Machine Learning Engineer

DocuSign DocuSign · Enterprise · San Francisco, CA +2 · Engineering

Machine Learning Engineer on the AI Platform team responsible for designing and building foundational infrastructure for intelligent systems, including scalable platforms for autonomous agents, advanced retrieval systems, and automated model optimization. This role involves building high-performance distributed systems for model inference and data processing, designing frameworks for multi-agent systems, architecting RAG pipelines, and developing tools for prompt engineering, evaluation, and optimization.

What you'd actually do

  1. Build and maintain high-performance distributed systems to support large-scale model inference and data processing
  2. Design frameworks for multi-agent systems, focusing on state management, reliability, and long-running autonomous workflows
  3. Architect sophisticated Retrieval-Augmented Generation (RAG) pipelines and advanced context management strategies to improve model accuracy and relevance
  4. Develop platform-level tools for automated prompt engineering, evaluation, and optimization to accelerate the AI development lifecycle
  5. Implement robust ML pipelines, focusing on observability, versioning, and the seamless deployment of generative AI services

Skills

Required

  • 5+ years of experience in machine learning engineering, software engineering, or related operational roles
  • Experience in software engineering with a focus on distributed systems and scalable backend architecture
  • Deep understanding of the ML lifecycle, from data ingestion and training to production monitoring
  • Experience building with LLMs, including RAG architectures and sophisticated prompt engineering
  • Experience deploying and maintaining ML models in high-traffic, production environments
  • Expertise in Python
  • Experience with modern ML frameworks such as PyTorch

Nice to have

  • Experience with distributed task queues or stateful workflow engines for managing complex, multi-step AI processes
  • Experience with frameworks designed for horizontal scaling of compute-intensive ML workloads
  • Experience designing "agent-loop" architectures that involve tool-use, self-correction, and multi-step reasoning
  • Familiarity with vector storage systems and high-throughput data processing pipelines

What the JD emphasized

  • Experience building with LLMs, including RAG architectures and sophisticated prompt engineering
  • Experience deploying and maintaining ML models in high-traffic, production environments
  • Experience designing "agent-loop" architectures that involve tool-use, self-correction, and multi-step reasoning

Other signals

  • design and build the foundational infrastructure that powers Docusign’s next generation of intelligent systems
  • develop scalable platforms for autonomous agents, advanced retrieval systems, and automated model optimization
  • build and maintain high-performance distributed systems to support large-scale model inference and data processing
  • design frameworks for multi-agent systems, focusing on state management, reliability, and long-running autonomous workflows
  • architect sophisticated Retrieval-Augmented Generation (RAG) pipelines and advanced context management strategies
  • develop platform-level tools for automated prompt engineering, evaluation, and optimization
  • implement robust ML pipelines, focusing on observability, versioning, and the seamless deployment of generative AI services