Product Engineering Manager

Aaru•New York, NY

3d•$280,000 - $425,000•Onsite

About The Position

Aaru builds simulations of human behavior using AI agents to model real-world decision-making. Companies use these simulations to test consequential choices before committing to product launches, pricing, communications, or policy changes. Building useful simulations requires populations that represent real people, calibrated predictions, coherent simulations, and legible evidence for decision-making. Aaru is a small, in-person team in New York that values urgency, high ownership, and intellectual honesty, expecting team members to surface inconvenient evidence, change their minds quickly, and carry important work to completion. Product Engineering at Aaru focuses on transforming simulation capabilities into user-independent and repeatable products. The team builds on the shared platform and simulation systems to create workflows, interfaces, integrations, and decision-ready artifacts that make a sophisticated system clear and dependable. This function is organized around durable product domains, not just feature queues, and includes areas like simulation setup, experiment types, continuous simulations, analysis, reporting, customer templates, collaboration, and integrations. Product Engineering owns end-to-end product outcomes, collaborating with Platform Engineering, Simulation Engineering, Research, Product, Design, and Deployment. As a Product Engineering Manager, you will lead a focused team of Product Engineers, responsible for their quality, pace, and impact. Managers at Aaru are also builders, setting technical direction, shaping product decisions, reviewing designs, writing code when most effective, hiring engineers, and developing team members. You will partner with a Product Manager and Product Designer to select problems, define product domains, establish roadmaps balancing immediate value with reusable foundations, and create operating mechanisms for efficient workstreams. This is a line-management role focused on making a small team highly effective through context, high standards, ambiguity resolution, direct feedback, and ensuring commitments result in excellent production systems.

Requirements

You were a strong product or full-stack engineer before becoming a manager and remain capable of going deep in architecture, code, and debugging.
You have led a small engineering team that shipped and operated meaningful production products, preferably in a fast-moving or ambiguous environment.
You have strong product judgment and can move between customer evidence, interaction design, technical architecture, and organizational tradeoffs.
You know how to make a team faster through clarity and better systems rather than through constant intervention or lowered standards.
You can separate a compelling prototype from a product that is ready to carry real customer decisions.
You make crisp decisions with incomplete information, state the assumptions behind them, and change direction when the evidence changes.
You reduce complexity in product scope, architecture, code, process, and team ownership.
You give clear feedback early, address performance problems promptly, and take genuine responsibility for developing people.
You recruit well and can explain a difficult mission, a demanding environment, and an unusually high bar without relying on title inflation.
You want to work in person in New York with a team that moves quickly and resolves hard questions directly.

Nice To Haves

Experience leading an AI-native product team, particularly one involving agents, model-dependent workflows, or probabilistic outputs.
Experience as a founder, founding engineer, or early engineering leader at a fast-growing startup.
Experience managing full-stack teams that work closely with Product and Design while depending on shared platform or infrastructure teams.
Experience with B2B or enterprise products involving integrations, permissions, auditability, complex configuration, or high-consequence workflows.
A record of turning customer-specific work into a durable product platform or reusable set of primitives.
Experience partnering with research or machine-learning teams and creating a disciplined path from experimental capability to production product.
Experience building teams through a period of rapid hiring while preserving talent density and technical culture.

Responsibilities

Build, lead, and develop a high-performing team of Product Engineers with clear ownership, strong technical judgment, and a high standard for product craft.
Set technical direction for the team's product domain across frontend, backend, APIs, data models, workflow orchestration, integrations, observability, and model-dependent behavior.
Partner with Product and Design to identify the most important user problems, define a coherent roadmap, and make principled decisions about scope and sequence.
Create an execution model for parallel workstreams: clear DRIs, credible milestones, early risk discovery, focused reviews, and fast escalation when dependencies or assumptions fail.
Stay close to the work through system design, architecture reviews, code review, debugging, product critique, user sessions, and direct contribution to the hardest or most ambiguous problems.
Ensure that the team turns specific customer evidence into generalizable product capabilities rather than accumulating custom branches, configuration debt, or manual operations.
Establish a quality bar for AI-native product development, including evaluations, human review points, instrumentation, rollout criteria, fallbacks, provenance, and safe rollback.
Define clean interfaces with Platform Engineering and Simulation Engineering. Work through missing primitives explicitly and contribute to shared foundations when that is the right organizational answer.
Build tight feedback loops with Deployment and customers so that field failures, confusing workflows, and unexpected model behavior become prioritized product and engineering work.
Own production quality for the team's systems, including reliability, latency, security, permissions, cost, on-call health, incident follow-up, and maintenance.
Recruit exceptional engineers from sourcing through close. Build a team with the right mix of product sense, technical depth, speed, and ownership.
Set expectations clearly, provide frequent and candid feedback, recognize exceptional work, address performance problems early, and invest in each engineer's growth.
Improve the broader engineering organization through reusable patterns, stronger development tools, better technical communication, and a culture of thoughtful urgency.