Senior Staff Machine Learning Systems Engineer, Ads ML Platform

Reddit · Remote - United StatesRemotosenior

En corto

▸Liderazgo técnico en infraestructura de ML a gran escala para anuncios en Reddit.
▸Enfocado en acelerar el ciclo de desarrollo de modelos: features, datos de entrenamiento y experimentos offline.
▸Rol clave para hacer que el ML sea más rápido, seguro y autónomo para ingenieros de ML.

Fluency in English is required for collaboration across global teams.

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

¿Qué piden?

✓Experiencia demostrable en sistemas de ML a gran escala en entornos de producción.
✓Conocimiento profundo de pipelines de features, entrenamiento de modelos y experimentación offline.
✓Capacidad para definir arquitecturas y estándares técnicos con impacto en múltiples equipos.
✓Habilidades de liderazgo técnico y influencia sin autoridad sobre equipos distribuidos.
✓Experiencia trabajando con sistemas batch y streaming para datos de ML.
✓Compromiso con la calidad, observabilidad y consistencia entre entornos offline y online.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonJavaScalaSQLKubernetesAirflowSparkFlinkGCPMLflow

¿A quién escribirle en Reddit?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Reddit 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 About Reddit Reddit is a community of communities, built on shared interests, passion, and trust. With 100,000+ active communities and 101M+ daily active unique visitors, we’re one of the largest sources of conversation and knowledge on the internet. For more information, visit redditinc.com . Team Overview The Ads ML Platform team builds infrastructure that accelerates high-scale ML systems and tooling for Ads ML, while extending reusable capabilities to broader Reddit 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. What You’ll Do • 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 Reddit’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

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