We are seeking highly technical and product-minded Member of Technical Staff (AI Algorithms & Systems) to build core intelligence for automating the software development lifecycle. This opportunity requires a strong intuition for LLMs, applied research ability, and a bias toward shipping.

Key Responsibilities

LLM Systems & Applications

• Design and develop LLM-powered systems for code understanding, generation, and reasoning.
• Build agents and workflows that automate complex parts of the software development lifecycle.
• Improve reliability, accuracy, and controllability of AI behaviors in production.

Algorithm & Model Development

• Research, implement, and refine algorithms for reasoning, retrieval, planning, and tool use.
• Develop advanced prompting, RAG, and agentic architectures.
• Contribute to fine-tuning, distillation, and specialized model work when needed.

Evaluation & Optimization

• Build evaluation frameworks to measure model quality, performance, and reliability.
• Run experiments to identify areas for model and system improvement.
• Optimize AI pipelines for latency, cost, and scalability.

Integration

• Work cross-functionally with infrastructure, product, and engineering teams to ship AI capabilities end-to-end.
• Integrate AI features into developer tools, IDEs, CI/CD systems, and internal platforms.

Research to Production

• Translate state-of-the-art research into practical, production-ready systems.
• Stay ahead of advancements in LLMs, agentic AI, and AI-assisted software engineering.

Qualifications

• Strong background in machine learning, NLP, or applied AI research.
• Experience building with LLMs and modern AI tooling (LangChain, LlamaIndex, OpenAI, Anthropic, etc.).
• Proficiency in Python and familiarity with ML frameworks (PyTorch, Hugging Face).
• Experience shipping end-to-end AI systems in real products.
• Strong problem-solving and experimental mindset.
• Ability to operate independently in ambiguous, fast-moving environments.

What We Value

• Preference for simple, scalable solutions.
• Fast iteration loops and a bias toward action.
• Ability to bridge research and production.
• Deep curiosity about AI and its applications in software engineering.