At Two Six Technologies, we build, deploy, and implement innovative products that solve the world’s most complex challenges today. Through unrivaled collaboration and unwavering trust, we push the boundaries of what’s possible to empower our team and support our customers in building a safer global future. Would you like to use your engineering skills and your experience with machine learning (ML) and large language models (LLMs) to help policy makers, corporate leaders, and operators make sense of increasingly complex information spaces? Are you excited by the possibilities of generative and agentic artificial intelligence? Because Two Six Technologies uses these technologies to provide Information Advantage to Government, Military, and private sector customers tasked with mission critical decision making. Our products collect, synthesize, and enrich huge quantities of publicly available data, then serve that data to be queried by our expert analysts and to be used in machine learning analytics. As a data pipeline engineer you will work with product engineering teams, data scientists, devops, and architects to integrate data science into our products. As a top candidate for this role, you are an experienced server-side Python engineer with a history of building products or platforms with integrated ML capabilities. You are very comfortable working with AWS, Linux, and using technologies like Docker and Kubernetes. You also have worked as a member of remote engineering teams in Agile software development environments. You are a good fit if you want to continue to grow your career in the ML space by taking data science and making it available to end users in fast, scalable, and secure applications. This role is right for you if you have managed before, or if you have been a team lead or senior engineer before and are interested in managing a small team of data pipeline engineers while also being a hands-on data pipeline engineer yourself. Most importantly you share our passion for applying technology to solve hard problems and finding innovative ways to derive insights from high-volume data.
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Job Type
Full-time
Career Level
Mid Level
Number of Employees
501-1,000 employees