Job Description

Brief Job Description:

Contributes hands-on across the applied-research cycle behind Constructor’s education products — surveying recent work, designing and running experiments, analysing results, and prototyping capabilities that apply AI and quantum through an education lens to improve how people teach, learn, and do research. Works closely with research scientists, engineering, and product teams, and helps turn early-stage ideas into working features that feed the AI software development project. Requires 3+ years of research experience — ideally in education technology or applied AI — with a track record of delivering solid, hands-on output in a fast-paced, multidisciplinary setting.

Mission:

Do the hands-on experimental work that turns promising ideas into working proof-of-concept code — quickly, and backed by evidence the product and engineering teams can build on.

Education:

• Master’s or PhD (or near-completion) in Computer Science, Physics, Mathematics, or a related field — or equivalent applied research experience. Shipped research prototypes, open-source contributions, or publications all count.

Duties and Responsibilities:

• Scan recent research and turn relevant techniques into prototypes the team can test fast.
• Design, implement, and run experiments; analyse results and decide what’s worth taking forward.
• Deliver each idea as working proof-of-concept code — clean enough to hand to engineering, not just a written summary.
• Help integrate validated research into product features and measure the impact.
• Work day to day with research scientists, ML engineers, and product teams to move ideas from notebook to product.

Qualifications and Experience:

• 3+ years of applied research or research-engineering experience — ideally in education technology or applied AI.
• Strong Python and PyTorch — able to produce runnable code others can extend, not just specs.
• Sound grasp of experiment design, statistics, and result analysis.
• Able to read recent papers and reproduce or adapt their methods into working code quickly.
• Reproducible, well-documented work using version control (GitLab) and experiment tracking (MLflow).

Nice to Have (Not Obligatory):

• Experience taking research prototypes into production or shipped product features.
• Publications, preprints, or open-source contributions in AI or related areas.
• Exposure to AI-for-education products, or to quantum computing frameworks.