채용
Who we are
At Twelve Labs, we are pioneering the development of cutting-edge multimodal foundation models that have the ability to comprehend videos just like humans do. Our models have redefined the standards in video-language modeling, empowering us with more intuitive and far-reaching capabilities, and fundamentally transforming the way we interact with and analyze various forms of media.
With a $110+ million in Seed and Series A funding, our company is backed by top-tier venture capital firms such as NVIDIA’s NVentures, NEA, Radical Ventures, and Index Ventures, and prominent AI visionaries and founders such as Fei-Fei Li, Silvio Savarese, Alexandr Wang and more. Headquartered in San Francisco, with an influential APAC presence in Seoul, our global footprint underscores our commitment to driving worldwide innovation.
Our partnership with NVIDIA and AWS gives us access to the most advanced chips, including B300s, enabling us to push the boundaries of what's possible in video AI.
We are a global company that values the uniqueness of each person’s journey. It is the differences in our cultural, educational, and life experiences that allow us to constantly challenge the status quo. We are looking for individuals who are motivated by our mission and eager to make an impact as we push the bounds of technology to transform the world. Join us as we revolutionize video understanding and multimodal AI.
About the Team
The Pegasus team sits at the core of Twelve Labs' video understanding capabilities and is responsible for driving Pegasus, our Video Analysis product. Our focus is on developing multimodal video analysis systems that are designed for high instruction following capability and producing highly complex, hierarchically structured outputs. We focus on shipping products with real-world value rather than doing research in isolation, and we work in a goal-oriented, cross-functional team that encompasses both ML researchers and engineers.
Our work covers a broad range of challenges: large-scale distributed training of multi-modal LLMs that span from pre-training to RL, accurate temporal segmentation and structured metadata extraction for real-world use cases, extending temporal context length to multiple hours, and data curation processes that enable well-aligned evaluation and performance improvements through training data enhancements.
Our team has access to the most advanced chips in the world, including NVIDIA B300s, to push the boundaries of video analysis systems—accelerating our research-to-production cycle as fast as possible.
In this role, you will
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Build, improve, and operate production ML systems for Pegasus, with a focus on reliability, performance, and maintainability.
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Work across core parts of the ML stack, including deployment, inference, evaluation, monitoring, and supporting infrastructure.
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Develop systems for serving Video Language Models (VLMs) and handling multimodal data and metadata at production quality.
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Make strong technical decisions within your area and drive execution with a high degree of ownership.
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Explore and adopt AI-assisted development tools such as Claude, Gemini, and GPT to improve productivity across coding, experimentation, debugging, and documentation.
You may be a good fit if you have
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Strong software engineering and machine learning fundamentals.
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Experience building and shipping ML systems in production.
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Experience with multimodal data and familiarity with areas such as computer vision, natural language processing, LLMs, or VLMs.
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Experience with distributed ML or data workflows, ideally in Kubernetes-based environments.
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Strong engineering judgment around performance, reliability, and maintainability in production environments.
Preferred qualifications
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Experience serving or optimizing LLM/VLM systems in production.
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Experience with inference optimization techniques such as batching, caching, or quantization.
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Experience building AI/ML systems from early-stage development through production deployment.
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Master’s or PhD in Machine Learning, Computer Science, or a related technical field.
Hiring Process
Application Review → Recruiter Interview (비대면/30분) → Coding test → Hiring Manager Interview(비대면/30분) → Live Coding Test Interview (대면/135분) → System Design(비대면/105분) → Final Round 인터뷰(비대면/30분) → Reference Check → Offer
Benefits and Perks
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Growth & Tools
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글로벌 B2B 고객과 함께 성장하는 Global Team
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자율성과 협업을 모두 갖춘 하이브리드 근무
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최신 맥북 및 70만 원 상당 재택근무 장비 지원, 3년 주기로 최신 장비 교체
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Tokens never sleep
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Tech 직군 LLM 토큰 무제한 지원
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강의, 컨퍼런스, 멤버십 등에 사용 가능한 연 140만원 상당 자기개발비 지원
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영어 교육 프로그램 및 글로벌 버디 프로그램 운영
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야간 및 주말 출퇴근 택시비 지원
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Meal & Snack
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식비·교통비 등 자유롭게 사용할 수 있는 연 720만원 상당 법인카드 제공
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사무실 내 스낵바 운영 (간식, 커피, 제철 과일 등)
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사무실 근무 시, 오후 7시 이후 저녁 식대 제공
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Wellness & Family
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연 1회 본인 및 가족 1인의 건강검진 제공
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단체보험 가입 (상해보험/치아보험/가족 상해보험 중 택 1)
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독감 예방접종비 지원
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연말 2주간 유급 Holiday Break 운영
총 조회수
0
총 지원 클릭 수
0
모의 지원자 수
0
스크랩
0
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Twelve Labs 소개

Twelve Labs
Series ATwelve Labs is an AI company that develops video understanding technology using multimodal foundation models. The company provides APIs and tools for developers to build applications that can search, analyze, and generate insights from video content.
51-200
직원 수
San Francisco
본사 위치
리뷰
3.8
10개 리뷰
워라밸
4.2
보상
2.5
문화
4.0
커리어
2.8
경영진
3.2
65%
친구에게 추천
장점
Good work-life balance
Supportive team and environment
Friendly coworkers and team spirit
단점
Poor compensation/pay not competitive
Limited career advancement opportunities
Poor management and lack of direction
연봉 정보
5개 데이터
Senior/L5
Intern
Senior/L5 · Machine Learning Engineer
1개 리포트
$318,500
총 연봉
기본급
$245,000
주식
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보너스
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$318,500
$318,500
뉴스 & 버즈
Twelve Labs announced on the 1st that it has built an AI archive that allows users to quickly search.. - 매일경제
매일경제
News
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2w ago
Tried a bunch of “popular” AI tools for organizing recordings… some hot takes
I’ve been cleaning up a few months’ worth of recordings and video clips lately (meetings, random notes, saved content, etc.), so I figured I’d finally try some of the AI tools everyone keeps recommending. Still wanna pick one tool to be my go-to tbh. Just wanna say upfront, this is purely my personal experience. Not saying any of these are bad, just what worked / didn’t work for me.(no affiliate links, just sharing my feeling) - Otter.AI Probably the most well-known one. Transcription is solid
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3w ago
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3
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5
How do you actually go back through meeting recordings without it taking forever?
Been in grad school long enough that lab meetings have become their own kind of stress. My PI throws out ideas mid-sentence, keeps going, and I'm nodding, then I'm back at my desk and realize I've retained maybe half of it. Started recording everything a while back. (of course, with everyone’s consent before recording) It helped, but reviewing became its own problem. I'd scrub through an hour of audio trying to find one 15-second comment. Been trying a few different AI tools for this over the
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3w ago
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4
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4
Twelve Labs introduces video AI search on Gettyimagebank - 디지털투데이
디지털투데이
News
·
4w ago