Sphere Digital Recruitment is hiring: Senior Machine Learning Engineer in New Yo
Sphere Digital Recruitment, New York, NY, United States
Overview
I have partnered with a leading AI-driven advertising technology company to hire a Senior Machine Learning Engineer who will play a pivotal role in building and scaling distributed machine learning infrastructure that powers real-time programmatic decisioning. This role sits at the intersection of proprietary advertiser data, predictive analytics, and large-scale ML systems, supporting a platform that deploys custom bidding algorithms across major DSPs and walled gardens.
This is a highly technical, impact-driven engineering role, not a research-only or analytics position. You will own the end-to-end lifecycle of production ML systems, from distributed training and hyperparameter tuning to batch inference and observability. Working closely with engineering, product, and data science leadership, you will advance core AI products by translating sophisticated models into scalable, reliable systems that directly influence product strategy and business outcomes in the programmatic advertising ecosystem.
Senior Machine Learning Engineer - Responsibilities
Design and deploy scalable, distributed ML systems for audience modeling and bid optimization, leveraging PyTorch and Ray on Databricks to support multi-GPU training, hyperparameter tuning, and champion/challenger model evaluation.
Own end-to-end ML pipelines from feature engineering and model training to batch inference, ensuring automation, reproducibility, fault tolerance, and reliable checkpoint recovery in production environments.
Build and operate robust MLOps and observability frameworks, implementing model versioning, experiment tracking, drift detection, and performance monitoring (AUC, AUPRC, F1) using MLflow and modern monitoring stacks.
Collaborate cross-functionally with product, data science, and engineering leadership to align ML development with core product strategy and business KPIs across multiple AI-driven advertising products.
Provide technical leadership and architectural guidance, driving best practices in distributed ML infrastructure, contributing to shared codebases, documenting systems, and enabling teams to extend and scale the ML platform.
Senior Machine Learning Engineer - Requirements
Advanced academic background (MS or PhD) in Computer Science, Statistics, Machine Learning, or a related field, with 5-10 years of industry experience building and deploying production ML systems at scale.
Deep expertise in PyTorch and distributed ML training, including custom neural network architectures (embeddings, MLPs, classification heads), multi-GPU workflows, and batch inference pipelines with robust artifact and schema management.
Strong production experience on Databricks, leveraging Delta Lake, Unity Catalog, and cluster management to support scalable training, feature engineering, and governed ML lifecycles.
Proven MLOps and software engineering rigor, encompassing MLflow-based experiment tracking and model versioning, CI/CD workflows, monitoring and observability, and reproducible ML systems built with Python and PySpark.
Cloud-native and collaborative engineering mindset, with hands-on experience across AWS and modern data platforms, and the ability to partner effectively with Product, Data Science, and Engineering teams to deliver business-critical ML solutions.
Sphere is an equal opportunities employer. We encourage applications regardless of ethnic origin, race, religious beliefs, age, disability, gender or sexual orientation, and any other protected status as required by applicable law.
If you require any adjustments or additional support during the recruitment process for any reason whatsoever, please let us know.
#J-18808-Ljbffr
In Summary: Senior Machine Learning Engineer will play a pivotal role in building and scaling distributed machine learning infrastructure that powers real-time programmatic decisioning . This role sits at the intersection of proprietary advertiser data, predictive analytics, and large-scale ML systems .
En Español: Junto con una empresa líder en tecnología de publicidad impulsada por IA para contratar a un Ingeniero Superior de Aprendizaje Automático que desempeñará un papel fundamental en la construcción y ampliación de infraestructura distribuida de aprendizaje automático que impulsa la toma de decisiones programáticas en tiempo real. Este rol se sitúa en el cruce de los datos propietarios del anunciante, análisis predictivo y sistemas ML a gran escala, apoyando una plataforma que despliega algoritmos custom bidding en grandes DSPs y jardines amurados. Trabajando estrechamente con el liderazgo de ingeniería, producto y ciencias de los datos, avanzará en productos básicos de IA mediante la traducción de modelos sofisticados/evaluación del modelo desafiante a sistemas escalables y fiables que influyen directamente en la estrategia del producto y los resultados comerciales en el ecosistema publicitario programático. Ingeniero Senior Machine Learning - Responsabilidades Diseñar e implementar sistemas ML distribuidos escalables para modelado de audiencia y optimización de ofertas, aprovechando PyTorch y Ray on Databricks para apoyar el entrenamiento multi-GPU, ajuste hiperparámetro, y evaluación de modelos / modelos campeones. Alentamos las solicitudes independientemente de su origen étnico, raza, creencias religiosas, edad, discapacidad, género o orientación sexual y cualquier otro estatus protegido según lo exija la ley aplicable. Si necesita algún ajuste o apoyo adicional durante el proceso de reclutamiento por alguna razón, háganoslo saber. #J-18808-Ljbffr
I have partnered with a leading AI-driven advertising technology company to hire a Senior Machine Learning Engineer who will play a pivotal role in building and scaling distributed machine learning infrastructure that powers real-time programmatic decisioning. This role sits at the intersection of proprietary advertiser data, predictive analytics, and large-scale ML systems, supporting a platform that deploys custom bidding algorithms across major DSPs and walled gardens.
