Wizard
Machine Learning Engineer- Relevance & Learning Systems
Vaga remota de Machine Learning Engineer com fit claro de localização do candidato.
Publicada1 de jul. de 2026
Países elegíveis1 país aceito
Sinal de senioridadeSenior
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Estados Unidos
Resumo da vaga
Machine Learning Engineer- Relevance & Learning Systems
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
What You’ll Do
- Build and productionize feedback loops that improve agent performance over time
- Build the evaluation infrastructure – offline metrics, regression suites, and experiment analysis
- Own the signal pipelines end-to-end: instrument events, build clean labeled datasets, and translate user behaviors into reliable learning signals
- Design lightweight reinforcement learning / bandit-style approaches where appropriate
- Partner closely with product and engineering to define success metrics and optimize for them
- Design and analyze experiments that validate whether learning system changes actually improve real outcomes
- Improve ranking, recommendations and decision making within the agent
- Iterate quickly: Ship → measure → learn → improve
What Success Looks like
- You ship quickly and drive measurable improvements in core product metrics
- You turn noisy user behavior into reliable learning signals that improve the agent over time
- You own systems end to end and operate comfortably in production
Ideal Background
- 5-8 years hands on experience building and shipping ML systems
- Bachelor’s or Master's degree in computer science
- Experience shipping ML systems to production and have worked on recommendation systems, ranking, personalization or optimization problems
- Deep knowledge in Python and model ML tooling
- Pragmatic: you choose simple, effective solutions over theoretically perfect ones
Compensation & Benefits
- Equity in the form of stock options
- Medical, dental, and vision coverage
- 401(k) plan
- Flexible PTO and company holidays
- Fully remote work within the United States
- Periodic company offsites and team gatherings
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