7 часов назад
Machine Learning Engineer (Applied AI)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Machine Learning Engineer (Applied AI) (LLMs, Computer Vision, Agentic Systems): Building production-grade AI decision systems for regulated institutions, including document understanding, vision pipelines, and agents that advance real workflows with an accent on end-to-end ownership, rigorous evaluation, and institutional-grade accuracy. Focus on composing models, VLM reasoning, segmentation, rule engines, and reinforcement learning into reliable systems that operate under real-world accuracy, latency, cost, and reliability constraints.
Location: London, hybrid
Company
builds AI-native operating systems for large, regulated institutions across government, insurance, healthcare, and financial services.
What you will do
- Build and own applied ML systems end-to-end, from ambiguous customer problems through evaluation and production deployment.
- Develop custom vision and document-understanding pipelines for blueprints, site plans, policies, contracts, and clinical records.
- Design and improve agentic systems using LLMs, prompting, fine-tuning, tool use, reasoning, and reinforcement learning.
- Compose vision transformers, segmentation models, VLM reasoning, and rule engines into accurate production systems.
- Build evaluation suites, failure-mode taxonomies, data pipelines, and training loops based on verified outcomes from real deployments.
- Work directly with permit reviewers, underwriters, and compliance officers to ensure systems improve institutional workflows.
Requirements
- Strong understanding of machine learning fundamentals, including loss functions, generalization, distribution shift, and evaluation.
- Experience applying modern AI methods to production systems rather than prototypes only.
- Ability to work with LLMs, agentic systems, fine-tuning, tool use, and reasoning.
- Ability to turn undefined problems, limited labeled data, and unclear success criteria into well-posed ML problems.
- Ability to balance accuracy, latency, cost, and reliability in production environments.
- Ability to work in a hybrid setup in London.
Culture & Benefits
- Work on frontier applied AI with production stakes and measurable customer impact.
- Every project ships to production and supports real institutional workflows.
- Collaborate with experienced engineers and researchers from Palantir, Google, Meta, and Nvidia.
- Contribute through design reviews, an internal paper club, and a shared playbook for trustworthy AI systems.
- Help create a new category of agent-native operating systems without an established playbook.
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