AI Engineer, Intern

We're seeking an AI Engineer Intern to work alongside our AI team on large-scale AI and Agentic  systems from data pipeline to production deployment. This role is scoped for someone with foundational experience who wants to deepen it: you'll own discrete pieces of real systems under the mentorship of senior engineers, not shadow work or isolated coursework-style projects.

What You'll Do

Model Development

Partner with product managers, designers, and engineers to translate product requirements into scoped AI problem statements.

Prepare data pipeline and AI store design under senior engineer guidance.

Build and validate AI models using PyTorch or JAX(Optax/Orbax / TensorStore/Grain) and similar tools.

Run large-scale experiments, evaluate models against defined metrics, and support ablation studies and error analysis.

Productionization

Help build inference APIs and batch scoring workflows; integrate AI outputs with backend services.

Support AIOps practices already in place on the team: model versioning, CI/CD pipelines, monitoring dashboards, and logging.

Assist with model optimization work (quantization, batching, distillation) under supervision -this is a learning objective, not an expected independent deliverable.

Operational Rigor

Contribute to root-cause analysis on production issues alongside senior engineers.

Document experiments, design decisions, and runbooks so your work is legible to the next person who touches it.

Flag fairness, interpretability, or privacy concerns you observe in data or model behavior….you're not expected to resolve these alone, but surfacing them is part of the job.

About You

Currently pursuing a BS/MS/PhD in Computer Science, Data Science, or a related field.

Some hands-on experience with ML systems - course projects, research, hackathons, or a prior internship all count. We are not requiring 1+ years of production experience for this role; that bar belongs on a full-time posting, not an internship.

Solid Python/Rust fundamentals: data structures, functions, basic testing, comfort reading and writing code reviews.

Exposure to at least one AI/ML frameworks (PyTorch or JAX) and basic data tooling (Pandas, SQL).

Nice to have, not required: familiarity with a cloud platform (AWS/GCP/Azure), Docker, or a model-serving tool (vLLM, Triton, Ray Serve).

Clear written and verbal communication. You'll be documenting your own work and asking questions across product, design, and engineering.