Sr. Machine Learning Engineer

6sense•San Francisco, CA

37d•Remote

About The Position

6sense is hiring a Senior Machine Learning Engineer to join their AI team. This role focuses on building intelligence that explains why an account matters, why now, and who is deciding, by turning a trillion daily signals into cited, explainable answers. The models built will power customer-facing products like RevvyAI and integrate through APIs and the MCP server. This is a build-and-ship role with end-to-end ownership, requiring collaboration with Product and Go-to-Market teams. The role is part of a distributed team across the US and India, in a company where AI is central to the product.

Requirements

8+ years of industry experience building and deploying machine learning systems in production, with clear end-to-end ownership.
Strong foundation in machine learning and applied statistics, with hands-on depth in NLP, transformers, embeddings, and retrieval-based systems.
Practical experience with modern GenAI tooling such as LangGraph, LangChain, or Amazon Bedrock.
Strong Python skills and experience building distributed ML pipelines on cloud infrastructure (AWS, Databricks, or equivalent).
Solid grasp of feature engineering, model evaluation, and MLOps practices.
A product mindset — you want to build AI products customers use, and you measure yourself on customer impact.
Excellent communication: you can explain complex technical work clearly, tell the story of what you’ve built and why, and hold your own with product and business partners.
Comfort with ambiguity and the judgment to drive execution independently.

Nice To Haves

Experience with RAG architectures, vector databases, and prompt engineering.
Hands-on work with PyTorch or TensorFlow.
Background in B2B SaaS, enterprise AI products, or forward-deployed engineering — especially where you worked directly with complex customer data and delivered quickly.

Responsibilities

Own machine learning problems end to end — from data exploration and modeling through deployment, monitoring, and iteration in production.
Build NLP, LLM, and agentic systems at enterprise scale, including retrieval-based architectures and multi-agent workflows.
Develop ranking, recommendation, prediction, and optimization models that are explainable rather than black-box.
Partner with Product and Go-to-Market to turn ambiguous business problems into shipped capabilities.
Improve the performance, scalability, and reliability of production ML systems, and help shape AI platform architecture.
Explain your work clearly to technical and non-technical audiences, and engage with customers when needed.
Mentor engineers and raise the bar for engineering excellence.