Agentic AI/ML Engineer Intern, Solutions

FieldAI•Irvine, CA

1d•Onsite

About The Position

FieldAI’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field. As an Agentic AI/ML Engineer Intern, you will build agentic solutions that turn FieldAI’s data and tooling into actionable support and insights. Your work has a dual focus: creating internal agents that accelerate our engineering and operations workflows, and building external agentic experiences to help customers gain insights from their deployments. The critical foundation for both is the AI Ops platform you will help build—developing the core infrastructure for orchestration, tool integration, memory, and observability. This is a hands-on role where you will prototype, evaluate, and ship agent-native solutions that directly multiply the impact of our teams and technology. This role is crucial to how we scale. As our fleet, customer base, and team grow quickly, the agentic solutions and AI Ops infrastructure you build are what let us support more customers, surface more insights, and run our own operations without scaling headcount linearly. Your work directly multiplies what every engineer, field team, and customer can do — making it a high-leverage part of the company’s next phase of growth. This is a paid internship with a strong opportunity to convert to a full-time role based on performance and business needs.

Requirements

Currently pursuing a BS, MS, or Ph.D. in Computer Science, AI/ML, Robotics, or a related technical field, with deep project-based experience.
Strong evidence of building agentic projects (hackathons, research, internships, or personal projects).
Solid theoretical understanding and practical application of Agentic Engineering principles (Tool Use, Memory, RAG, Planning).
Proven ability to write reliable, testable, clean, and performant Python code, with familiarity with software engineering best practices, including version control, containerization (Docker), and test-driven development (pytest).
Hands-on engineering experience with modern, open-source agentic frameworks (e.g., LangChain, LangGraph, LlamaIndex) rather than relying strictly on service-managed agent APIs.
Experience implementing evaluation, tracing, and monitoring pipelines (e.g., MLflow, Langfuse, TruLens) to quantitatively measure agent quality, factual accuracy, latency, and reliability.
Practical expertise building and optimizing context-aware systems, with hands-on experience using Vector Databases (e.g., Pinecone, FAISS, OpenSearch) and designing Knowledge Graphs to reliably ground agents and mitigate hallucinations.
Familiarity with Cloud Platforms (e.g., AWS, GCP) for ML/AI deployment, and/or experience with on-robot compute environments.
Ability to take a loosely defined, complex problem and define and drive a working solution end-to-end.
Strong ability to drive solutions end-to-end , including cross-team coordination and seeking out customer input to shape what gets built.
Strong communication, initiative, and ability to learn quickly in a fast-moving team.

Nice To Haves

Deep experience designing and operating AI Ops infrastructure at production scale, including robotics-grade data logging and observability (e.g., Foxglove).
Experience with advanced agent patterns: Multi-Agent Systems , Human-in-the-Loop workflows , or Long-Horizon Planning .
Prior experience shipping internal tools or customer-facing assistants used by real users.
Personal projects and a portfolio of agentic builds are a big bonus — we love seeing what you’ve shipped on your own.

Responsibilities

Design and implement agentic workflows with tool use, memory, and orchestration to automate repetitive tasks and answer questions over internal and customer-facing data.
Contribute to AI Ops (agent infrastructure) — orchestration, evals, and observability — and apply it to enable agent-native DevOps that automates our engineering and internal operations workflows.
Build and optimize RAG pipelines with vector DBs and knowledge graphs to ground agents in the right context.
Set up evaluation pipelines to measure agent quality, reliability, and performance.