Senior Software Engineer II, AI Labs & Foundations

Overview

Join Instacart's mission to transform grocery shopping through frontier AI. As a Senior Software Engineer on AI Labs & Foundations, you will design, build, and operate the high-scale production systems that power our most ambitious AI experiences—from Cart Assistant, our conversational shopping agent, to voice AI interactions and beyond. This is a high-impact opportunity to work at the intersection of robust software engineering and cutting-edge production AI/ML, directly shaping products used by millions of customers every day.

About the Role

We are hiring a Senior Software Engineer who will participate in the design and delivery of production AI systems, identify high-leverage technical opportunities, and contribute hands-on to AI-native products across Instacart's platform. We value bottom-up ideas, high engineering quality, and close partnership with Product, Data Science, ML, and Infrastructure teams. If you enjoy inventing, navigating ambiguity, prototyping fast, and turning wild ideas into real, scalable products, this is the team for you.

About the Team

AI Labs & Foundations sits at the intersection of frontier AI research and production engineering. Our portfolio spans the full stack of AI innovation at Instacart, including building and launching Cart Assistant, pioneering voice AI interactions, and constructing the foundational systems that power these cutting-edge experiences. We are a fast-moving, collaborative team that thrives on 0-to-1 thinking, shares learnings openly, and ships with urgency by prototyping fast and testing rigorously.

About the Job

• Design, build, and operate production AI-powered systems and agentic experiences (including Cart Assistant and voice AI) that directly impact how millions of customers shop.


• Build foundational systems for cutting-edge AI experiences, ranging from embedding infrastructure and voice AI pipelines, to client facing components and integrations, by prototyping bold ideas and productizing what works.


• Integrate foundation models via APIs and open-source frameworks; apply techniques like retrieval-augmented generation and vector search where appropriate.


• Own projects end-to-end: requirements, technical design, implementation, testing, deployment, observability, and iterative improvement focused on reliability, latency, and cost efficiency.


• Collaborate with cross-functional partners in product, design, data science, and infrastructure to ship AI features end-to-end.


• Drive engineering excellence, including thoughtful system design, rigorous code review, and technical leadership that includes defining and promoting best practices for AI/ML production engineering across the team.

About You

Minimum Qualifications:

• Proven senior software engineer who has built, shipped, and operated production systems at scale. You make architectural calls, own what you build, and deliver through ambiguity.


• Hands-on experience with AI or ML in production. You've shipped LLM-powered features or integrated foundation model APIs into a live product, demonstrating the necessary expertise at the intersection of robust software engineering and deep production ML.


• Experience owning services end-to-end, including CI/CD, automated testing, observability (logging, metrics, tracing), and on-call participation.


• Strong communicator who partners well across disciplines - you want to get to the right answer, not just defend the first one.


• Excitement and ability to leverage cutting-edge development tools, including AI assistance (e.g., Copilot, Cursor, Claude), to maximize velocity.

Preferred Qualifications:

• 5 to 8+ years of industry experience.


• A track record of 0-to-1 work taking unconventional ideas from prototype through rapid iteration to production.


• Experience building conversational agents, multi-turn dialogue systems, or agentic LLM applications.


• Experience with STT/TTS or natural language interfaces, LLM fine tuning, ML transfer learning, model training and the vocabulary of measurement of model performance.


• Experience with embedding systems, vector search, or retrieval-augmented generation (RAG) with vector databases (e.g., Pinecone, Weaviate, FAISS, or Elasticsearch).


• Experience with cloud platforms (AWS or GCP), containers (Docker), and orchestration (Kubernetes).


• Experience with event-driven or distributed systems (e.g., Kafka).


• Demonstrated mentorship and technical leadership within cross-functional teams.