Software engineering consumer

Job Title

Software Engineering Intern, Consumer AI

Job Description

This People Inc. internship is full time position starting in June and finishing May 2027.

About The Team

We are a small, high-impact AI Product R&D team building experimental AI systems that frequently become customer-facing products. Our work moves quickly from idea to implementation, and the systems we design are built with real users and real scale in mind.

Operating with high agility and minimal bureaucracy, we take ownership from concept through execution. As a focused group with strong visibility across the organization, our prototypes can evolve into core product features used at scale.

We work in fast-moving problem spaces where stakeholder priorities and experiment results shape what we build next. That means high urgency, collaborative reprioritization, and meaningful ownership for every engineer on the team.

If you want to build AI systems that matter, this is the environment to do it.

About The Role

As an intern on this team, you will operate as a junior AI engineer with a strong data and systems mindset. You will work with a broad range of internal data assets and rich metadata to design and build tools and pipelines that extract, transform, and derive meaningful signals that power AI experimentation and product development.

You will contribute to building and evolving our infrastructure. As you gain context and ownership, you’ll have opportunities to identify improvements, propose new ideas, and implement lightweight user experiences to demonstrate concepts end-to-end.

This role is hands-on and deeply technical. You will work across data pipelines, orchestration, evaluation frameworks, and AI tooling; developing a systems-level understanding of how modern AI features move from prototype to production. You’ll gain exposure to real-world data engineering practices and the full lifecycle of AI system development within a fast-moving, production-oriented environment.

Core Responsibilities

Data Engineering

• Maintain and extend Python-based ETL pipelines
• Process and transform large datasets stored in MongoDB, S3, and Postgres
• Improve reliability, clarity, and maintainability of data workflows
• Containerize services using Docker and work within CI/CD pipelines (Jenkins)
• Write clear documentation and README files so other teams can understand and adapt your code

AI Experimentation & Evaluation

• Write scripts to evaluate and score model outputs
• Compare model performance across prompts, embeddings, and configurations
• Run retrieval experiments using Milvus
• Iterate on prompts and log experiment results clearly
• Contribute to benchmarking efforts; exceptional candidates may help formalize parts of an evaluation framework in partnership with a Principal Engineer

Front-End & Tooling Contributions

• Contribute to lightweight UI components when needed (HTML/CSS/JS)
• Work with GraphiQL and related developer tooling
• Script in other languages as needed (Apps Script, JavaScript, etc.)

Communication & Documentation

• Maintain clear research logs
• Write Proof-of-Concept (PoC) summaries
• Document engineering decisions
• Communicate progress and blockers asynchronously via Slack
• Present findings to stakeholders

Required

What We’re Looking For

:

• Currently pursuing (or recently completed) a Master’s degree in Computer Science, Data Science, or a related field
• Strong proficiency in Python
• Demonstrated experience building and deploying:
  • At least one AI-related project (LLMs, RAG, model evaluation, etc.)
  • At least one data-intensive system (ETL pipelines, large-scale data processing, etc.)
• Experience working with Git in a collaborative setting
• Strong written communication skills (technical documentation, experiment summaries, clear Slack communication)
• Ability to independently scope and execute small projects after onboarding
• Comfort working in a fast-moving, evolving AI landscape

Strongly Preferred

• Experience with APIs
• Experience with MongoDB
• Exposure to Airflow or workflow orchestration systems
• Experience evaluating model outputs or benchmarking AI systems
• Experience with Docker or CI/CD pipelines
• Prior internship or real-world engineering experience

How to Stand Out

The Strongest Candidates Will Demonstrate

• A deployed AI system (not just a notebook) — something others can access, use, or review.
• A clear example of a data pipeline you built or significantly improved, including how you handled scale, reliability, or edge cases.
• Being able to explain:
  • A model comparison you ran
  • A retrieval experiment you conducted
  • Or a meaningful technical tradeoff you navigated.
• Evidence of strong written communication (a well-written README, blog post, research summary, or technical documentation).
• Demonstrate clear reasoning behind architectural decisions; not just a list of technologies used.

It is the policy of People Inc. to provide employment opportunities regardless of age, physical or mental disability, race, gender, sex, sexual orientation, or any other characteristic protected by applicable laws. In addition, the Company will provide reasonable accommodations for qualified individuals with disabilities. Accommodation requests can be made by emailing [email protected].

#NMG#