ML Systems - Member of Technical Staff

ABOUT THE COMPANY

Pilots don't train with real passengers. Actors don't rehearse with real audiences. Yet, the most consequential decisions in society are often pushed straight to production.

Simile is changing that. We have built the first AI simulation of society, populated by generative agents based on real humans. Our research pioneered the field of AI-based simulation, proving it is possible to model human behavior with high accuracy. Today, we are developing a Foundation Model to predict human behavior in any situation, at any scale.

We have raised $300M in total funding, with a $2B post-money valuation in our latest round. Our investors include Greenoaks, Index Ventures, Hanabi, A*, Bain Capital Ventures, Factory, CVS Health Ventures, and Definition.

ABOUT THE TEAM

Simile's Research organization builds the systems that every step of the model lifecycle runs on: data ingestion and schema design, distributed training, evaluation, serving, and monitoring. We are the reason a researcher's hypothesis can become a production simulation in days rather than quarters.

Two things make this problem unusual. First, our research-to-product pipeline is unusually tight - the experimental methods we validate on Monday are integrated into systems customers use to make high-stakes decisions. Second, simulating a society means running inference over populations of agents, not single requests. A single customer study can mean millions of model calls with interdependent state. Cost per simulation and latency per agent are not back-office metrics for us; they determine what research is even possible to run.

ABOUT THE ROLE

As a Member of Technical Staff in ML Systems, you will build the platform our researchers train, evaluate, and deploy on - and own it through the last mile, where a trained checkpoint becomes a production service serving millions of interdependent agent calls at a cost per simulation we can afford.

This is a role for someone who is energized by both halves of that. You will spend some weeks designing the data schemas and training pipelines a research team depends on, others profiling a serving path to find where the FLOPs and GPU memory are going, and others still bringing up cluster nodes or deleting the third redundant copy of a code path. The common thread is leverage: every improvement you make compounds across every researcher and every simulation we run.

We are looking for engineers who find it gratifying to see their work pushed to its absolute limits, and who own problems end-to-end - including the last mile of deployment that most people would rather hand off.

IN THIS ROLE, YOU WILL

• Build the ML platform our researchers live in. Design and operate the services, libraries, and tooling that cover the full lifecycle - data exploration, feature generation, experiment tracking, training orchestration, evaluation, and deployment. Success is defined by your ability to increase experiment velocity, streamlining the researcher’s path from ideation to a fully validated, production-ready model.

• Make training and data pipelines fast. Own throughput end to end: model FLOPs utilization across our training configs, tokenization cost when the data mix changes, and ingestion paths that take hours today where they should take minutes. Profile where the time and GPU memory actually go, then fix it, including the observability that makes the next bottleneck obvious before it bites.

• Make serving fast and cheap enough to run a society. Own the inference path our simulations run on: batching and scheduling, KV cache reuse across agents sharing context, quantization, and the request patterns unique to population-scale runs where one study is millions of interdependent calls. Cost per simulation and latency per agent decide what research we can afford to run at all, so treat them as research constraints, not ops metrics.

• Scaling simulation Data. Lead the redesign of our data architecture to handle the complexity and sheer volume of our simulation models. You will define the schemas and ingestion logic to unify high-variety input streams (e.g., human survey behavior) and scale our training pipelines to meet the intense demands of society-scale modeling.

• Own the GPU cluster. Keep a multi-node fleet healthy and saturated: node bring-up, topology-aware NCCL and RDMA configuration, scheduling and queue depth, storage lifecycle and checkpoint capacity, autoscaling of serving capacity alongside training jobs, and the alerting that tells us when GPUs are sitting idle. We run across more than one compute provider, so keeping the whole stack portable is part of the job.

• Engineer scientific evaluations. Build evaluation tooling that goes beyond standard benchmarks, with rigorous statistical frameworks that prove the fidelity of our simulations - and that run fast enough to be part of the development loop rather than a gate at the end of it.

• Push the state of the art. Reproduce, critique, and improve upon academic work in simulation, training, and inference optimization. Translate theoretical breakthroughs into production improvements, and document them with academic-level rigor.

REQUIREMENTS

YOU MIGHT THRIVE IN THIS ROLE IF YOU

• Have deep systems and ML proficiency. High proficiency in Python and hands-on experience with modern ML frameworks (e.g., PyTorch, JAX). You can refactor a complex codebase for both performance and architectural integrity.

• Understand modern ML architectures well enough to optimize them. You have an intuition for where the time and memory go and can act on it. You are comfortable around NVIDIA GPUs and the surrounding stack (NCCL, InfiniBand and NVLink topology, CUDA, Triton) or can get there quickly. Writing custom kernels is a bonus here, not the job.

• Have built production ML platforms or MLOps systems. You have shipped the infrastructure other engineers and researchers build on: training orchestration, experiment tooling, model serving, or LLM