Member of Technical Staff
About the role
Role at Activeloop focusing on building scalable AI/ML infrastructure with strong Python skills, owning feature development, APIs/microservices, and end-to-end delivery in a startup environment.
Responsibilities
- Architect and Develop: Design and build scalable software 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 Founder: Directly influence engineering strategy and roadmap decisions through close collaboration with the technical founder.
- Partner Cross-functionally: Work closely with ML, frontend and backend 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).
Qualifications
- Deep expertise in Python and modern software frameworks (e.g., FastAPI).
- Solid understanding of distributed systems, ideally applied to AI workloads
- Strong knowledge of SQL and NoSQL databases (PostgreSQL, Redis, MongoDB).
- Experience building RESTful APIs and microservices
- Demonstrated capability in implementing CI/CD pipelines and DevOps practices.
- Exceptional communication, collaboration, and problem-solving skills.
- History of owning complex product features from inception through support.
Skills
About Activeloop
Database for AI
We provide a simple API for creating, storing, versioning, and collaborating on multi-modal AI datasets of any size. With Activeloop's open-core stack, you can rapidly transform and stream data while training models at scale. Deep Lake powers foundational model training by acting as a vector database with significant benefits, such as (1) the ability to use multi-modal datasets to fine-tune your own LLM models, (2) storing both the embeddings and the original data with automatic version control, so no embedding re-computation is needed (3) truly serverless service with no vendor lock-in. How cool is that? GitHub loves us - we're one of the fastest-growing libraries there, and we're used by little-known companies like Google, Waymo, and Intel. No big deal. Our founding team hails from places like Princeton, Stanford, Google, and Tesla, and we're backed by Y Combinator & other Silicon Valley heavyweights. Activeloop is hiring, and we want you! Check out our open roles on our YC page and join the fun. 10-min demo: https://activeloop.wistia.com/medias/aibvo0dst2 Whitepaper: https://www.deeplake.ai/whitepaper