> Markdown version of [/jobs/ext/1465727-quantitative-software-engineer-techniques-engineering](https://www.wearedevelopers.com/jobs/ext/1465727-quantitative-software-engineer-techniques-engineering). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Quantitative Software Engineer: Techniques Engineering

- **Company:** Propertyvalue Two Sigma Investments, Llc.
- **Location:** United States (Remote available)
- **Salary:** $165,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Continuous Integration, Distributed Systems, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, Pytorch, Large Language Models, Information Technology, Deployment Automation, HuggingFace, Production Code, Machine Learning Operations, Software Version Control
- **Published:** July 28, 2026
- **Apply:** https://www.dice.com/job-detail/2f712996-542d-42cb-b766-8ed9c7d950b7

## About the Role

* BS in Computer Science, Mathematics, Physics, or related technical subject area
 * Minimum 1 year of experience required; 7-15 years of experience preferred
 * Strong software engineering skills using Python, version control, testing frameworks, and CI/CD practices
 * Ability to work across functions and influence technical strategy while balancing research innovation with operational excellence
 * A phenomenal teammate that can work efficiently with modeling and engineering partners
 * Experience with leading ML frameworks (e.g., PyTorch, TensorFlow, HuggingFace) and familiarity with model serving/deployment tools, distributed systems and data infrastructures
 * Experience with the following are preferred but not required:

 * Working with LLMs, multimodal models, or time-series forecasting
 * ML Ops platforms
 * Background in research or a proven understanding of ML theory

## Description

We're seeking a Quantitative Software Engineer to help scale the capabilities behind our AI/ML modeling techniques. You'll work at the intersection of research enablement, advanced hardware efficiency, AI tooling, and software engineering discipline, partnering closely with researchers to turn techniques with real production impact into fast, well-tested, and maintainable systems that the rest of the firm can build on.

You will take on the following responsibilities:

 * Collaborate with ML researchers to explore model architectures, training methodologies, and experimental objectives
 * Translate prototypes into reusable, maintainable, and production-ready code, ensuring research insights scale seamlessly into operation
 * Design, implement, and optimize fully automated training pipelines and evaluation workflows
 * Optimize models for production-improving efficiency through techniques like quantization, distillation, and intelligent batching
 * Continuously assess and fine-tune both research and production workflows, integrating user feedback and automated monitoring systems
 * Identify and implement opportunities for process improvements to reduce bottlenecks and improve overall modeler efficiency.
 * Stay ahead of industry trends and new technologies, incorporating sophisticated methodologies into production systems
 * Provide technical guidance and mentorship to peers, ensuring the team uses new tools and techniques that drive innovation


## Related Videos

- [Architecting the Future: Leveraging AI, Cloud, and Data for Business Success](https://www.wearedevelopers.com/videos/1096-architecting-the-future-leveraging-ai-cloud-and-data-for-business-success)
- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs)
- [How AI Models Get Smarter](https://www.wearedevelopers.com/videos/1374-how-ai-models-get-smarter)
- [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models)
- [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [Best Countries for Software Engineers](https://www.wearedevelopers.com/magazine/267-best-countries-for-software-engineers)
- [The Fastest-Growing Tech Sectors to Look Out for in 2025](https://www.wearedevelopers.com/magazine/373-the-fastest-growing-tech-sectors-to-look-out-for-in-2025)
- [Is Software Engineering Over-Saturated?](https://www.wearedevelopers.com/magazine/418-is-software-engineering-over-saturated)