Sr. Machine Learning Engineer

Posted: 2026-08-20

- 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.

Required

- 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.