ML Systems - Member of Technical Staff

The posting, as Simile wrote it

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, stre

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desire to do whatever it takes for the team to succeed.

• Have a strong quantitative foundation - typically a degree in Computer Science, Mathematics, Statistics, or a related field, though we care about demonstrated ability far more than credentials.

NICE TO HAVES

• Experience optimizing inference for multi-agent or agentic environments, where requests are interdependent rather than independent.
• Experience with distributed training at scale.
• Experience building and shipping production AI agents, and familiarity with LLM serving frameworks (vLLM, SGLang, TensorRT-LLM) and inference-time optimization.
• Interdisciplinary background in social science modeling or behavioral economics.

COMPENSATION & BENEFITS

At Simile, we provide competitive compensation packages that include base salary, equity, and comprehensive benefits.

• Salary Range: $200,000 – $400,000 USD
• Note: Final offers are based on experience, specialized skills, interview performance, and relevant training.
• Equity: Grants are available for eligible roles, subject to board approval.
• Health & Wellness: Comprehensive medical, dental, and vision coverage.
• Time Off: Flexible time off policies to support work-life balance.

OUR PROCESS

We prioritize thoughtful conversations and clear examples of past work. Our hiring journey is designed to help both sides align on fit, working style, and expectations.

Reapplication Policy: To ensure a fair and thorough evaluation for all applicants, Simile observes a 90-day waiting period before reconsidering candidates for the same role.

COMMITMENT TO DIVERSITY & INCLUSION

Equal Opportunity: Simile is an equal opportunity workplace. We welcome applicants of all backgrounds and identities, valuing an environment where everyone can contribute authentically.

Accommodations: If you require support or reasonable accommodations during the application process due to a disability, please let us know. We are happy to assist.