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BrainCo

Machine Learning Engineer, Applied AI

San Francisco Bay Area (Remote)fulltimemidAdded 2 weeks ago

About this role

Brain Co. seeks an AI/ML Engineer to design and deploy advanced language models solving critical real-world problems for governments, healthcare systems, and energy companies. You'll work with a team of top AI talent to transform cutting-edge AI research into production systems serving millions globally.

What you'll do

  • Design and deploy large language models to automate complex processes across healthcare, government, and energy sectors
  • Build scalable data pipelines and optimize models for production performance and accuracy
  • Monitor and maintain deployed models in production environments across multiple countries
  • Collaborate with government officials and institutional leaders to understand business requirements and deliver AI solutions
  • Conduct code reviews, share knowledge, and maintain high engineering standards
  • Stay current with latest ML/AI developments and participate in continuous learning

What they're looking for

  • Large language model architecture and operation
  • Generative AI applications (agents, reasoning models, RAG)
  • PyTorch, JAX, or TensorFlow implementation
  • Data structures and algorithms
  • Production ML deployment and model monitoring
  • Software engineering best practices
  • Cross-functional collaboration
  • Problem-solving and analytical thinking

Benefits

  • Competitive salary
  • Medical, dental, and vision coverage (100%)
  • Paid maternity and paternity leave
  • 401(k)
  • Daily lunches and commuter benefits
  • Unlimited PTO
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BrainCo

BrainCo builds and deploys cutting-edge AI and language model solutions for governments, healthcare systems, and critical infrastructure organizations. The company is hiring AI/ML engineers, backend platform engineers, AI platform engineers, and sales engineers to develop scalable infrastructure, production AI systems, and secure government contracts.

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Likely interview questions

  • Walk us through a GenAI application you've built end-to-end—what was the problem, which LLM architecture did you use, and how did you handle production deployment and monitoring?
  • Describe your experience with RAG systems or AI agents. What challenges did you encounter, and how did you optimize for accuracy and latency?
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