> Markdown version of [/jobs/ext/4004666-software-engineer](https://www.wearedevelopers.com/jobs/ext/4004666-software-engineer). 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). --- # Software Engineer - **Company:** David Joseph & Company - **Location:** New York, NY, United States - **Experience:** Starter - **Salary:** $125,000.0 - $175,000.0 - **Contract:** Internship / Graduate position - **Skills:** Amazon Web Services, Data Infrastructure, Data Integration, Python (Programming Language), Machine Learning, Software Engineering, Cloud Platform System, Real Time Systems, Backend, Information Technology, Data Pipelines - **Published:** October 29, 2026 - **Apply:** https://www.thejobnetwork.com/job/3aefaef9-f05d-4fba-bb3c-b2be763745f8/software-engineer ## About the Role * Bachelor's degree in Computer Science or a related STEM field * 0 to 2 years of software engineering experience with a back-end focus * Hands-on experience building or maintaining data pipelines or data infrastructure * Experience with platform systems or operations automation * Able to integrate existing ML models into production systems * Able to work on-site five days a week in New York Nice to Haves * Strong internship experience (recent graduates are welcome to apply) * Familiarity with a major cloud platform such as AWS or GCP ## Description You would be a versatile back-end-focused engineer maintaining and extending the real-time systems at the core of the platform: the pricing and bidding engines, data infrastructure, and the tooling that keeps production running. This is a build-and-maintain role on live systems, not greenfield research. What you'll be doing * Maintaining and extending core platform systems: pricing, bidding, and the data infrastructure behind real-time trading * Building and managing data integration and mapping workflows that synchronize data across multiple sources * Developing smaller systems and tooling to resolve production issues * Building ML feature pipelines and integrating existing ML models into production * Contributing across operations automation, platform features, and data infrastructure on two-week sprints Tech stack: Python, with a major cloud platform (AWS or GCP)