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Applied ML Engineer

Adaptive training models

Founding · equity-first Remote · any timezone

Sharpen how HYREN adapts — without ever letting a model make the training decision it cannot explain.

What you would own

  • Turn logged sessions, check-ins and outcomes into signals the deterministic engine can use
  • Improve readiness and progression rules with evidence rather than intuition
  • Build the offline evaluation that proves a change helps before an athlete feels it
  • Keep the boundary honest: models inform and narrate, rules decide

What you bring

  • Applied ML on small, messy, longitudinal human data — not just benchmark datasets
  • The judgement to know when a heuristic beats a model
  • Ability to explain a result to a coach who does not care about your loss curve

Nice to have

  • Sports science or physiology background
  • Bayesian or hierarchical modelling on sparse per-user data
  • LLM evaluation experience

Founding · equity-first. Pre-revenue. Equity or profit-share, agreed in writing before you start.

What happens next

  1. 01

    Every application is read by a person here — no tracker, no keyword filter, no auto-reject.

  2. 02

    First call is 30 minutes on where you think a model belongs in a safety-bounded system, and where it does not.

  3. 03

    The technical step is a real evaluation problem on training data, timeboxed.

Worth preparing. Come with a view on how you would prove an adaptation actually helped an athlete. That question is the whole job.

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