New opportunity

Manager, AI & Research Computing Initiatives, IS&T Research Computing

Boston University · Boston, United States

About this role

Boston University Information Services & Technology seeks a manager to lead strategy and planning for AI in Research Computing Services (RCS). The role sets the technical roadmap for AI-enabled research computing, coordinates support for university researchers, supervises the team delivering that support, and represents AI and machine learning needs in broader research computing planning. The position is based in Boston at 2 Cummington Mall, Biological Science Center, and is eligible for a hybrid work schedule.

Responsibilities

Plan GPU and accelerator capacity, containerized and AI workflows, and MLOps tooling. Anticipate compute, data-pipeline, and model-serving needs and translate them into infrastructure plans.
Coordinate daily research-project work, incident management, service requests, and support across technical units and research groups. Follow up on requests and incidents at risk of becoming overdue.
Supervise and develop staff, set priorities and workloads, manage service delivery, and support performance management, hiring, and onboarding.
Produce annual, monthly, and ad hoc reports on system utilization, storage services, buy-in utilization, and other operational metrics. Analyze research workflows, including projects on Secure Research Enclaves or other regulated systems, and work with RCS Operations on chargeback and service models as needed.
Coordinate synchronous and asynchronous training, video tutorials, and updates to RCS technical documentation.

Required qualifications include experience developing or leading AI, machine learning, research computing, or advanced computing infrastructure strategy; knowledge of the relevant GPU, workflow, data-pipeline, model-serving, and MLOps needs; and experience coordinating technical work, supervising staff, reporting on operations, and analyzing research computing or regulated-environment workflows. Bonus qualifications include supporting AI-intensive grant-funded research or working with faculty and labs; experience with secure or regulated research systems and service models; and coordinating technical training or documentation. The expected hiring range is $120,000.00–$130,000.00. Listed benefits include paid time off, a paid intersession break, 13 paid holidays, a university-funded retirement plan, and tuition assistance.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI infrastructure strategyResearch computingGPU capacity planningAccelerator capacity planningContainerized workflowsAI workflowsMLOpsData pipelinesModel servingTechnical project coordinationIncident managementService deliveryStaff supervisionOperational reportingBusiness process analysisSecure Research EnclavesChargeback modelsTechnical trainingTechnical documentation

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

AI research-computing roadmap
Source-backed: Set the technical roadmap for GPU and accelerator capacity, containerized AI workflows, and MLOps; translate emerging compute, data-pipeline, and model-serving needs into infrastructure plans.
Bridge researchers and technical teams
Source-backed: Serve as the primary technical bridge between IS&T and the academic and research community, coordinating support across RCS, other technical units, and research groups.
Lead staff and service delivery
Source-backed: Coordinate daily project work, incidents, and service requests; follow up on work at risk of becoming overdue; supervise staff and set priorities and workloads.
Use operational evidence to shape services
Source-backed: Report on system utilization, storage, and buy-in utilization; analyze research workflows, including those on secure or regulated systems; engage RCS Operations on chargeback and service models as needed.
Keep training and documentation current
Source-backed: Coordinate synchronous and asynchronous training, video tutorials, and updates to RCS technical documentation.

Likely challenges to think through

Plan capacity amid changing research demand
Preparation hypothesis: Emerging AI workloads may make it difficult to balance GPU capacity against uncertain compute, data-pipeline, and model-serving needs. Prepare an example of how you would gather demand signals, compare options, and revise an infrastructure plan.
Protect roadmap work while maintaining service
Preparation hypothesis: Incident follow-up and service requests could compete with longer-term planning and staff development. Prepare a prioritization approach that explains what you would delegate, escalate, or defer, and how you would track overdue risk.
Support varied workflows without overlooking controls
Preparation hypothesis: Research projects on secure or regulated systems may need different workflows from other projects, complicating service design. Prepare to explain how you would analyze workflow needs, involve relevant technical teams, and use operational reporting to inform service-model discussions.

