Applied Ml Engineer

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.

The Applied ML Engineer builds applied machine learning systems within a Production Platform Engineering pod. This role translates technical direction into working software, including model integrations, data pipelines, retrieval systems, evaluation instrumentation, and service layers. Working within defined execution cycles, the Applied ML Engineer delivers modular, testable systems that can be evaluated, integrated, and extended by downstream teams. The role focuses on implementation and iteration, not architecture ownership, model behavior definition, or system recovery.

Key Responsibilities

Applied ML System Development
• Implement machine learning systems, including model integrations, data pipelines, retrieval systems, evaluation instrumentation, and service layers.
• Translate technical direction into modular, maintainable codebases with clear interfaces.
• Deliver working artifacts at defined milestones, including code, configuration, tests, and documentation.

System Implementation & Iteration
• Iterate on systems based on evaluation results, domain feedback, and integration requirements.
• Refine performance, usability, and functionality within the scope of the system being built.
• Support rapid development cycles while maintaining code quality and reproducibility.

Evaluation Integration
• Implement evaluation hooks, metrics, and instrumentation defined by the ML Behavior Systems team.
• Ensure systems can be tested against defined benchmarks and quality standards.
• Support debugging and iteration based on evaluation outcomes.

Platform-Aligned Development
• Build systems using ML Platform & Operations infrastructure for training, inference, and deployment.
• Ensure compatibility with platform services, APIs, and constraints.
• Follow established patterns for system integration and deployment readiness.

Integration Readiness
• Design outputs as modular components with stable interfaces.
• Include configuration controls, observability hooks, and error handling required for integration.
• Partner with Platform Integration teams to ensure deliverables meet downstream requirements.

Accountabilities
• Delivery: Working systems are delivered within defined execution cycles.
• Code quality: Code is modular, readable, and maintainable by downstream teams.
• Evaluation readiness: Systems can be measured and validated against defined standards.
• Integration readiness: Outputs can be adopted without significant rework.

Required Qualifications
• 4 years of software engineering experience in backend, systems, or ML-adjacent environments.
• 2 years hands-on ML implementation.
• Proficient programming skills, particularly in Python, and experience building APIs or services.
• Working knowledge of machine learning systems, including model integration, evaluation, and debugging.
• Experience building end-to-end systems from unclear requirements to working software.
• Ability to operate in dynamic, iterative development environments.

Preferred Qualifications
• Experience building ML-enabled systems such as retrieval pipelines, agent workflows, or model-backed services.
• Familiarity with evaluation instrumentation, logging, and tracing in ML systems.
• Experience working with cloud-based infrastructure and distributed systems.
• Contributions to reusable libraries, frameworks, or internal platforms.

Core Competencies
• Execution focus: Translates direction into working systems proficiently.
• System implementation: Proficient ability to build across model, data, and service layers.
• Iteration discipline: Improves systems based on feedback and evaluation results.
• Integration awareness: Builds with downstream systems and constraints in mind.
• Collaboration: Works successfully within a pod and across engineering, domain, and integration teams.

ADDITIONAL INFORMATION