Senior/Staff Applied ML Engineer - AI/ML Evaluation & Simulation

We're building the next generation of AI evaluation systems - and we're looking for a

hands-on engineer who can bridge ML, software, and product to make AI systems more

measurable, testable, and trustworthy.

We're part of the AI/ML Evaluation organization, seeking a Senior or Staff-level Applied

ML Engineer with strong software engineering skills and a solid understanding of

machine learning. In this hands-on role, you'll help design and build intelligent systems

that simulate complex interactions (including agentic workflows powered by LLMs),

develop tools for extracting structured insights, and create robust evaluation datasets.

You'll also contribute to building scalable platforms for simulation and behavior analysis.

This role sits at the intersection of ML, engineering, and product - ideal for someone

passionate about bringing clarity and rigor to real-world AI performance.

Description

We're looking for a pragmatic engineer who thrives at the intersection of machine

learning and software development - capable of building robust, scalable systems that

support evaluation and development of advanced AI capabilities, including large

language models and agentic behaviors.

A successful candidate is comfortable navigating ML, systems, and product domains.

You bring strong software engineering fundamentals, experience building and

maintaining end-to-end pipelines, and a practical understanding of how to evaluate AI

systems in real-world contexts. You're curious about how LLMs behave in interactive or

agentic settings, thoughtful about evaluation design, and eager to build tools that

improve visibility and trust in AI. Above all, you enjoy collaborating across disciplines

and bringing structure to complex, evolving problems.","responsibilities":"Design and implement systems that simulate user-like interactions and workflows,

Build tools and infrastructure to generate, manage, and analyze evaluation data

Develop scalable pipelines to extract structured insights from simulation outputs

Collaborate with scientists and engineers to instrument and assess model

performance

Engineer reusable, testable components for experimentation and evaluation workflows

Help define and operationalize success metrics aligned with product and research

goals

Preferred Qualifications

Experience working on AI evaluation systems, LLM-based simulations, or agentic AI

frameworks

Background in building tools for data analysis, model evaluation, or synthetic data

generation

Familiarity with metrics instrumentation and observability in ML systems

Experience designing pipelines for AI/ML workflows

Exposure to applied research, generative models, or real-time systems

Understanding of how model quality connects to product outcomes and user

experience

Minimum Qualifications