
Deep Lake is a Database for AI powered by a unique storage format optimized for deep-learning and Large Language Model (LLM) based applications (http://github.com/activeloopai/deeplake; 8K+ stars). It simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, etc.), querying and vector search, data streaming while training models at scale, data versioning and lineage for all workloads, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more. Deep Lake works with data of any size, it is serverless, and it enables you to store all of your data in one place. Deep Lake is used by Intel, Matterport, Hercules.ai, Red Cross, Yale, & Oxford. Try out Deep Lake today via app.activeloop.ai Activeloop's founding team is from Princeton, Stanford, Google, and Tesla, and is backed by Y Combinator.

Deep Lake is a Database for AI powered by a unique storage format optimized for deep-learning and Large Language Model (LLM) based applications (http://github.com/activeloopai/deeplake; 8K+ stars). It simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, etc.), querying and vector search, data streaming while training models at scale, data versioning and lineage for all workloads, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more. Deep Lake works with data of any size, it is serverless, and it enables you to store all of your data in one place. Deep Lake is used by Intel, Matterport, Hercules.ai, Red Cross, Yale, & Oxford. Try out Deep Lake today via app.activeloop.ai Activeloop's founding team is from Princeton, Stanford, Google, and Tesla, and is backed by Y Combinator.
Product: Deep Lake — open-source and commercial database optimized for multimodal AI data and tensors
Founded: 2018
Founders: Davit Buniatyan, Sergiy Popovych, Jason Ge
Notable recognition: Named a 2024 Gartner Cool Vendor in Data Management
Known funding: $5M seed announced Nov 2021; Series A closed Mar 26, 2024 (amount not specified)
Storage and management of large-scale multimodal datasets for machine learning and LLM workflows.
2018
Software Development
$5,000,000
Participants included Tribe Capital and Shasta Ventures
Series A closed (amount obfuscated in available records)
We are looking for an experienced Staff Backend Engineer with deep expertise in backend engineering, Go proficiency, advanced software development skills, and proven capabilities in architecting scalable platforms tailored for AI and ML workloads. As a core technical leader, you'll influence strategic decisions, partner closely with the founding team, and play a critical role in shaping Activeloop's AI infrastructure.
What You Will Be Doing
Architect and Develop: Design and build scalable backend infrastructure optimized for high-performance AI and ML workloads, ensuring robustness and maintainability.
Lead Feature Development: Own end-to-end delivery of key product features, from initial concept through deployment and support.
Innovate APIs and Microservices: Define efficient RESTful APIs and microservice architectures tailored to customer-driven use cases.
Scale Distributed Systems: Implement systems designed for high-throughput, low-latency, large-scale data processing.
Enhance Scalability: Drive strategic evolution of system architecture to proactively handle future scalability and performance demands.
Implement CI/CD Pipelines: Develop streamlined CI/CD workflows for rapid testing, deployment, and feature iteration.
Collaborate with Technical Leadership: Directly influence engineering strategy and roadmap decisions through close collaboration with the technical leadership.
Mentorship and Optimization: Mentor engineers, establish development best practices, and continuously improve engineering processes.
Partner Cross-functionally: Work closely with product team, ML engineers, and frontend, backend, and full-stack engineers to seamlessly deliver features aligned with business objectives.
Enhance Reliability: Ensure platform reliability and uptime through advanced monitoring, logging, and alerting using modern DevOps tools (Docker, Kubernetes, AWS, GCP, Azure).
What We Need to See
7+ years of backend engineering experience with significant contributions to scalable platform development.
Deep expertise in Go, high-performance backend services, and modern API frameworks.
Proven experience in architecting and scaling distributed systems tailored for AI workloads.
Strong knowledge of PostgreSQL and databases (PostgreSQL, Redis, MongoDB).
Extensive experience in developing RESTful APIs and microservices.
Demonstrated capability in implementing CI/CD pipelines and DevOps practices.
History of owning complex product features from inception through support.
Exceptional communication, collaboration, and problem-solving skills.
Bachelor's or higher degree in Computer Science, Software Engineering, or equivalent practical experience.
Deep interest in or hands-on experience with distributed databases, data lake technologies, and AI agents.
Proven comfort with ambiguity, rapid iteration, and adaptive decision-making in startup environments.
Enthusiasm for directly incorporating customer feedback into product development.
Proactive in anticipating technical challenges and providing innovative solutions.
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