Machine Learning Engineer - Studio Domain

This is a Full Time Job

#WeAreParamount on a mission to unleash the power of content… you in?

We’ve got the brands, we’ve got the stars, we’ve got the power to achieve our mission to entertain the planet – now all we’re missing is… YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture.

SUMMARY

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.

KEY RESPONSIBILITIES

Domain-Grounded System Development
- 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.

Workflow Validation & Usability
- 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.

Applied Technical Contribution
- 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.

Evaluation & Quality Alignment
- 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.

Integration Readiness
- 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.

ACCOUNTABILITIES

- Relevance: Systems address real production workflows and user needs.
- Usability: Outputs align with practitioner expectations and working environments.
- Quality: Deliverables meet professional standards within the domain.
- Integration Readiness: Systems fit into existing pipelines with minimal rework.

REQUIRED QUALIFICATIONS

- 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.

PREFERRED QUALIFICATIONS

- 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.

CORE COMPETENCIES

- Domain Expertise: Deep understanding of real-world production workflows and constraints.
- Practical Judgment: Ability to define what is usable and production-ready in context.
- Technical Collaboration: Works proficiently with engineers while contributing directly where
needed.
- Quality Standards: Maintains a clear and consistent bar for professional output.
- Translation Ability: Converts practitioner needs into structured, testable system behaviors.