Practice your answers

Technical practice

How would you build an AI research computing roadmap?

What to coverThe role sets the technical roadmap and represents AI/ML needs in broader research computing planning. Suggested preparation: explain how you would gather researcher needs, assess current capacity, and prioritize investments under resource constraints.

How would you plan GPU and accelerator capacity?

What to coverGPU and accelerator capacity planning is an explicit responsibility. Suggested preparation: discuss workload demand, utilization, allocation, and the trade-off between anticipated growth and unused capacity.

How would you support containerized research workflows?

What to coverThe posting identifies containerized and AI workflows as roadmap areas. Suggested preparation: describe how you would assess workflow requirements and balance researcher flexibility with reproducibility and operational support.

How would you translate data-pipeline needs into infrastructure plans?

What to coverThe role must anticipate data-pipeline needs and turn them into infrastructure plans. Suggested preparation: cover data movement, storage demands, workflow bottlenecks, and how you would validate assumptions with research groups.

How would you plan for model-serving needs?

What to coverModel serving is named among the emerging needs this manager must anticipate. Suggested preparation: explain how usage patterns, compute demand, and ongoing operations would shape an infrastructure plan.

How would you evaluate MLOps tooling for researchers?

What to coverMLOps tooling is part of the advertised roadmap. Suggested preparation: discuss how you would compare researcher workflow needs, maintainability, and the support burden without assuming a particular tool is in use.

How would you manage a research computing incident at risk of becoming overdue?

What to coverThe role coordinates incidents and service requests, follows up on overdue risks, and works across technical units. Suggested preparation: walk through triage, ownership, communication with the research group, escalation, and resolution tracking.

What would you include in an operational resource report?

What to coverThe manager produces annual, monthly, and ad hoc reports on system utilization, storage services, buy-in utilization, and other metrics. Suggested preparation: explain how you would check data quality, distinguish trends from one-off spikes, and make findings useful for planning.

How would you analyze a workflow on a regulated research system?

What to coverBusiness process analysis includes projects on Secure Research Enclaves or other regulated research systems. Suggested preparation: describe how you would map the workflow, identify support and infrastructure dependencies, and clarify applicable constraints before proposing changes.

How would you assess a research computing service or chargeback model?

What to coverThe role engages RCS Operations on chargeback and service models as needed. Suggested preparation: discuss how utilization, storage, buy-in reporting, researcher needs, and operational effort could inform trade-offs; do not assume a specific pricing model.

Behavioural practice

When have you reconciled differing needs from researchers and technical teams?

What to coverThe role serves as a bridge between IS&T and the academic and research community. Suggested preparation: use a real situation, explain how you heard both sides and agreed on a course of action, and state the result.

When have you coordinated a time-sensitive issue across teams?

What to coverThe posting calls for cross-functional incident and service-request coordination. Suggested preparation: give a real situation, your actions to establish ownership and communicate progress, and the outcome.

How have you balanced service delivery with staff development?

What to coverThe manager sets workloads and priorities while mentoring staff and supporting professional development. Suggested preparation: describe a real competing-demand situation, the decisions and coaching you provided, and the result for the team and service.

When have you made technical training or documentation more useful?

What to coverThe role coordinates synchronous and asynchronous training, video tutorials, and documentation updates. Suggested preparation: give a real example of the audience's need, what you changed or coordinated, and how you assessed the result.

When have you changed a technical plan after learning more about stakeholder needs?

What to coverThis position leads a roadmap while coordinating support for research groups and partnering on broader strategy. Suggested preparation: describe the original plan, the new evidence and your decision, and the outcome without implying that any particular approach is required at BU.

