Machine Learning Engineer, Applied AI - Deployed

Brain Co.
London
Workplace: HybridFull timeFunction: Data Science & Machine LearningSkills: ["Turning ambiguity into shipped systems","End-to-end ownership","Collaboration","Raising engineering standards","Comfort with uncertainty"]

Build and deploy frontier applied AI systems where models must become institution-grade decision engines. Own ML and agent-native workflows end-to-end—from inventing success criteria to production behavior—using composite systems like vision/segmentation models, VLM reasoning, rule engines, and RL fine-tuning. Design data, eval suites, and failure-mode taxonomies, and collaborate directly with permit reviewers, underwriters, and compliance teams to improve real-world outcomes.

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FursaFursa
Brain Co.
Brain Co.
3 weeks ago

Machine Learning Engineer, Applied AI - Deployed

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 4 hours agoStatus: Live

Job Summary

Build and deploy frontier applied AI systems where models must become institution-grade decision engines. Own ML and agent-native workflows end-to-end—from inventing success criteria to production behavior—using composite systems like vision/segmentation models, VLM reasoning, rule engines, and RL fine-tuning. Design data, eval suites, and failure-mode taxonomies, and collaborate directly with permit reviewers, underwriters, and compliance teams to improve real-world outcomes.
Location: London
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Turn ambiguous, underspecified problems into well-posed ML problems and production deployments.
  • •Own AI systems end-to-end with no handoff, including training and production behavior.
  • •Apply LLMs, RL fine-tuning, and agentic systems where outputs drive institutional decisions.
  • •Work directly with the institutions served (e.g., permit reviewers, underwriters, compliance officers) to ensure systems change how work is done.
  • •Engineer for production reality across accuracy, latency, cost, and reliability, and raise the bar through design reviews and shared playbooks.

Key Requirements

  • •Understand how machine learning works beyond tools, including loss functions, generalization under distribution shift, and why evaluation fails in production.
  • •Be skilled with modern AI/LLMs and agentic systems, with instincts for prompting, fine-tuning, tool use, and reasoning.
  • •Know how to compose frontier components (e.g., fine-tuned segmentation vs VLMs, plus rule engines) into systems that outperform single models.
  • •Treat frontier models as measured engineering components, not magic, and be able to push performance with the right evaluation feedback loops.
  • •Be energized by building things that have never existed and comfortable inventing the problem, data, and definition of success together.
Experience:Applied AILLMsAgentic systems
Skills:Turning ambiguity into shipped systemsEnd-to-end ownershipCollaborationRaising engineering standardsComfort with uncertainty
Tech Stack:Machine learningLLMsAgentic systemsPromptingFine-tuningTool useReasoningVision transformersSegmentation modelsVLM reasoningRule enginesRL fine-tuningEvaluation (evals)Eval suitesFailure-mode taxonomiesMultimodal documents

Company Brief

Brain Co.
Brain Co. builds agent-native operating systems for regulated institutions across government, health, insurance, finance, and enterprise. Its platform emphasizes secure deployment, workflow integration, and AI applications tailored to complex operational environments.
Industry: AI & Machine Learning
Growth: Growth Stage Startup
Headquarters: San Francisco, United States
Founded: 2026
WebsiteLinkedIn