Meta·Software Engineer·Onsite - Cross-functional / Panel·Senior

Senior

Jun 2026

Summary

Research round at Meta for a Research Engineer role. The last chunk of the interview opened up into a pretty free-form discussion about building an AI agents team from scratch, which was a nice change of pace from the usual grind but also harder to prepare for than I expected.

Questions Asked (1)

If you had unlimited resources, how would you build a team focused on AI agents? Walk through team composition, research priorities, infrastructure investments, evaluation strategy, and how you'd structure milestones.

Product StrategySystem DesignRoadmap Prioritization

Author's notes

This was the whole last 20 minutes and I kind of underestimated how much ground it would cover.

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AI HintsAI Generated

Suggested Approach

Start by framing the problem around a clear product goal (e.g., building reliable, scalable AI agents for Meta's ecosystem), then walk through each dimension (team, research, infrastructure, evaluation, milestones) in a structured way. Emphasize trade-offs and prioritization, showing how you'd balance long-term research with short-term deliverables.

Pro tip: Tie every decision back to measurable impact and Meta's scale—e.g., how your team structure and evaluation strategy would enable rapid iteration and deployment to billions of users. Show that you understand the difference between research prototypes and production-grade systems.

1. Define the mission and success metrics

Clarify the overarching goal of the AI agents team (e.g., build autonomous agents that can assist users across Meta's apps) and define what success looks like (e.g., task completion rate, user engagement, safety metrics).

2. Design team composition and roles

Outline a cross-functional team including research scientists, ML engineers, infra engineers, product managers, and safety/ethics experts. Explain how you'd structure sub-teams (e.g., research, platform, applications) and the ratio of researchers to engineers.

3. Prioritize research and infrastructure investments

Identify key research areas (e.g., planning, memory, tool use, multi-agent collaboration) and infrastructure needs (e.g., large-scale training clusters, simulation environments, data pipelines). Explain how you'd allocate resources between exploratory research and scalable infrastructure.

4. Establish evaluation and safety strategy

Describe a multi-layered evaluation approach: offline benchmarks, human evaluations, online A/B tests, and red-teaming for safety. Emphasize the importance of continuous monitoring and iterative improvement.

5. Set milestones and roadmap

Propose a phased roadmap with clear milestones (e.g., 6-month: prototype for internal use; 12-month: limited external beta; 24-month: full-scale deployment). Include checkpoints for research breakthroughs, infrastructure readiness, and safety reviews.

Key Points to Mention

Cross-functional team structure with clear ownership and collaboration between research and engineering
Focus on scalable infrastructure (e.g., distributed training, simulation environments) to support rapid experimentation
Balanced research portfolio: foundational (e.g., reasoning, memory) and applied (e.g., tool use, personalization)
Robust evaluation framework combining automated metrics, human feedback, and adversarial testing
Safety and alignment as first-class concerns, with dedicated teams and processes
Milestones tied to product impact and learning goals, with regular reviews and pivots as needed

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.