Team: Web Applications and Services
Location: US-MA-Natick
Salary Range: USD 210,400 - 135,700
Job Summary
MathWorks 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: https://www.mathworks.com/company/jobs/resources/applying-and-interviewing.html#onboarding.
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 MathWorks 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.
MathWorks nurtures growth, appreciates inclusivity, encourages initiative, values teamwork, shares success, and rewards excellence.
Responsibilities
Design and drive the architecture of MathWorks’ 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
Minimum Qualifications
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.
Demonstrated experience in/with Platform as a Service (PaaS)
Expertise with Python
Additional Qualifications
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 tradeoffs 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)