Edtech.com's Summary

MathWorks is hiring a Senior Applied AI Engineer to architect and build an agentic AI platform that enhances how the company develops software. The role involves driving technical decisions, integrating AI frameworks, and collaborating across teams to implement AI-powered developer tools that accelerate software delivery.

Highlights

Architect and develop core AI platform capabilities including agent orchestration, SDKs, sandboxed runtimes, and LLM gateway infrastructure
Evaluate and integrate external open-source tools ensuring security, extensibility, and developer experience
Apply strong technical judgment in code review, architecture, and build-vs-buy decisions
Define and promote best practices for safe and effective AI agent use in software development workflows
Mentor Applied AI engineers on LLM applications, agent design, and platform engineering
Partner with stakeholders to identify automation opportunities and shape AI tooling roadmaps
Stay updated on AI tooling, agent architectures, and LLM advancements to inform platform strategy
Requires expertise in Python and demonstrated experience with Platform as a Service (PaaS)
Minimum qualifications include a bachelor's degree with 6 years professional experience or advanced degree equivalents
Salary range is $135,700 to $210,400

Senior Applied AI Engineer Full Description

Senior Applied AI Engineer

Team: Product Development
Location: US-MA-Natick
Salary Range: $135,700 - $210,400

Job Summary

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.

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.
Expertise with python
Demonstrated experience in/with Platform as a Service (PaaS)

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)

Original Job Description

Senior Applied AI Engineer

Team: Product Development
Location: US-MA-Natick
Salary Range: $135,700 - $210,400

Job Summary

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.
Expertise with python
Demonstrated experience in/with Platform as a Service (PaaS)

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)

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