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/aibvo0dst2Whitepaper: https://www.deeplake.ai/whitepaper

Skills: C++, Python, TensorFlow, Deep Learning, Reinforcement learning (RL)

We're looking for an AI Search Engineer who possesses a deep understanding of large-scale information retrieval systems, deep learning, databases, and RAG architectures. The ideal candidate will have expertise in developing and optimizing search algorithms, implementing efficient indexing techniques, and leveraging RAG to enhance AI-powered search and question-answering systems.

What You Will Be Doing

As an AI Search Engineer, you will play a pivotal role in designing, developing, and deploying advanced search and retrieval systems that leverage RAG techniques to solve complex information access challenges. You will collaborate with software engineers, customers, and business stakeholders to develop AI search solutions that deliver significant value to the organization and our clients.

Key Responsibilities

RAG System Research and Implementation: Lead the design and implementation of advanced retrieval systems like Deep Memory by Activeloop, delivering optimized RAG systems across the entire value chain - from embedding or model fine-tuning to retrieval optimization with custom algorithms, to enhance knowledge retrieval accuracy.

Search Algorithm Optimization: Develop and refine search algorithms, including semantic search, hybrid search, and multi-modal search techniques, to improve retrieval performance and relevance ranking.

Vector Database Integration : Implement and optimize vector storage and indexing solutions within Deep Lake, ensuring efficient similarity search capabilities for high-dimensional embeddings used in RAG systems.

Query Understanding and Processing: Design and implement advanced query processing pipelines, including query expansion, intent recognition, and contextual interpretation to enhance search precision.

Information Retrieval Model Development: Create and fine-tune machine learning models specifically for information retrieval tasks, such as document ranking, query-document relevance scoring, and zero-shot retrieval.

Performance Evaluation and Metrics: Establish comprehensive evaluation frameworks for search and RAG systems, including relevance assessments, A/B testing, and user satisfaction metrics to continually improve system performance.

Scalability and Efficiency: Optimize RAG and search systems for high throughput and low latency, ensuring they can handle large-scale datasets and real-time query processing demands.

Data Ingestion and Indexing: Develop efficient data ingestion pipelines and indexing strategies to support rapid updates and real-time search capabilities across diverse data types and sources.

What We Need To See

Master's or PhD degree in Computer Science, Machine Learning, Statistics, or a related field.
Strong programming skills in one or more programming languages, such as Python, or C++, and extensive experience with machine learning libraries, such as TensorFlow, PyTorch, Llama Index, LangChain etc.
Proven experience in developing and deploying complex machine learning models in production environments, including experience with cloud-based platforms, edge devices, or embedded systems.
Strong understanding of advanced machine learning algorithms, such as deep learning, reinforcement learning, RAGs, and ensemble methods, and experience with model optimization techniques, such as hyper-parameter tuning, model compression, and quantization.
Solid understanding of data pre-processing, feature engineering, and data quality assurance techniques, and experience with large-scale, complex data sets.
Excellent problem-solving skills and ability to analyze and interpret complex data sets to extract meaningful insights and drive decision-making.

Ways To Stand Out From The Crowd

You have trained deep learning models in a distributed manner.
You are a highly motivated, curious, hardworking explorer in the field of AI
You have publications in top-tier machine learning and AI conferences such as ICML, NeurIPS, and CVPR (highly desirable).
You have a builder attitude and you love building cool things that matter
You're excited to work closely with the founding team in developing hyper-scalable software for ML
You can proactively identify and anticipate problems and provide tangible solutions
You enjoy the startup journey of building an endurable, scalable business

We are building Deep Lake, the Data Lake for Deep Learning https://github.com/activeloopai/deeplake

The landscape of computation resources across different special hardware and cloud providers is becoming increasingly fragmented.

We're building a platform that unifies and abstracts away infrastructure for easier and highly efficient machine learning and deep learning.

Activeloop

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AI Search Engineer