Senior ML Engineer

Build and deploy explainable machine learning, NLP, LLM, and agentic systems that power enterprise go-to-market intelligence products. The role requires 6+ years of production ML experience, strong Python and cloud skills, and end-to-end ownership from modeling through monitoring.

About the job

Responsibilities

Own machine learning problems end to end, from data exploration and modeling through deployment, monitoring, and production iteration.
Build NLP, LLM, and agentic systems at enterprise scale, including retrieval-based architectures and multi-agent workflows.
Develop explainable ranking, recommendation, prediction, and optimization models.
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 technical work to technical and non-technical audiences and engage with customers when needed.
Mentor engineers and raise the bar for engineering excellence.

Requirements

6+ 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 generative AI tooling such as LangGraph, LangChain, or Amazon Bedrock.
Strong Python skills and experience building distributed ML pipelines on cloud infrastructure such as AWS or Databricks.
Solid understanding of feature engineering, model evaluation, and MLOps practices.
Product mindset focused on building customer-facing AI products and measuring customer impact.
Excellent communication and ability to explain complex technical work clearly.
Comfort with ambiguity and ability to drive execution independently.

Nice to Have

Experience with RAG architectures, vector databases, and prompt engineering.
Hands-on experience with PyTorch or TensorFlow.
Background in B2B SaaS, enterprise AI products, or forward-deployed engineering, especially involving complex customer data and rapid delivery.

Compensation and Benefits

Health coverage.
Paid parental leave.
Generous paid time off and holidays.
Quarterly self-care days off.
Stock options.
Equipment and support for working from home or in an office.
Learning and development initiatives, including access to LinkedIn Learning.
Wellness education sessions, wellness days, and employee resource group events.

Skills

Machine Learning, Natural Language Processing, LLMs, Transformers, Embeddings, Retrieval-Augmented Generation, LangGraph, LangChain, Amazon Bedrock, Python, AWS, Databricks, MLOps, PyTorch, TensorFlow