Sharpen your knowledge

Topics to prepare

AI research computing strategy
The role sets the technical roadmap and represents AI/ML needs in broader research computing planning. Suggested revision exercise: outline how researcher needs, infrastructure constraints, and service priorities would shape a roadmap; distinguish near-term decisions from longer-term planning.
GPU and accelerator capacity planning
Capacity planning is an explicit roadmap responsibility and required knowledge area. Suggested revision exercise: compare how concurrent research workloads, utilization, and anticipated demand would affect capacity decisions.
AI workflow infrastructure
The posting names containerized and AI workflows, data pipelines, model-serving needs, and MLOps tooling. Suggested revision exercise: trace a research workflow from data preparation through model serving, noting where infrastructure or operational support is needed.
Research service delivery and team leadership
The manager coordinates projects, incidents, and service requests while supervising staff and setting workloads. Suggested revision exercise: explain how you would distinguish an urgent incident from a routine request and balance both against planned research-project work.
Regulated research workflows and service models
Business process analysis for research computing or regulated environments is required. Secure Research Enclave experience and chargeback or service-model experience are bonus qualifications. Suggested revision exercise: sketch the questions needed to understand a regulated project's workflow and its support needs.

Skills to revise

Draft a capacity-planning brief
Suggested practice: given illustrative workload demand and utilization figures, write a one-page GPU and accelerator capacity recommendation with assumptions and open questions. This rehearses the posting's infrastructure-planning responsibility; the figures are practice inputs, not BU data.
Triage a support queue
Suggested practice: sort example research-project requests and incidents by urgency, identify items at risk of becoming overdue, and specify which technical or research group needs to be engaged. The posting explicitly assigns this coordination and follow-up.
Allocate team work
Suggested practice: make a weekly workload plan covering project work, service requests, and incidents, then explain how priorities would change after an urgent issue. This relates to staff supervision, workload setting, and service delivery.
Summarize operational metrics
Suggested practice: turn illustrative system-utilization, storage-service, and buy-in-utilization figures into a short monthly report that flags trends and data gaps. The role produces monthly, annual, and ad hoc resource reports.
Map a research-project process
Suggested practice: diagram a hypothetical project's request, infrastructure, support, and handoff steps, including questions to raise if it uses a regulated system. This targets the required business process analysis; Secure Research Enclave experience is listed as a bonus.
Plan a training and documentation update
Suggested practice: outline a short researcher-facing tutorial and the accompanying documentation changes for an AI research computing workflow. The role coordinates synchronous and asynchronous training and RCS documentation updates; prior experience doing so is a bonus qualification.

Your preparation checklist

Draft an AI research-computing roadmap
The role sets the AI-enabled RCS roadmap. Prepare a sample plan showing how you would assess GPU and accelerator capacity, containerized workflows, data pipelines, model serving, and MLOps tooling; label assumptions rather than presenting them as BU's current setup.
Prepare a technical project walkthrough
Select a relevant project you can discuss and outline the research need, infrastructure decisions, collaborators, tradeoffs, and results. Be ready to explain how it informed a roadmap or service decision.
Map a service coordination scenario
The posting emphasizes research projects, incidents, service requests, and overdue follow-up. Prepare a short scenario showing how you would prioritize work, coordinate technical units and research groups, and track resolution.
Gather staff-leadership examples
Prepare examples of setting workloads, mentoring staff, and managing service delivery, with specific actions and outcomes. Include hiring, onboarding, or performance-management work where applicable.
Sketch an operations and workflow review
The role reports on system, storage, and buy-in utilization and analyzes research workflows. Draft a sample report outline and a workflow-review approach, including how you would identify questions for RCS Operations about regulated systems, chargeback, or service models.
Outline a training and documentation plan
Prepare a brief example of how you would coordinate synchronous training, asynchronous material or video tutorials, and documentation updates for researchers and support staff.
Write questions for RCS leadership
Ask how roadmap priorities are set with researchers, which capacity and service metrics matter most, how cross-unit incidents are coordinated, and where the team needs the most support. These are interview questions, not assumptions about BU's current practices.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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