AI Engineer

JST•,

1d•Remote

About The Position

JST is seeking an AI Engineer to help build a new AI-powered product from the ground up. The Engineer will use advanced AI development harnesses to move from product ideas to working software across the full stack, including cloud infrastructure, application services, data workflows, user interfaces, operational tooling, and software support. This role is designed for a recent graduate of a technical master's or doctoral program who has strong engineering fundamentals and wants broad product ownership. You will work directly with the Head of AI Engineering, learning how to make sound architecture and product decisions while shipping useful, secure, and maintainable capabilities. You do not need to arrive as an expert in every layer of the stack. This is a full-time remote, project-based position. You will report to the Head of AI Engineering and collaborate closely with product leaders, domain experts, designers, and other engineers through a distributed working environment. Joint Strategic Technologies's environment rewards practical experimentation, careful reasoning, and the willingness to work across conventional role boundaries. The team will expect you to use AI tools aggressively where they improve speed or quality, and to verify the result with the same care you would apply to code written by hand. JST offers a complete benefits package, a collaborative work environment, and strong company culture. Veterans and military spouses are encouraged to apply.

Requirements

A recently completed or soon-to-be-completed master's or doctoral degree in computer science, artificial intelligence, machine learning, software engineering, data science, robotics, human-computer interaction, or a closely related technical field.
Strong programming fundamentals and practical experience in at least one modern language. Python, TypeScript, or JavaScript experience is especially relevant.
A working understanding of APIs, databases, version control, testing, debugging, and the core concepts behind cloud or distributed applications.
Evidence that you can take an ambiguous problem, break it into manageable steps, and finish a working project. Academic, research, startup, open-source, and professional projects all count.
Clear written and verbal communication, intellectual curiosity, and comfort asking questions when requirements or evidence are incomplete.
Good product judgment: you consider the user's workflow, usability, reliability, and support needs alongside the technical implementation.
MUST be able to successfully pass a drug and background check

Nice To Haves

Two to three years of relevant industry experience. This is preferred rather than required; candidates with less experience and an unusually strong record of building and shipping will be considered.
Exposure to product development through an engineering, research, design, startup, internship, co-op, or customer-facing project. A previous product-management title is not required.
Hands-on work with large language models, retrieval-augmented generation, agent frameworks, tool calling, AI evaluations, or AI-assisted software-development workflows.
Experience with a major cloud platform, infrastructure as code, serverless systems, containers, CI/CD, or production monitoring.
Experience building modern web interfaces or collaborating on UX, design systems, accessibility, and user research.
Interest in privacy, security, responsible AI, and the demands of enterprise or government software.

Responsibilities

Use advanced AI coding and engineering harnesses to turn product requirements, prototypes, and technical designs into tested production software.
Build complete product capabilities across cloud infrastructure, backend services, APIs, data pipelines, web applications, and user-facing experiences.
Design agentic systems that can plan work, use tools, coordinate workflows, retain appropriate context, and automate repeatable product and operational tasks.
Implement the controls that make AI systems dependable, including human approval points, permissions, evaluations, observability, failure handling, and cost and latency measurement.
Work with product leaders and domain experts to understand real workflows, identify the smallest useful release, and refine the product from user feedback.
Own vertical slices of the product from design through deployment and support, with guidance and review from the Head of AI Engineering.
Evaluate models, prompting approaches, retrieval patterns, and tool-use designs against practical measures of quality, safety, speed, and cost.
Write clear tests, technical documentation, runbooks, and support notes so that the product can be operated and improved by the broader team.
Investigate software issues, improve reliability, and turn recurring support needs into better product behavior or safe automation.