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Staff Machine Learning Engineer
Vaga remota de Machine Learning Platform Engineering com fit claro de localização do candidato.
Publicada12 de jul. de 2026
Países elegíveis1 país aceito
Sinal de senioridadeSenior
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Estados Unidos
Resumo da vaga
Staff Machine Learning Engineer
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
A day in the life (Responsibilities)
- 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
What you'll need to thrive (Requirements):
- 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
Nice to Haves:
- 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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