Junior AI Researcher
Posted 2/22/20261 applicant
About TechCorp Inc.
Focus on relentless innovation and the belief that effort, discipline, and confidence can turn potential into achievement. Celebrate movement, resilience, and the idea that greatness is available to anyone willing to push past limits.
Job Description
TechCorp Inc. is hiring a Junior AI Researcher to join our Applied AI team focused on building next-generation, customer-facing intelligence: LLM-based assistants, agent workflows, and multimodal features. You will work alongside senior researchers, engineers, and product managers to turn research ideas into working prototypes and production-ready components. This is a hands-on role where the emphasis is on rapid, rigorous experimentation, reproducible results, and delivering measurable product impact.
In this role you will own small end-to-end projects — from hypothesis and experiment design, through data curation and model iteration, to evaluation and handoff to engineering for deployment. Day-to-day responsibilities include running controlled experiments, designing evaluation frameworks and metrics, implementing reproducible training and inference pipelines, producing clear technical write-ups, and collaborating with product and infrastructure teams to ensure safe, efficient integration. You will also contribute to team knowledge by documenting lessons learned, designing benchmark suites, and participating in code reviews. The role is ideal for someone with a strong analytical foundation who enjoys both research thinking and building production-ready artifacts.
Key Skills
PythonPyTorch
Requirements
Candidates should have a formal background in computer science, machine learning, statistics, or a related quantitative field (B.S., M.S., or equivalent practical experience). We expect demonstrated experience with ML projects — through coursework, independent projects, internships, or open-source contributions — that shows the ability to take a model or prototype from concept to functioning artifact. A successful applicant will be able to explain experimental design choices, produce reproducible results, and communicate findings clearly to non-research stakeholders.
This role suits early-career professionals who are eager to learn, iterate quickly, and accept mentorship. You should be comfortable reading primary research papers and translating them into implementable experiments, keeping rigorous evaluation and safety considerations in mind. Prior exposure to production ML environments or collaboration with product/infra teams is a plus, as is any experience contributing to shared codebases or internal tools.