Senior​/Principal ML Systems Engineer

The AI company that's revolutionizing Hollywood

Flawless is transforming Hollywood with assistive AI. Our tools empower filmmakers to edit, localize, and refine performances while preserving artistic intent.

Designed to support, not replace, artists, our technology expands what is possible on screen and gives creators freedom to tell stories with greater impact and reach audiences in new ways. From enabling seamless multilingual releases to eliminating the need for costly reshoots, Flawless solves critical challenges that slow down productions and limit distribution.

We are also setting the standard for ethical AI in entertainment. Our Artistic Rights Treasury (A.R.T.) is a rights management solution that protects artists and rights holders, ensuring that innovation moves forward with transparency and respect for creative ownership.

Research Services builds the infrastructure that enables scientists to train, evaluate, and deploy models at scale - forming the foundation of Hollywood's AI transformation.

Our team sits at the intersection of large-scale data systems, machine learning, and high-performance computing. We own the full stack, from data ingestion and curation through distributed training and production inference, enabling researchers to move quickly while maintaining reliability and scalability.

This role focuses on building and optimizing systems for large-scale multimodal datasets, including video, embeddings, and metadata, ensuring they are fast, reliable, and production-ready.

We're looking for experienced ML Systems Engineers to join our Research Services team and help build the infrastructure that powers machine learning across Flawless.

This role is open across multiple levels, from Senior Engineer through Staff Engineer. The level and scope of responsibility will be determined based on your experience, technical depth, leadership impact, and track record of delivery.

As an ML Systems Engineer, you'll work closely with scientists, machine learning engineers, and platform teams to design and build the systems that underpin model development and deployment. You'll contribute hands‑on across data platforms, training infrastructure, evaluation systems, model lifecycle management, and production inference.

More senior candidates will be expected to provide technical leadership, drive architectural decisions, mentor other engineers, and influence infrastructure strategy across multiple initiatives.

• Build and evolve data platforms used to curate and manage large‑scale multimodal datasets.

• Design systems that index, process, and enrich thousands of videos through machine‑learning pipelines.

• Optimize data storage and access patterns for efficient model training and experimentation.

• Improve reliability, scalability, and observability across the data ecosystem.

• Build and optimize infrastructure for large-scale model training.

• Improve performance across single-node and distributed training environments.

• Scale data loading, preprocessing, and training workflows.

• Ensure training pipelines are reproducible, efficient, and easy to operate.

• Develop systems for collecting, storing, and analyzing model outputs.

• Build tooling for dataset exploration, experiment tracking, and model comparison.

• Enable scientists to iterate rapidly while maintaining robust evaluation practices.

• Design and maintain infrastructure for model versioning, experimentation, validation, and deployment.

• Improve reproducibility and governance across the machine learning lifecycle.

• Support the promotion of models from research through production.

• Build and optimize inference infrastructure for production workloads.

• Define and improve model serving protocols and deployment patterns.

• Enhance performance, reliability, and scalability of production inference systems.

We're interested in engineers who enjoy building systems that make machine learning teams more effective and productive.

• Experien…