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Staff Machine Learning Engineer

Rol remoto de Machine Learning Platform Engineering con fit claro de ubicación del candidato.

Publicado12 jul 2026

Países elegibles1 país aceptado

Señal de senioritySenior

Modelo de trabajoRemoto

Ubicaciones aceptadas para candidatos

Estados Unidos

Resumen del rol

Contenido del rol extraído en secciones para revisar más rápido.

Own technical direction of the ML Platform — feature store, model hosting and serving, experimentation, training infrastructure — driving architectural decisions around scalability, reliability, latency, and cost
Lead design and delivery of large-scope platform initiatives from conception through production, coordinating across ML, data, and infrastructure teams
Identify and resolve systemic technical challenges: online/offline feature parity, model deployment friction, experimentation velocity, GPU utilization, cross-team dependencies
Set and maintain a high engineering quality bar through hands-on code contributions, design reviews, and mentorship of platform and ML-adjacent engineers
Partner with ML engineering, data science, product, and platform leadership to translate ML strategy into technical roadmaps
Define the paved paths ML teams use to ship models safely — from feature registration through canary rollout, monitoring, and rollback
Leverage AI-augmented development tools to increase development velocity and code quality

8+ years delivering complex backend or infrastructure systems at scale
Direct experience building or operating core ML infrastructure — feature stores, model serving, experimentation platforms, training orchestration, or equivalent
Mastery of a modern backend language such as Python, Java, Kotlin, Go, or Scala
Deep proficiency with distributed systems concepts: consistency, latency, throughput, fault tolerance, and observability
Strong understanding of data modeling, query languages, and the online/offline data patterns that underpin ML systems
Demonstrated technical leadership, with ability to drive cross-team alignment and influence engineering, product, and business stakeholders
Bachelor's degree in Computer Science or a related field, or equivalent practical experience

Hands-on experience with open-source or commercial ML platform components (e.g. Tecton, MLflow, SageMaker, Databricks)
Experience building or operating experimentation / A-B testing platforms at scale
Familiarity with real-time streaming systems (Kafka, Flink, Spark Streaming) and their use in feature computation
Experience serving LLMs or large deep-learning models in production, including GPU capacity planning and inference optimization
Comfort with Kubernetes and modern cloud-native infrastructure
Prior work supporting internal-developer-facing platforms with a product mindset

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