage petabytes of multi-modal data (video, audio, text).
• Dynamic and inclusive work environment.
• Opportunities for professional growth and advancement.