Studio Domain ML Engineer

Paramount•New York, NY

13d

About The Position

The Studio Domain ML Engineer is a domain-experienced technical practitioner who contributes directly to the development of new applied machine learning technologies within a Production Platform Engineering pod. This role combines deep expertise in studio workflows with hands-on technical development, evaluation design, data preparation, model behavior validation, and production integration. Working alongside Applied ML Engineers, ML Behavior Systems, and Platform Integration teams, the Studio Domain ML Engineer helps shape emerging ML capabilities from early concept through production readiness. The role ensures that new ML technologies are grounded in real production workflows, informed by domain-specific quality standards, and validated against the practical constraints of film, television, animation, VFX, and related studio environments. This role contributes to the creation of production-relevant ML systems by translating complex studio workflows into structured requirements, representative datasets, evaluation criteria, edge-case scenarios, and testable system behaviors. The Studio Domain ML Engineer helps define what “good” means for professional production use, while also contributing technical artifacts such as validation scripts, data workflows, integration utilities, and evaluation assets that accelerate ML system development. By bridging advanced ML development with real-world studio practice, this role helps ensure that new technologies are not merely functional in isolation, but usable, reliable, and ready for adoption in demanding production environments.

Requirements

8+ years of experience within a film, media, or entertainment production domain (e.g., production, post-production, animation, VFX, studio technology, or related functions).
Demonstrated ability to translate real-world workflows and quality expectations into structured requirements.
Working proficiency in Python or similar scripting languages sufficient to contribute to system development and validation.
Experience collaborating with technical teams on tools, systems, or pipeline development.
Ability to operate in dynamic, iterative environments with evolving requirements.

Nice To Haves

Experience working with ML-enabled tools, data systems, or production technology initiatives.
Familiarity with evaluation frameworks, testing methodologies, or pipeline validation approaches.
Experience documenting workflows, standards, or domain practices for reuse.
Exposure to cross-functional collaboration between creative and engineering teams.

Responsibilities

Ensure systems align with real-world production workflows, tools, and operational constraints.
Define domain-specific acceptance criteria and quality standards for outputs.
Identify workflow mismatches early and guide teams toward solutions that fit existing production environments.
Validate prototypes and systems within realistic production scenarios, including edge cases and constraints.
Provide actionable feedback on usability, quality, and workflow integration.
Ensure outputs align with established domain standards, including formats, conventions, and downstream requirements.
Contribute directly to the development of new applied ML capabilities through validation scripts, data preparation workflows, evaluation datasets, prototype utilities, model behavior analysis, and integration tooling.
Partner with Applied ML Engineers to translate complex domain requirements into testable specifications, model behavior expectations, prototype workflows, and production-ready system behaviors.
Support evaluation design through representative examples, edge cases, and ground-truth data.
Work with the ML Behavior Systems Team to ensure systems meet defined evaluation and quality standards.
Validate that outputs meet professional expectations within the domain, beyond technical correctness.
Help interpret evaluation results in the context of real production use.
Ensure systems can be adopted within real production pipelines with minimal friction.
Validate compatibility with downstream tools, workflows, and operational environments.
Partner with Platform Integration teams to ensure outputs meet functional deployment requirements.