Drimva

Meta

Software Engineer, Multimedia & Multimodal AI

$184K–$257K

Bellevue, WA +2 · Full-time · Posted Sep 23, 2026

Location
Bellevue, WA
Menlo Park, CA
New York, NY
Experience
8+ years of software engineering experience, or a PhD plus 5 years.
Education
Bachelor's degree in Computer Science, Computer Engineering, a related technical field, or equivalent practical experience.

About the job

Leads multimedia data and evaluation work for AI models, setting technical direction and building workflows and pipelines across image, video, audio, speech, and music.

What you'll do

  • Set direction for a modality or capability area and align partner teams on priorities and success measures.
  • Create and review expert multimedia tasks, guidelines, rubrics, and reliable model evaluations.
  • Build data-production and evaluation pipelines, diagnose quality issues, and mentor engineers.

What they ask for

  • 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.
  • Experience designing and building multimedia pipelines, workflows, or tooling; owning systems end to end; and working with cross-functional partners.
  • Hands-on experience evaluating or red-teaming multimodal models or creating improvement data; experience designing benchmarks or evaluations.

Nice to have

  • Working knowledge of video, audio, and streaming standards (e.g. H.265, MPEG-DASH, WebRTC) and professional media tools (e.g. Premiere, After Effects, Blender, Pro Tools).
  • Experience with AI agents, human-in-the-loop systems, research partnerships, responsible AI, or zero-to-one work.

Pay

$183,997/year to $257,000/year (base salary)

Tools

H.265, MPEG-DASH, WebRTC, Premiere, After Effects, Blender, Pro Tools

AI focus

Builds multimedia training data, evaluations, and agentic or human-in-the-loop workflows for multimodal AI models; analyzes model failures and improves data quality and coverage.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 4, 2026 (UTC)