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Machine Learning Systems Engineer, Ads ML Platform
Vaga remota de Ads Engineering com fit claro de localização do candidato.
PublicadaAdicionada recentemente
Países elegíveis1 país aceito
Sinal de senioridadeNível aberto
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Reino Unido
Resumo da vaga
Machine Learning Systems Engineer, Ads ML Platform
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
Details
- Design and build data infrastructure that supports large-scale feature and training set computation, transformation, and storage.
- Develop frameworks for batch and real-time features with a focus on reliability, scalability, and ease of use.
- Build platform capabilities for feature governance, including lineage tracking, validation, drift detection, anomaly monitoring, reproducibility, and versioning
- Partner with ML engineers to ensure smooth integration of feature engineering workflows into ML production systems.
- Build systems that support agentic ML workflows, including automated feature discovery, feature quality evaluation and feature lifecycle management
- Contribute to operational excellence through observability, performance tuning, reliability engineering, and cost optimization initiatives.
- 3+ years in data infrastructure/platform engineering or ML infrastructure platforms.
- Hands-on experience building production services, data pipelines, APIs, workflow systems, or developer tools.
- Experience with at least one distributed data or compute system such as Spark, PySpark, Flink, Kafka, Ray, Airflow, Kubernetes, BigQuery, or similar technologies.
- Familiarity with ML data workflows such as feature generation, training dataset creation, batch processing, real-time data processing, model training, experimentation, or online serving.
- Strong coding skills and ability to write clean, maintainable, well-tested code.
- Experience building intelligent automation or agentic workflows for ML systems is a strong plus
- Experience with ML infrastructure and MLOps workflows spanning feature engineering, training pipelines, experimentation, model deployment, and online serving is a plus
- 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
- Group Personal Pension Scheme with Employer match
- Private Medical and Dental Scheme
- Income Replacement Programs
- Bike to Work scheme
- Flexible Vacation & Paid Volunteer Time Off
- Generous Paid Parental Leave
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