This is a highly technical, impact-driven engineering role, not a research-only or analytics position. You will own the end-to-end lifecycle of production ML systems, from distributed training and hyperparameter tuning to batch inference and observability. Working closely with engineering, product, and data science leadership, you will advance core AI products by translating sophisticated models into scalable, reliable systems that directly influence product strategy and business outcomes in the programmatic advertising ecosystem.
Senior Machine Learning Engineer - Responsibilities
Design and deploy scalable, distributed ML systems for audience modeling and bid optimization, leveraging PyTorch and Ray on Databricks to support multi-GPU training, hyperparameter tuning, and champion/challenger model evaluation.
Own end-to-end ML pipelines from feature engineering and model training to batch inference, ensuring automation, reproducibility, fault tolerance, and reliable checkpoint recovery in production environments.
Build and operate robust MLOps and observability frameworks, implementing model versioning, experiment tracking, drift detection, and performance monitoring (AUC, AUPRC, F1) using MLflow and modern monitoring stacks.
Collaborate cross-functionally with product, data science, and engineering leadership to align ML development with core product strategy and business KPIs across multiple AI-driven advertising products.
Provide technical leadership and architectural guidance, driving best practices in distributed ML infrastructure, contributing to shared codebases, documenting systems, and enabling teams to extend and scale the ML platform.
Senior Machine Learning Engineer - Requirements
Advanced academic background (MS or PhD) in Computer Science, Statistics, Machine Learning, or a related field, with 5-10 years of industry experience building and deploying production ML systems at scale.
Deep expertise in PyTorch and distributed ML training, including custom neural network architectures (embeddings, MLPs, classification heads), multi-GPU workflows, and batch inference pipelines with robust artifact and schema management.
Strong production experience on Databricks, leveraging Delta Lake, Unity Catalog, and cluster management to support scalable training, feature engineering, and governed ML lifecycles.
Proven MLOps and software engineering rigor, encompassing MLflow-based experiment tracking and model versioning, CI/CD workflows, monitoring and observability, and reproducible ML systems built with Python and PySpark.
Cloud-native and collaborative engineering mindset, with hands-on experience across AWS and modern data platforms, and the ability to partner effectively with Product, Data Science, and Engineering teams to deliver business-critical ML solutions.
Sphere is an equal opportunities employer. We encourage applications regardless of ethnic origin, race, religious beliefs, age, disability, gender or sexual orientation, and any other protected status as required by applicable law.
If you require any adjustments or additional support during the recruitment process for any reason whatsoever, please let us know.
#J-18808-Ljbffr
In Summary: Senior Machine Learning Engineer will play a pivotal role in building and scaling distributed machine learning infrastructure that powers real-time programmatic decisioning . This role sits at the intersection of proprietary advertiser data, predictive analytics, and large-scale ML systems .
En Español: Junto con una empresa líder en tecnología de publicidad impulsada por IA para contratar a un Ingeniero Superior de Aprendizaje Automático que desempeñará un papel fundamental en la construcción y ampliación de infraestructura distribuida de aprendizaje automático que impulsa la toma de decisiones programáticas en tiempo real. Este rol se sitúa en el cruce de los datos propietarios del anunciante, análisis predictivo y sistemas ML a gran escala, apoyando una plataforma que despliega algoritmos custom bidding en grandes DSPs y jardines amurados. Trabajando estrechamente con el liderazgo de ingeniería, producto y ciencias de los datos, avanzará en productos básicos de IA mediante la traducción de modelos sofisticados/evaluación del modelo desafiante a sistemas escalables y fiables que influyen directamente en la estrategia del producto y los resultados comerciales en el ecosistema publicitario programático. Ingeniero Senior Machine Learning - Responsabilidades Diseñar e implementar sistemas ML distribuidos escalables para modelado de audiencia y optimización de ofertas, aprovechando PyTorch y Ray on Databricks para apoyar el entrenamiento multi-GPU, ajuste hiperparámetro, y evaluación de modelos / modelos campeones. Alentamos las solicitudes independientemente de su origen étnico, raza, creencias religiosas, edad, discapacidad, género o orientación sexual y cualquier otro estatus protegido según lo exija la ley aplicable. Si necesita algún ajuste o apoyo adicional durante el proceso de reclutamiento por alguna razón, háganoslo saber. #J-18808-Ljbffr