Job Description

Applied AI (AAI) is Meta’s organization focused on making our AI models best-in-class, starting with coding. Within AAI, the MultiMedia & MultiModality team covers the multimedia domain across every modality, on both the input and the output side of a model: image, video, audio, speech and music. We work directly with research, model-training and engineering partners across MSL, TBD and FAIR. Current problems include evaluating video experiences, diagnosing multimedia model behavior, producing domain-expert agent tasks, and building the data and measurement pipelines multimodal capabilities are trained and judged against.

About the role

We are hiring a senior engineer to lead this work end to end. You will take a modality or a capability area, decide what data is worth producing and how it should be measured, and carry it from an open question through to a pipeline that runs and a measurement the org relies on.

This is a multimodal role, not a text-only role. You will work across image, video, audio and speech, as model inputs and as model outputs, and the data and evaluations you own will cover media, not text alone.

The role sits close to research. You will translate what researchers need into data and evaluation the team can produce at scale, and bring their findings back into what we build next.

You will join a newly formed team, so setting direction, standards and review practice are a critical part of the job.

Responsibilities

Set technical direction for a modality or capability area.

• Determining where the team invests, what success means, and the tradeoffs behind both.
• Drive that direction across partner teams, not just inside your own.
• Design, create, and quality-review expert multimedia tasks and reference outputs for model training and evaluation.
• Define task guidelines, rubrics, and quality criteria; calibrate reviewers to apply them consistently.
• Build and harden evaluations.
• Make graders reliable, separate model failure from instrumentation failure, and reproduce and debug quality issues to resolution.
• Analyze failure modes in model outputs and propose new task types or data to close gaps.
• Design and build agentic workflows and pipelines, including human-in-the-loop and expert-in-the-loop designs, to automate data production and scale output past what manual authoring supports.
• Mentor engineers on the team, contribute to hiring and onboarding, and raise the bar on evaluation and quality practice.

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, a related technical field, or equivalent practical experience

• 8+ years of software engineering experience, or a PhD plus 5 years, including significant depth in one or more media modalities (video, image, audio, speech, or music)
• Demonstrated experience designing and building multimedia pipelines, workflows or tooling
• Working understanding of how models are trained and evaluated, and of how data quality and coverage shape model behavior
• Experience owning software components or systems end to end, and driving work with cross-functional partners
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Hands-on experience evaluating or red-teaming multimodal models, or creating the data used to improve them
• Experience designing benchmarks or evaluations for model capability, with attention to grading reliability, reproducibility and label quality
• Working knowledge of common video, audio and streaming standards (e.g. H.265, MPEG-DASH, WebRTC)
• Fluency with professional media tooling (e.g. Premiere, After Effects, Blender, Pro Tools). We care about this because authoring tasks a model cannot solve requires knowing what expert media work actually looks like
• Experience building data pipelines for image, video, audio, speech or complex media formats, including versioning, lineage and provenance
• Experience designing AI agents, orchestration, or human-in-the-loop systems
• Experience working directly with researchers and translating research needs into engineering and evaluation work
• Understanding of Responsible AI practices and building quality controls into AI output
• Experience with zero-to-one work: forming a charter and standing up process while priorities are still moving
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies