About the Client

Our client is an AI-first career and hiring platform building the behavioral intelligence infrastructure for the future of hiring. Their mission is to create an intelligent system that understands candidates beyond resumes and helps organizations make better hiring decisions. Through AI-powered career coaches, they help candidates prepare for every stage of their interview journey. On the other side, the AI-native recruitment platform enables companies to discover, assess, and hire talent more intelligently and efficiently.

They are a small, fast-moving team that believes speed, ownership, and AI-native execution create extraordinary products. Every team member works alongside up-to-date AI tools that dramatically accelerate development, allowing them to build in days what traditionally took weeks. If you enjoy solving hard problems, taking end-to-end ownership, and building technology that can redefine how millions of people prepare for careers and how companies hire, this is the right place for you. You'll work closely with the founder and core leadership, with the opportunity to shape both the product and the company from an early stage.

Required Skills & Qualifications

Must-Have:
4-6 years hands-on experience building and shipping ML/AI products to production - and keeping them running. Not just research or notebooks.




LLM-as-component discipline: you have built structured-extraction or LLM-pipeline systems with versioned prompts and measured accuracy - you treat LLM output as a data source to be evaluated, not an oracle.
Classical ML fundamentals: linear/gradient-boosted models, regularization, honest train/test hygiene; you know what leaks and what overfits when you have only a few hundred labels.
Evaluation literacy: you can design an experiment, argue precision@K vs AUC vs calibration, and you are willing to say "this doesn't work" when the numbers say so.
Strong Python and the modern ML stack (PyTorch / scikit-learn / Hugging Face or equivalent), plus comfort with data plumbing (SQL, batch pipelines, Docker).
A builder's mindset with proven independent ownership - you've shipped real products end to end. Bonus Points
Learning-to-rank or recommender-system experience.
Prior hiring-tech or assessment-tech exposure, or awareness of its regulatory landscape (NYC LL144, EU AI Act).
Fairness/bias measurement experience on ranked or scored outcomes.
Speech/ASR or multimodal (audio/video) feature-extraction experience - this is our roadmap, not the day-one job.
Vector search / embedding-retrieval experience.
Early-stage startup or founder-led environment experience.

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