New opportunity

Engineering Manager, Mobile AI

Notion · New York, United States

About this role

Lead Notion’s Mobile AI engineering team to make its AI features useful on phones and tablets, particularly when users are capturing ideas, catching up between meetings, asking questions, or working away from a desk.

Responsibilities

Define and evolve the roadmap for mobile AI chat, capture, agent workflows, workspace Q&A, and lightweight creation flows.
Move ideas from exploration to shipped products, using prototypes, user feedback, and product data to guide decisions.
Improve production quality by identifying instrumentation gaps, reliability risks, and performance bottlenecks. Build technical foundations that enable fast iteration across mobile, web, and server systems.
Recruit, coach, and grow engineers; set strategy, sequencing, staffing, and delivery for open-ended product work; and collaborate across functions to ship polished experiences.

Required qualifications

At least 2 years managing engineering teams and experience helping teams ship high-quality products at scale.
Hands-on technical leadership to guide architecture, performance, reliability, and release quality, with the ability to make small code contributions when useful.
Practical familiarity with AI tools and curiosity about their effects on products, engineering workflows, and user expectations. Strong execution through ambiguity, cross-functional leadership, and clear communication are also needed. AI research expertise is not required.

Preferred experience:

Leading AI product experiences involving streaming UX, tool use, voice or multimodal input, evaluation loops, or reliability guardrails; managing 0→1 development or rapid iteration; and addressing mobile concerns such as offline resilience, realtime collaboration, rich-text editing, app performance, or release quality at scale.
Delivering end-to-end experiences across mobile, web, and server systems.

The role can be based in New York City or San Francisco. The team works from its offices on Mondays, Tuesdays, and Thursdays. For New York City roles, the estimated base salary is $255k–$290k per year; Notion also offers equity and benefits. Compensation depends on factors including location, role scope, and experience.

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

Skills for this role

Engineering managementMobile AIAI product developmentProduct roadmappingPrototypingProduct analyticsInstrumentationReliability engineeringPerformance optimizationSoftware architectureCross-functional leadershipPeople managementAI toolsAgent workflowsStreaming UXTool useVoice interfacesMultimodal inputAI evaluationOffline resilienceRealtime collaborationRich-text editing

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

Set the mobile AI roadmap
The role owns the roadmap for chat, capture, agent workflows, workspace Q&A, and lightweight creation on phones and tablets. Prepare to explain how you would prioritize these surfaces around users working in the moment.
Turn exploration into shipped products
Notion calls for prototypes, user feedback, and product data to guide what gets built next. Prepare an example of how evidence changed the sequencing or scope of an open-ended product bet.
Protect production quality
The manager is expected to spot instrumentation gaps, reliability risks, and performance bottlenecks before they affect users, while guiding architecture and release-quality decisions. Prepare to discuss how you make those risks visible and act on them.
Build foundations across systems
The posting emphasizes technical foundations that support fast iteration across mobile, web, and server boundaries. Prepare to explain how you would choose shared foundations without slowing delivery.
Lead the team through ambiguity
The role includes strategy, sequencing, staffing, and delivery, alongside recruiting, coaching, inclusive team-building, and cross-functional collaboration. Prepare an example that shows how you aligned people around a decision despite disagreement.

Likely challenges to think through

Balancing breadth with mobile usefulness
A likely challenge, not a confirmed problem, is choosing among several AI surfaces while keeping each experience useful during brief, on-the-go interactions. Prepare a prioritization rationale that distinguishes user value from feature breadth.
Moving fast without weakening quality
A likely trade-off is rapid experimentation versus performance, reliability, and release quality at scale. Prepare to describe decision thresholds for prototyping, instrumenting, and hardening a feature before wider release.
Coordinating end-to-end delivery
Mobile AI work may depend on decisions across mobile, web, and server teams; the difficulty could be preserving a polished experience without blocking iteration. Prepare an example of resolving cross-functional dependencies and communicating trade-offs clearly.

Practice your answers

Technical practice

How would you prioritize mobile AI chat, capture, workspace Q&A, agent workflows, and lightweight creation?

What to coverThe posting assigns this role the roadmap for those surfaces. Suggested preparation (inference): Explain how you would compare in-the-moment user needs, prototype findings, product data, and engineering cost before sequencing work.

