# Senior Machine Learning Engineer

**Company:** [6sense](https://hotfix.jobs/companies/6sense)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $200k – $261k/yr
**Experience:** 8+ years
**Skills:** Python, Machine Learning, Applied Statistics, Natural Language Processing, Transformers, Embeddings, Retrieval-Augmented Generation, LangGraph, LangChain, Amazon Bedrock, AWS, Databricks, MLOps, PyTorch, TensorFlow
**Posted:** 2026-08-20

> Owns end-to-end production machine learning systems, including NLP, LLM, agentic, ranking, and recommendation capabilities. Requires 8+ years of industry experience, strong Python and cloud ML expertise, and the ability to deliver explainable AI products with cross-functional and customer impact.

## Job Description

## 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
- 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 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 AI products customers use and measuring customer impact.
- Excellent communication and ability to explain complex technical work clearly to product and business partners.
- Comfort with ambiguity and judgment 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, particularly involving complex customer data and rapid delivery.

## Compensation and Benefits
- Base salary range: **$200,349.50–$260,912.60**.
- Additional compensation may include a bonus or commission plan and stock options.
- Benefits include health insurance, life and disability insurance, a 401(k) employer matching program, paid holidays, self-care days, paid time off, paid parental leave, stock options, equipment support, and learning and development programs.

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