As an AI Algorithm Engineer at our company, youll design and implement advanced algorithmic workflows that power our data digestion pipelines and conversational AI agent experience. You will:
Architect and build workflows that turn raw, unstructured data (e.g., PDF documents and ultra high-resolution architectural drawings) into meaningful, structured context for downstream analysis.
Combine techniques from classical NLP, computer vision, unsupervised learning, and graph theory to build robust end-to-end pipelines - not just standard LLM API calls, but intelligent decomposition, structuring, and context engineering.
Develop AI agent workflows that let customers explore, query, and reason about their data naturally and reliably.
Build strong evaluation infrastructure to benchmark and continuously improve both classical algorithmic components and LLM-based workflows.
Work primarily in Python, and collaborate across systems and services in TypeScript when needed.
Move fast from prototype to production, while maintaining correctness, scalability, and measurable quality.
Requirements: Core Responsibilities
Design and implement end-to-end data digestion pipelines for complex unstructured and semi-structured inputs.
Integrate classical algorithms with LLM-centric workflows to produce high-quality contextual inputs for reasoning tasks.
Build systems for segmentation, structure extraction, semantic decomposition, embedding-based representations, and graph construction.
Develop agentic workflows that combine tools, retrieval, and reasoning for interactive customer experiences.
Design evaluation suites, datasets, metrics, and regression tests to measure quality, robustness, and performance over time.
Continuously refine workflows based on customer usage, failure analysis, and measurable improvements.
What We're Looking For
Required Qualifications
MSc (or equivalent experience) in Computer Science, Mathematics, Electrical Engineering, or a related quantitative field.
5+ years of experience in data science / algorithm development / applied research engineering roles.
Strong experience building complex algorithmic workflows beyond model calls - including multi-stage pipelines, feature extraction, structure inference, and optimization.
Deep understanding of LLM digestion concepts: RAG, context engineering, chunking strategies, retrieval and ranking, tool usage patterns, and reliability techniques.
Experience designing and maintaining evaluation frameworks for both classical algorithms and LLM workflows (offline + online, regression, benchmarking).
Strong programming skills in Python; familiarity with TypeScript is a plus.
Ability to thrive in a high-ownership startup environment: ambiguity, speed, responsibility, and continuous learning.
Nice To Have
Familiarity with vector databases and scalable retrieval architectures.
Familiarity with agent orchestration frameworks (e.g., LangChain) and tool-driven reasoning systems.
Experience deploying AI workflows into production with monitoring, guardrails, and iteration loops.
This position is open to all candidates.