How would you decide whether a new mobile AI idea is ready to ship?

What to coverThe posting calls for moving ideas from exploration to product using prototypes, user feedback, and product data. Suggested preparation (inference): Describe what each source of evidence can reveal, what it cannot, and how you would decide to iterate, ship, or stop.

How would you structure a mobile AI feature that spans mobile, web, and server systems?

What to coverThe posting calls for technical foundations that support fast iteration across those boundaries. Suggested preparation (inference): Discuss ownership of shared behavior, interface boundaries, and the trade-off between reuse and a responsive mobile experience.

What would you instrument to detect problems in a mobile AI experience before users report them?

What to coverThe posting specifically names instrumentation gaps, reliability risks, and performance bottlenecks. Suggested preparation (inference): Identify signals across the user journey and explain how you would distinguish a measurement gap from a product or service failure.

How would you investigate a slow mobile AI interaction?

What to coverThe role requires guidance on performance, and the posting highlights mobile app performance. Suggested preparation (inference): Walk through isolating client, network, and server delays, then explain how you would prioritize fixes against user impact.

How would you balance rapid iteration with mobile release quality at scale?

What to coverThe posting asks for high-quality shipping at scale and hands-on leadership of reliability and release-quality decisions. Suggested preparation (inference): Describe how you would assess risk, validate changes, and respond if quality signals deteriorated after release.

How would you evaluate an AI-powered workspace Q&A experience?

What to coverWorkspace Q&A is a named roadmap surface, while evaluation loops and reliability guardrails are listed as nice-to-have experience. Suggested preparation (inference): Discuss usefulness, answer failures, and ways to combine qualitative review with product signals without assuming a particular evaluation system.

What design trade-offs arise in a mobile AI experience with streaming responses or tool use?

What to coverThe posting lists streaming UX and tool use as nice-to-have AI product experience. Suggested preparation (inference): Consider perceived responsiveness, clear progress and error states, and how the interface should handle an interrupted or unsuccessful action.

How would you design a capture flow for intermittent connectivity?

What to coverThe role emphasizes capturing ideas on phones and tablets; offline resilience is listed as a nice-to-have mobile concern. Suggested preparation (inference): Explain how you would preserve user input, communicate its state, and handle synchronization without claiming the current product works this way.

What would you consider when adding voice or multimodal input to a mobile AI flow?

What to coverVoice and multimodal input are listed as nice-to-have experience, not stated requirements for an existing feature. Suggested preparation (inference): Discuss when these inputs help users away from a desk and the usability, performance, and failure-state trade-offs you would test.

Behavioural practice

Tell me about a time you turned an ambiguous product bet into a delivery plan.

What to coverThe posting calls for strategy, sequencing, staffing, and delivery through ambiguity. Suggested preparation (inference): Use a real situation, describe the decisions and trade-offs you owned, and give the outcome and what you learned.

Tell me about a time you coached an engineer while raising a team's technical bar.

What to coverThe posting emphasizes coaching and growing engineers alongside high-quality delivery. Suggested preparation (inference): Give the situation, your specific coaching actions, and an observable result without presenting the engineer's work as solely your own.

Tell me about a cross-functional disagreement you helped resolve.

What to coverThe posting calls for collaboration across functions and clear communication through disagreement. Suggested preparation (inference): Explain the competing perspectives, how you reached a decision, and the resulting product or team outcome.

Tell me about a time you led an early product idea through rapid iteration.

What to coverThe posting describes exploration-to-launch work and lists 0→1 development or fast iteration as nice-to-have experience. Suggested preparation (inference): Ground the example in a real situation, your response to prototype or user evidence, and what ultimately changed or shipped.

Tell me about a time you identified a quality risk before it became a user problem.

What to coverThe posting prioritizes finding instrumentation gaps, reliability risks, and performance bottlenecks early. Suggested preparation (inference): Describe the signal you noticed, the action you coordinated, and the measured result or remaining uncertainty.

