About this role.
This is an early-career AI engineering accelerator role focused on improving internal engineering productivity and developer experience. The participant will help build coding assistants, automation scripts, knowledge Q&A tools, and lightweight LLM/RAG applications. Core work includes experimentation, testing, iteration, documentation, and research into emerging AI use cases. Candidates should be current computer science or related students with Python skills, sound programming fundamentals, Git familiarity, and strong interest in LLMs. The role is well suited to someone building practical AI-development experience through projects and collaborative engineering workflows.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
3/5Pace & Pressure
4/5Autonomy Level
3/5Communication Load
3/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
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Sample interview questions
I would describe the problem, the intended users, the technical approach, and how I would measure success. For example, I might build a Python knowledge assistant that indexes internal documentation, retrieves relevant passages, and uses an LLM to generate cited answers; I would evaluate it using answer accuracy, citation relevance, and user feedback.
I would first define the desired output and collect examples of failures. Then I would make instructions explicit, add constraints and output formatting, provide a few representative examples where appropriate, and test changes against a small evaluation set rather than relying on a single result.
Key considerations include document quality, chunking strategy, embedding and retrieval quality, metadata filtering, prompt grounding, and evaluation. I would also ensure responses cite retrieved sources where possible and add safeguards so the model says when the available context is insufficient.
I would reproduce the issue with logs and compare environment variables, dependency versions, permissions, file paths, and input data. I would isolate the smallest failing case, implement a targeted fix, and document the root cause and setup requirements so the issue is less likely to recur.
I would establish a baseline and define measurable outcomes, such as time to resolve common questions, task completion rate, adoption, response usefulness ratings, and reduced repetitive support work. I would combine those metrics with qualitative feedback from engineers, iterate on the highest-impact issues, and monitor for incorrect or unsafe outputs.
What You Will Do
– Assist in building AI tools such as coding assistants, automation scripts, and knowledge Q&A systems
– Support the development of LLM-based applications (e.g., prompt optimization, lightweight RAG pipelines)
– Contribute to initiatives that improve engineering efficiency and developer experience
– Help test, iterate, and document internal AI tools and platforms
– Research and experiment with emerging AI technologies and use cases
What We’re Looking For
– Currently pursuing a Bachelor’s or Master’s degree in Computer Science or a related field
– Proficiency in at least one programming language (Python preferred)
– Strong fundamentals in programming and problem-solving
– Interest in AI, especially large language models (LLMs)
– Experience using tools like ChatGPT, Copilot, or similar
– Familiarity with basic software development workflows (e.g., Git, debugging)
– Good communication skills and willingness to learn
Bonus Points
– Experience with AI-related projects (e.g., chatbots, NLP, automation tools)
– Exposure to Prompt Engineering or AI-assisted development workflows
– Coursework or basic knowledge in machine learning or NLP
– Personal projects, technical blogs, or GitHub portfolio
Compensation
Additional Information
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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