Senior Applied AI Engineer

Job Summary

Math Works has a hybrid work model that enables staff members to split their time between office and home. The hybrid model provides the advantage of having both in-person time with colleagues and flexible at-home life optimizations. Learn More:

We are looking for a senior software engineer to join our Applied AI team in Natick, MA building the agentic AI platform that will transform how Math Works develops software. Our team is responsible for enabling AI-powered tools and systems central to the transformation of how we plan, build, deliver, and monitor our products.

You will architect and build core platform capabilities while shaping the technical direction for AI-powered developer tooling across the organization. This role requires strong technical judgment in a fast-moving domain: you’ll make consequential decisions about architecture, frameworks, and code quality in an environment where best practices are still being established. You will work with a group of self-starting, cross-functional engineers and build strong relationships across the development organization to drive adoption of AI systems that meaningfully accelerate how we ship software.

Math Works nurtures growth, appreciates inclusivity, encourages initiative, values teamwork, shares success, and rewards excellence.

• Design and drive the architecture of Math Works’ agentic AI platform — agent orchestration, SDK, sandboxed runtime environments, and LLM gateway infrastructure
• Evaluate, select, and integrate external frameworks and open-source tooling (agent frameworks, orchestration layers, context management systems) against internal requirements for security, extensibility, and developer experience
• Exercise strong technical judgment in code review, architectural decisions, and build‑vs‑buy tradeoffs — setting a high bar for code quality, maintainability, and security across the team’s work
• Define patterns, abstractions, and best practices for safe, effective use of AI agents across software development workflows — and drive their adoption across the development organization
• Scope and decompose ambiguous, cross‑cutting technical problems into actionable work for yourself and the team
• Mentor engineers on the Applied AI team through code review, design collaboration, and knowledge sharing — raising the team’s collective expertise in LLM application development, agent design, and platform engineering
• Partner with stakeholders across the development organization to understand workflows, identify high‑leverage automation opportunities, and shape the technical roadmap for AI‑powered developer tooling
• Stay current with a rapidly evolving landscape of AI tooling, agent architectures, and LLM capabilities, and translate that knowledge into platform decisions

• Expertise with Python
• Demonstrated experience in/with Platform as a Service (PaaS)
• A bachelor's degree and 6 years of professional work experience (or a master's degree and 3 years of professional work experience, or a PhD degree, or equivalent experience) is required.

• Experience designing agent systems that combine deterministic and LLM‑driven orchestration, including sandboxed execution environments, tool integration, and context engineering
• Experience building evaluation, observability, or quality assurance systems for AI/ML — including the ability to measure, trace, and improve system behavior in production
• Experience designing and building internal developer platforms, SDKs, or developer tooling adopted by other engineering teams
• Strong written and verbal communication skills, with the ability to explain complex technical decisions to both engineering and non‑engineering audiences
• Demonstrated ability to evaluate build‑vs‑buy trade‑offs and integrate open‑source or third‑party tooling into enterprise systems
• Understanding of security considerations for AI systems in enterprise environments, including sandboxing, access control, and safe agent execution
• Track record of driving technical direction across team boundaries — including the communication, relationship‑building, and stakeholder alignment needed to influence engineering practices at an organizational level
• Deep experience with software development tooling and workflows (CI/CD, code review systems, testing infrastructure)