Sharpen your knowledge

Topics to prepare

Mobile AI roadmap and sequencing
Posting: The manager owns the roadmap for chat, capture, agent workflows, workspace Q&A, and lightweight creation. Suggested exercise: rank three of these surfaces for users working between meetings, and explain the trade-offs, staffing assumptions, and order of delivery.
AI interactions on mobile
Posting: Hands-on familiarity with AI tools is required; streaming UX, tool use, voice or multimodal input, evaluation loops, and guardrails are nice-to-haves. Suggested exercise: compare two ways an AI answer could appear on a phone, including how each handles delay or an unsuccessful response. Treat the nice-to-haves as optional preparation, not requirements.
Production quality and release decisions
Posting: The role identifies instrumentation gaps, reliability risks, and performance bottlenecks and guides release quality. Suggested exercise: define what you would measure for a mobile workspace Q&A flow and what findings would delay a release.
Architecture across product boundaries
Posting: The team builds foundations for fast iteration across mobile, web, and server systems; end-to-end delivery experience is preferred. Suggested exercise: sketch the responsibilities and failure points of each part of a mobile AI chat flow, then explain one boundary you would change to make iteration safer.
Engineering and cross-functional leadership
Posting: The manager recruits, coaches, and grows engineers, builds an inclusive team, and turns open-ended bets into strategy and delivery with other functions. Suggested exercise: outline how you would resolve a disagreement over scope while preserving a clear decision, owner, and coaching opportunity.

Skills to revise

Prototype a mobile use case
Posting: Prototypes help move mobile AI ideas from exploration to shipped product. Suggested exercise: make a small mock interaction for capturing an idea on a phone, then record the questions it should answer before the team invests further.
Turn feedback and data into a decision
Posting: User feedback and product data guide what to build next. Suggested exercise: using hypothetical feedback and usage figures for a capture flow, write a one-page decision to continue, revise, or stop it, stating what evidence is missing.
Triage a quality regression
Posting: The manager must guide performance, reliability, instrumentation, and release-quality decisions. Suggested exercise: work through a hypothetical slow or failed mobile AI response; identify the observations needed, likely ownership boundaries, immediate release decision, and follow-up measurement.
Review a small technical change
Posting: Hands-on technical leadership includes architecture guidance and small code contributions when useful. Suggested exercise: review a small, hypothetical change to an AI response flow for failure handling; note the correctness, performance, and release checks you would request.
Practice a staffing and coaching conversation
Posting: The role includes staffing open-ended work and recruiting, coaching, and growing engineers. Suggested exercise: draft a brief staffing plan for two competing mobile AI bets, then rehearse explaining a changed priority and giving an engineer specific, constructive feedback.

Your preparation checklist

Prepare a shipped-product walkthrough
Select a product launch you can substantiate. Summarize your management role, the team’s scope, decisions from prototype through release, cross-functional partners, and evidence of quality at scale; distinguish your work from the team’s.
Draft a mobile AI roadmap exercise
As a preparation exercise, prioritize two of the role’s stated surfaces—such as capture and workspace Q&A—for users working away from a desk. Show assumptions, prototype tests, user-feedback and product-data signals, sequencing, and staffing tradeoffs; do not present it as Notion’s actual roadmap.
Sketch an end-to-end technical design
Draw a proposed mobile, web, and server interaction for one mobile AI flow. Explain architecture and release tradeoffs, including how you would assess performance, reliability, and, where relevant, offline resilience or streaming UX; these are discussion prompts, not stated design requirements.
Build a production-quality review
Prepare a one-page example of how you would find instrumentation gaps, reliability risks, and performance bottlenecks before users feel them. Include proposed measurements, failure scenarios, and a release or rollback decision.
Gather people-leadership evidence
Prepare specific examples of recruiting, coaching, growing an inclusive team, and communicating through disagreement. For an ambiguous product bet, explain how you set strategy, sequencing, staffing, and delivery without overstating the outcome.
Show practical AI-tool judgment
Prepare a brief demonstration or account of hands-on AI-tool use: what you tried, how you checked its output, and what it taught you about product or engineering workflows. AI research expertise is not required for this role.
Write questions for the hiring team
Bring questions about which mobile AI moments matter most now, how prototypes and user feedback affect roadmap choices, where production-quality gaps exist, and how the team coordinates across mobile, web, and server.

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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