Principal Machine Learning Engineer

Toast Toast · Enterprise · United States · Remote · R & D : BTT : Engineering

Principal ML Engineer to build 0-to-1 internal tools for sales teams, leveraging LLMs and agentic workflows to automate tasks like account research and lead prioritization. The role involves hands-on backend engineering and ML implementation, integrating with systems like Salesforce and custom quoting engines, and delivering recommendations to sales users. Focus is on building new intelligence models and agentic systems.

What you'd actually do

  1. Hands-on Execution: Spend the majority of your time "hands-on-keyboard," architecting and coding high-performance backend services and ML pipelines.
  2. 0-to-1 Development: Build and prototype new internal products from scratch that leverage LLMs and Agentic AI to automate account research, lead prioritization, and complex quoting logic.
  3. Core Intelligence Models: Design and implement ML models that provide real-time recommendations to sales users, reducing manual entry and increasing deal velocity.
  4. Infrastructure Integration: Develop the backend connective tissue between our custom quoting engine, Salesforce, and internal data lakes to ensure a seamless, low-latency end-to-end experience.
  5. Technical Problem Solving: Act as a domain expert to solve complex synchronization and architectural challenges across the GTM stack, ensuring systems are scalable and resilient.

Skills

Required

  • backend languages (Java, Go, Python)
  • building complex, distributed systems
  • deploying Machine Learning models
  • building with LLMs (e.g., LangChain, RAG architectures, or Model Fine-tuning) in a production environment
  • building or deeply integrating with complex enterprise software, specifically custom quoting engines or CRM internals (Salesforce Apex/LWC)
  • design event-driven architectures
  • manage data flow across disparate systems with high integrity

Nice to have

  • building internal "Sales Tech" or "Fintech" tools that directly impact revenue
  • AWS data services (SageMaker, Lambda, SQS)
  • removing "human-in-the-loop" friction through intelligent automation

What the JD emphasized

  • primary builder of 0-to-1 tools
  • architecting and coding high-performance backend services and ML pipelines
  • Build and prototype new internal products from scratch
  • leverage LLMs and Agentic AI
  • Design and implement ML models
  • building with LLMs (e.g., LangChain, RAG architectures, or Model Fine-tuning) in a production environment
  • Experience building or deeply integrating with complex enterprise software
  • systems are scalable and resilient
  • build flexible, decoupled backend systems that can evolve with our AI strategy

Other signals

  • building internal products from scratch
  • leverage LLMs and Agentic AI
  • automate account research, lead prioritization, and complex quoting logic
  • design and implement ML models that provide real-time recommendations
  • practical, hands-on experience deploying Machine Learning models and building with LLMs