The Role
Lead the engineering function inside InterDigital’s AI Transformation team. Set technical direction, own delivery against the plan, and manage and build the team that delivers it.
The Team
Our AI Transformation team applies AI to how InterDigital works. It is a small group of engineers and product managers operating close to the business, with a mandate that runs company-wide. This role leads the engineering side of it and reports to the Vice President, Strategy and AI Transformation.
Essential Duties and Responsibilities
- Own the architecture and engineering platform. Define the technical architecture, platform capabilities, shared services, interfaces and engineering standards the team builds on. Make deliberate choices about what belongs in reusable infrastructure versus individual applications.
- Translate business requirements into technical systems. Work with product management to turn business needs and workflow requirements into scalable technical approaches. Understand the capabilities and constraints of the infrastructure, challenge assumptions where needed, and identify the simplest architecture that can reliably meet the requirement.
- Anticipate engineering constraints and failure modes. Identify scaling, integration, data-access, security, reliability, latency, model, cost and maintainability issues early. Surface technical tradeoffs before they become delivery problems and design around constraints rather than discovering them after implementation.
- Manage and build the team. Lead the engineering team and grow it to the size and shape the plan requires. Own hiring, onboarding, development and retention, including the technical career paths that keep strong engineers here.
- Own delivery. Turn product priorities into executable engineering plans. Decompose work, size it, identify dependencies, commit to dates and surface risks or slips early. Maintain a clear view of what the team can deliver and what is constraining it.
- Create engineering leverage. Expand the team's capacity by building scalable practices and thoughtfully leveraging resources. Establish effective use of AI-assisted development, reusable components, shared infrastructure, automation and engineering practices that compound across projects.
- Own the engineering side of build versus buy. Supply what it costs to build, buy, and run, and hold the team to the answer. Nothing gets built here that a foundation model API or an existing service already does well.
- Build the measurement: the evaluation harness and instrumentation needed to measure quality, usage, latency, reliability, and cost in production.
- Set priorities and stretch them. Sequence the work with product management, then deliver more against that plan than the headcount predicts through sharper tooling, reusable building blocks and a cohesive team.
- Work inside enterprise constraints. Data classification, information security review and sanctioned tooling are real limits here.
Qualifications
- 8+ years building software, including 5+ years leading engineers with meaningful technical and people-management responsibility.
- Demonstrated ownership of architecture and technical direction for production software systems, preferably in a small or rapidly evolving engineering environment.
- Strong systems intuition: able to move from an ambiguous business requirement to a technical architecture and identify likely constraints, dependencies, failure modes and tradeoffs before significant engineering effort begins.
- Hands on and technically current: able to inspect and review code, prototype when necessary, interrogate architectural decisions, and use modern AI-assisted development tools.
- A record of changing how an existing engineering team works, including moving experienced engineers onto new tools and practices.
- Strong estimation and delivery discipline: able to decompose work, identify uncertainty, explain assumptions and risks, and communicate technical delivery plans clearly to non-technical executives.
- Working fluency with modern AI application architecture, including model APIs, retrieval, agent and tool orchestration, evaluation, observability, data access, security, latency and inference-cost management.
- An evaluation-oriented engineering approach: understanding of how product quality requirements translate into testable system behavior, regression testing and production measurement.
- Quantitative rigor: able to define metrics for output quality, design the tests that measure them, and read the results correctly. Formal training in data science, applied statistics or a related field preferred.
- Python and a modern backend stack. Cloud platform depth, with Azure preferred given our standardization path.
- B.S. in Computer Science, Engineering or a related field.
Preferred
- Experience designing platforms or shared infrastructure that allows multiple products, workflows or engineering teams to reuse common capabilities.
- Experience running a team against more demand than it could serve, and a clear account of how those tradeoffs were made.
- Experience with external engineering capacity: selecting vendors, standing up contracted teams, and holding them to delivery requirements.
- Experience running engineering against sensitive or proprietary data where third-party access is the gating constraint.
- Patent, standards or legal technology exposure. Useful, not required. The domain depth is in house.
Location
Conshohocken, PA
