Senior Staff Machine Learning Engineer, Ads Ranking

2d•$292,500 - $409,500•Remote

About The Position

We are seeking a Senior Staff Machine Learning Engineer to define and accelerate how we leverage agentic AI to transform machine learning models and modeling workflows. This high-impact role focuses directly on modeling leverage—enhancing model quality and velocity—rather than building general infrastructure. As a senior staff engineer, you will operate as a technical leader across the entire modeling loop, utilizing modern AI-assisted development systems (such as Claude, Cursor, and comparable tools) to make model development faster, more rigorous, and significantly more effective across the organization. This role offers a domain-flexible platform to redefine how ML engineering is done. If you are passionate about combining deep ML fundamentals with the vanguard of agentic AI to multiply team impact, we’d love to talk to you.

Requirements

9+ years of experience in ML engineering, applied ML, or a related field, with substantial production impact.
Senior Staff-level technical leadership and a record of influencing direction across teams.
Deep modeling expertise and strong fundamentals in data, objectives, architectures, optimization, evaluation, and experimentation.
Concrete examples of using agentic AI or AI coding tools to produce better ML outcomes—not just faster code generation.
Ability to design guardrails and evaluations that make autonomous or semi-autonomous workflows trustworthy.
Strong coding ability in Python and fluency with modern ML frameworks and data tooling.
Clear, concise communication and a collaborative, low-ego approach to technical leadership.

Responsibilities

Develop advanced ML models and the agentic workflows that help teams design, train, evaluate, and improve them.
Use tools such as Claude, Cursor, and comparable AI-assisted development systems to investigate model behavior, generate and test hypotheses, automate repetitive analysis, and improve code and experiments.
Build reliable loops for data and label analysis, feature and embedding development, architecture comparison, hyperparameter optimization, error analysis, and experiment readouts.
Design evaluation harnesses that measure both model outcomes and agent/workflow quality, including correctness, reproducibility, regressions, cost, and latency.
Partner with product, data science, ML infrastructure, and engineering teams to connect modeling improvements to user and business outcomes.
Set technical direction across teams, mentor engineers, and raise the bar for modeling rigor and AI-assisted engineering.