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Senior Machine Learning Engineer, London Hybrid

Build and deploy agentic document-understanding systems for Scarlet’s medical-device certification workflows. Develop evaluated machine learning systems that accelerate certification while protecting safety and evidence integrity.

GBBuyuk Britaniya
90 000 £ – 150 000 £ / yil
GibridTo‘liq stavkaO‘rta darajaSeniorTalab Qilinadigan Til: inglizchaMashinaviy o‘qitish muhandisi

Tavsif

  • Design agentic document-understanding systems that search, parse, and visually inspect technical files
  • Create agent harnesses that retrieve information from large collections of varied documents
  • Maintain source attribution and reduce hallucinations while balancing accuracy, latency, and cost
  • Deploy agent-based systems into production
  • Work with assessors to define success and create datasets and benchmarks for a complex domain
  • Evaluate retrieval quality, citation accuracy, expert agreement, assessor effort, assessment quality, customer experience, and rework
  • Use evaluation findings to guide improvement priorities
  • Develop agents that uphold the impartiality and objectivity expected of a certification body
  • Help users interpret evidence, identify uncertainty, and retain accountability for consequential decisions
  • Lead machine learning systems from concept and prototype through deployment, evaluation, and continuous improvement
  • Partner with clinicians, assessors, and engineers to accelerate medical-device certification without reducing safety

Talablar

  • At least three years of experience delivering software into production
  • Working knowledge of deep learning, agent tools, context management, and information retrieval
  • Experience creating datasets for machine learning evaluation
  • Strong first-principles understanding of statistics and machine learning system evaluation
  • Experience designing, deploying, and operating production systems
  • Practical judgment about production security, including data access, permissions, untrusted inputs, and consequential actions
  • Ability to select workload-appropriate infrastructure and explain trade-offs involving complexity, reliability, security, and cost
  • Ability to take ownership of ambiguous problems, from defining success with domain experts through experimentation and production improvement
  • Preferred: experience deploying agent systems involving tool use, sensitive data, or consequential actions, with evaluation and safeguards
  • Preferred: experience building retrieval or document-understanding systems whose outputs must be validated against complex source evidence
  • Ability to work in the London office for the working session

Imtiyozlar

  • Equity compensation
  • Working session and interviews included in the hiring process

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