Machine Learning Engineer, Applied AI

Brain Co.
San Francisco, New York
Workplace: HybridFull timeFunction: Data Science & Machine LearningSkills: ["Turning ambiguity into shipped systems","End-to-end ownership","Design/review discipline","Cross-functional collaboration","Problem-solving"]

Build production-ready ML decision systems for regulated institutions, starting where demos end and owning models end-to-end. Work on composite AI with vision and segmentation pipelines, VLM reasoning, rule engines, and agentic workflows that learn from verified outcomes. Create robust evaluation suites and failure-mode taxonomies so regulators can trust decisions. Partner with permit, underwriting, and compliance teams to translate messy institutional context into shipped systems with measurable impact.

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FursaFursa
Brain Co.
Brain Co.
1 year ago

Machine Learning Engineer, Applied AI

✓ 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 production-ready ML decision systems for regulated institutions, starting where demos end and owning models end-to-end. Work on composite AI with vision and segmentation pipelines, VLM reasoning, rule engines, and agentic workflows that learn from verified outcomes. Create robust evaluation suites and failure-mode taxonomies so regulators can trust decisions. Partner with permit, underwriting, and compliance teams to translate messy institutional context into shipped systems with measurable impact.
Location: San Francisco, New York
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •Turn ambiguous customer problems into well-posed ML problems and production deployments, even without labeled data or a clear success definition.
  • •Own AI systems end-to-end, from training to production behavior with no handoff.
  • •Apply LLMs, RL fine-tuning, and agentic systems where outputs drive real institutional decisions.
  • •Build document understanding models for dense multimodal inputs like blueprints, policy stacks, contracts, and clinical records.
  • •Create evaluation suites and failure-mode taxonomies rigorous enough for institutional sign-off.

Key Requirements

  • •Understand how machine learning works beyond tooling, including loss functions, generalization under distribution shift, and evaluation pitfalls.
  • •Strong hands-on experience applying LLMs and agentic systems, including prompting, fine-tuning, tool use, and reasoning.
  • •Know when to use fine-tuned segmentation models vs VLMs, and how to compose multiple components (e.g., rule engines) into a higher-accuracy system.
  • •Ability to turn underspecified problems into well-posed ML tasks, including inventing the definition of success and producing production deployments.
  • •Comfort working with hard frontier datasets and documents with institutional-grade accuracy and real feedback loops.
Experience:Applied AILLM-based systemsAgentic systemsRegulated institutionsComputer vision
Skills:Turning ambiguity into shipped systemsEnd-to-end ownershipDesign/review disciplineCross-functional collaborationProblem-solving
Tech Stack:LLMsAgentic systemsPromptingFine-tuningTool useReasoningVision transformersSegmentation modelsVLMRule enginesRL fine-tuningEvaluation 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