Senior Software Engineer, Backend

Who We Are: 

At TwelveLabs, 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 remarkable $107 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.

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 Role

As a Senior Product Backend Engineer at TwelveLabs, you’ll architect scalable APIs and systems to power our AI video platform. You’ll collaborate with cross-functional teams to integrate video foundation models, optimizing for performance and adaptability in a dynamic startup environment.

While this role is remote eligible for candidates residing in the United States, please note that remote employees will need to travel to the office for team meetups or other events as needed. Interviewing in person in San Francisco will be required.

In this role, you will

• Design and implement scalable RESTful APIs adhering to OpenAPI specifications, powering features like video search, generation, and embedding, integrated with model inference pipelines.

• Architect high-throughput, service-oriented backend systems to support enterprise-grade SaaS solutions for diverse customers, leveraging cloud-native tools (e.g., AWS, GCP, Azure).

• Optimize performance and reliability of distributed systems, processing large-scale video data with low latency and high availability.

• Collaborate with cross-functional teams (product managers, frontend engineers, AI/ML teams) to deliver end-to-end video solutions.

• Apply video-specific technologies (e.g., encoding, transcoding, streaming, metadata extraction) to enhance product capabilities and meet strategic goals.

You may be a good fit if you have:

• Experience: 5+ years of backend engineering experience, with a proven track record of designing and delivering scalable web services and APIs.

• API Expertise: Advanced proficiency in designing and implementing RESTful APIs, adhering to OpenAPI/Swagger specifications, with experience in modern frameworks (e.g., Go’s Gin or Echo, Spring Boot, or similar).

• Technical Expertise: Deep expertise in service-oriented architecture (SOA), microservices, and distributed systems, with strong knowledge of scalable database design (e.g., relational, NoSQL) and effective use of event-driven architecture.

• Cloud Proficiency: Extensive experience with cloud-native development and deployment on platforms like AWS, GCP, or Azure, leveraging tools such as Docker, Kubernetes, or serverless frameworks to ensure scalability and resilience.

• Analytical & Collaborative Skills: Strong first-principles thinking to address complex technical challenges, combined with effective communication skills and a collaborative approach to working with cross-functional teams.

Preferred Qualifications: 

• AI/ML Familiarity: Strong understanding of AI/ML concepts, particularly related to video analysis (e.g., object detection, motion tracking, or video summarization), and experience integrating backend systems with AI models or data pipelines.

• Video Technology Experience: Hands-on knowledge of video-specific tools and frameworks (e.g., FFmpeg, AWS Media Services) to support video processing workflows.

• Startup Agility: Experience thriving in fast-paced startup environments, with a demonstrated ability to adapt quickly and deliver results with agility

• Go Proficiency: Proficiency with Go (Golang) and its ecosystem, aligning wit