Simulation Engineering Manager

Aaru•New York, NY

16d•Onsite

About The Position

Simulation Engineering owns the path from a promising research result to a production system that customers can trust. Researchers may prove a new method for constructing a population, modeling a world, estimating an outcome, or evaluating fidelity. Simulation Engineering turns that method into robust, reusable, observable, and efficient software. The team's quality bar is broader than conventional service reliability. A production simulation must be behaviorally faithful, calibrated, reproducible, measurable, fast enough to use, economical enough to scale, and reliable under real customer workloads. The team owns the abstractions, contracts, evaluation gates, workflows, and operating systems that make those properties possible. Simulation Engineering is not a research-support queue. It is the engineering owner of the simulation system in production. When a simulation method breaks, regresses, becomes too expensive, or produces conclusions that cannot be explained, this team is accountable for finding the cause and restoring trust. As Simulation Engineering Manager, you will lead a focused team of Simulation Engineers and be accountable for the quality, pace, and operational health of Aaru's production simulation system. Managers at Aaru remain engineers. You will set technical direction, review critical designs, write and debug code when needed, inspect model and system failures, hire exceptional engineers, and develop the people on the team. You will operate at the interface of Simulation Research, Population Research, Prediction Research, Evaluation Research, Product Engineering, Platform Engineering, Infrastructure, and Deployment. A major part of the job is establishing explicit handoffs and shared evidence: what a research result must demonstrate before productionization, what the production system must expose for evaluation, and how field failures return to the right research or engineering owner. This is a line-management role. You are responsible for making one team exceptionally effective, not for building a layer of managers beneath you. You should expect to remain close to architecture, code, experiments, incidents, and the hardest technical tradeoffs.

Requirements

You were a strong software, machine-learning, or systems engineer before becoming a manager and remain comfortable going deep in code and architecture.
You have led a small engineering team that shipped and operated an AI, ML, data, or distributed system in production.
You understand that model behavior is part of the product and can debug failures that cross data, prompts, orchestration, statistical assumptions, services, and user-facing outputs.
You can take an underspecified research method and turn it into a deterministic, testable, observable, and efficient production system.
You design evaluations before trusting improvements and can distinguish benchmark movement from a meaningful gain in real-world quality.
You make clear tradeoffs among fidelity, calibration, latency, cost, reliability, maintainability, and iteration speed.
You build effective working relationships with researchers without lowering the engineering bar or imposing process that destroys research velocity.
You give clear feedback, develop technical judgment in others, and address performance or ownership problems promptly.
You can recruit unusually strong engineers and explain why this work demands both research sensitivity and production rigor.
You want to work in person in New York with a team that moves quickly and takes direct responsibility for its systems.

Nice To Haves

Experience with LLM applications, agentic systems, multi-agent frameworks, model orchestration, or inference-time computation.
Experience with model evaluation, post-training, experimentation platforms, synthetic data, forecasting systems, or probabilistic models.
Experience building simulation, scientific-computing, distributed-compute, workflow, or data-intensive systems at meaningful scale.
Experience with reproducible experimentation, model and data versioning, feature stores, observability for ML systems, or safe model rollout.
A research background or a record of close collaboration with research teams, including reading papers and translating experimental methods into software.
Time as a founder, founding engineer, or early engineering leader at a fast-growing AI company.
Experience building and operating teams through rapid growth while preserving a high technical bar.
Prior employment at a simulation company or formal training in computational social science.
Managed managers or a large organization; this role is about direct leadership of a focused technical team.
Expertise in every relevant research method.

Responsibilities

Build, lead, and develop a high-performing team of Simulation Engineers with clear ownership of production simulation quality.
Own quality across behavioral fidelity, calibration, reproducibility, latency, throughput, cost, reliability, debuggability, and safe operation.
Define the architecture and operating model that carries a method from research prototype through evaluation, integration, rollout, observation, and continuous improvement.
Establish clear readiness criteria with Research and Evaluation: strong baselines, decisive experiments, protected holdouts, known limitations, and evidence that survives changes in domain, population, and time.
Design and maintain the core abstractions of the simulation system, including agent and population representations, simulation contracts, environment and state models, workflow boundaries, evaluation interfaces, and publication layers.
Build evaluation harnesses, benchmarks, ablations, graders, regression suites, and launch scorecards that distinguish a faithful simulation from a merely plausible output.
Make large simulation runs reproducible and inspectable by versioning models, prompts, data, populations, environments, code, and experiment configuration.
Build observability that connects system behavior to model behavior: traces, intermediate artifacts, cohort diagnostics, cost and latency curves, failure classification, and comparison across versions.
Set technical direction for scaling large population runs without concealing uncertainty or trading away the fidelity that makes the simulation useful.
Partner with Product Engineering to adapt the simulation system for new product goals while preserving valid contracts and avoiding product-specific forks in the core engine.
Build fast feedback loops with Deployment.
Convert field failures, surprising customer outcomes, and operational incidents into concrete hypotheses, durable tests, fixes, and research questions.
Own the team's production operations, including on-call health, incident response, release safety, rollback, maintenance, and the elimination of recurring operational toil.
Recruit exceptional engineers, provide direct feedback, develop technical leaders, and address performance or ownership gaps early.
Improve engineering practice across Aaru by raising the bar for experimental discipline, production code, technical design, reproducibility, and post-incident learning.