Senior Staff Machine Learning Systems Engineer, Ads ML Platform

4 дн. назад

USALeadRemote

machine learningpythondistributed systemsdata engineeringmlops

Responsible for building infrastructure to accelerate large-scale machine learning systems and tooling for Ads ML platform at .

Условия

Comprehensive Healthcare Benefits and Income Replacement Programs
401k with Employer Match
Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
Family Planning Support
Gender-Affirming Care
Mental Health & Coaching Benefits
Flexible Vacation & Paid Volunteer Time Off
Generous Paid Parental Leave

Другое

has a flexible workforce! If you happen to live close to one of our physical office locations our doors are open for you to come into the office as often as you'd like. Don't live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence
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The Ads ML Platform team builds infrastructure that accelerates high-scale ML systems and tooling for Ads ML, while extending reusable capabilities to broader ML use cases where appropriate. Our systems help ML engineers move faster across the full development lifecycle: creating features, generating training data, running offline experiments, validating model quality, launching production models, and operating ML systems reliably.
We are looking for a Senior Staff Machine Learning Systems Engineer to lead the technical strategy for the end-to-end Ads ML engineer lifecycle. The initial focus will be on the feature development and training iteration loop: making it faster and easier for ML engineers to build features, generate reliable training data, run experiments, and move from idea to validated model improvement. Over time, this scope will expand into serving and online experimentation workflows, creating a more seamless path from offline iteration to production impact.
This is a senior technical leadership role for someone who can combine deep systems expertise, production ML experience, architectural judgment, and cross-team influence.
Own the technical strategy for the end-to-end Ads ML engineer lifecycle, starting with feature development, training data, offline experimentation, and model iteration workflows.
Align Ads ML platform priorities with ’s broader ML Platform vision, translating Ads pain points into reusable platform capabilities where appropriate.
Define architecture and technical standards for ML feature and training-data systems across batch/streaming computation, backfills, lineage, quality, observability, and online/offline consistency.
Stay close to ML engineers and platform customers to identify high-leverage friction points and improve day-to-day development velocity.
Build platform abstractions and workflow automation that make ML development faster, safer, more reliable, and more self-service.
Over time, extend the platform strategy into serving and online experimentation workflows, creating a more seamless offline-to-online ML development experience.
Partner across Ads, ML Platform, Data Platform, modeling, product, and engineering teams to clarify ownership, resolve ambiguity, and drive durable execution.
Mentor Staff and senior engineers, raise the architecture and operational bar, and help grow the next generation of technical leaders.
You have 8+ years of experience in infrastructure, distributed systems, ML platforms, data platforms, or large-scale backend systems.
You have 4+ years building or operating production ML infrastructure, feature platforms, training data systems, experimentation systems, or large-scale data pipelines.
You have led broad, ambiguous, multi-team platform initiatives from strategy through adoption.
You have built platforms used directly by ML engineers, data scientists, or product teams developing production ML systems.
You have deep experience in ML platform, feature platform, training data, experimentation, developer infrastructure, or distributed data infrastructure.
You have worked with distributed data and compute systems such as Spark, Flink, Kafka, Ray, Airflow, Iceberg, Kubernetes, BigQuery, Snowflake, Databricks, or similar technologies.
You can balance urgent customer needs with durable long-term architecture and reusable platform patterns.
You influence senior engineers and leaders through clear technical reasoning, RFCs, design reviews, decision frameworks, and operating mechanisms.
You are excited to shape how production ML systems are built, scaled, and operated, not only how models are trained.
This job posting may span more than one career level.
In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit.
To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.
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is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.