Edtech.com's Summary

Imagine Learning is hiring an AI Engineer. The role explores new AI capabilities for education on a small, co-located R&D team, turning experiments into working prototypes and reliable software. The engineer collaborates closely with product and research colleagues and reports to the Software Engineering Manager UK.

Highlights

Prototype AI applications and agentic workflows using model APIs, tool calling, retrieval, structured outputs, and state management.
Compare models and architectures for each use case, applying fine-tuning or other model adaptation when evaluation shows a clear advantage.
Build evaluation datasets, automated checks, and human review workflows to measure quality, robustness, and regressions.
Diagnose failures through traces and error analysis, improving grounding, tool reliability, context handling, and recovery from unexpected outputs.
Design sandboxed execution for agent tools and generated code with least-privilege access, network and filesystem restrictions, secrets protection, and resource limits, and test for prompt injection and data leakage with security partners.
Measure latency, throughput, and inference costs while working with application engineers and learning researchers, and document findings so the team can decide what to pursue, revise, or stop.
Bring a bachelor’s degree in computer science, engineering, or a related field and 5 or more years of software or machine learning engineering experience, or an acceptable combination of education and experience.
Show hands-on experience building LLM applications with tool use, retrieval, structured outputs, and evaluation, along with strong Python or TypeScript skills and a practical grasp of sandboxing and security risks in agentic systems.
Experience with multimodal or voice AI, agent orchestration, model adaptation, evaluation infrastructure, or frameworks such as LangChain and LangGraph is preferred.
Hybrid, with a coworking space provided in the Boston area (candidates must live there), about 10% or less travel, and pay of $129,339 to $200,000 per year.

AI Engineer Full Description

Come help us explore what’s possible with AI-powered research and development in education. We’re building a small, entrepreneurial team with room to experiment, big problems to solve, and a shared drive to make a difference. You’ll dig into emerging technology, turn ideas into working prototypes, and learn alongside colleagues who are as curious and committed as you are. We’re looking for people who take initiative, bring energy to hard problems, and care about seeing the work through. If you’re excited to go deep on AI, share what you discover, and move quickly with a team that builds and learns together, you’ll have a hand in shaping this work from the start. Bring your curiosity, your ideas, and your determination to make things better for students and educators.

The AI Engineer explores new AI capabilities for education at Imagine Learning. On a small, co-located R&D team, this hands-on role turns experiments into prototypes and reliable software. We seek an inventive engineer who wants to push beyond established solutions, improve learning, and collaborate closely with product and research colleagues. Success requires comfort with ambiguity, disciplined evaluation, and rapid learning.

Position Information

This is a regular, full-time position, reporting to the Software Engineering Manager UK.

Compensation

Base pay is anticipated to be between $129,339.00 and $200,000.00 per year. Eligible employees may also receive incentive/commission/annual bonus pay based on individual and/or company performance. Compensation may vary based on factors such as, but not limited to, individual skills, experience, training, education/certifications, geographic location, internal equity, and local market conditions.

Location

This US-based position is hybrid, and a coworking space will be provided in the Boston, MA area. Candidates must reside in the Boston area to be considered.

Travel

This role may require limited travel of approximately 10% or less, with occasional travel to Connecticut and, less frequently, Tempe, Arizona. Be sure you have a valid driver’s license, automobile insurance, and a clean driving record for at least the preceding 39 months.

Envision Your Experience

In this role you’ll have the opportunity to:

Prototype AI applications and agentic workflows using model APIs, tool calling, retrieval, structured outputs, and state management.
Compare models and architectures for each use case; apply fine-tuning or other model adaptation when evaluation demonstrates an advantage.
Build evaluation datasets, automated checks, and human review workflows to measure quality, robustness, and regressions.
Diagnose failures through traces and error analysis; improve grounding, tool reliability, context handling, and recovery from unexpected outputs.
Design sandboxed execution with least-privilege access, network and filesystem restrictions, secrets protection, and resource limits for agent tools and generated code.
Test prompt injection, data leakage, and unintended actions; implement output validation, isolation boundaries, and auditable execution with security partners.
Measure latency, throughput, and inference costs; collaborate with application engineers and learning researchers on testable, observable software.
Test emerging techniques, document findings and limitations, and help the team decide what to pursue, revise, or stop.
Other duties as required.

Share Your Expertise

Experience, education, and qualifications essential for success in this role, include:

Bachelor’s degree in computer science, engineering, or a related field and at least 5 or more years of software or machine learning engineering experience; or an acceptable combination of education and experience.
Hands-on experience building LLM applications with tool use, retrieval, structured outputs, and evaluation; ability to explain failures and improvements.
Strong Python or TypeScript skills; experience with APIs, asynchronous code, data models, testing, cloud services, and debugging across system boundaries.
Practical understanding of sandboxing, isolation, access controls, secrets management, and security risks in agentic systems.
Strongly preferred: experience with multimodal or voice AI, agent orchestration, model adaptation, or evaluation infrastructure; active experimentation with emerging AI methods.
Preferred: experience and understanding of agentic toolkits or frameworks such as Deep Agents, LangChain, LangGraph, OpenClaw, Hermes, or comparable tools.

Imagine Learning is an Equal Opportunity Employer committed to providing equal employment and advancement opportunities to qualified individuals. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, sex (including pregnancy, childbirth, lactation, or related medical conditions), gender identity or expression, transgender status (including whether or not you are transitioning or have transitioned), sexual orientation, marital status, religion (including religion dress and grooming practices), age 40 and over, physical or mental disability, medical condition, genetic information (including results of genetic testing and characteristics), veteran and/or military status, or service in the military, and any other basis or status protected under applicable federal, state, or local laws.

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