[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"seo-content-skill-distributed-training-global":3,"job-search-\u002Fde-global\u002Fjobs-by-skill\u002Fdistributed-training":4,"skill-page-filters-\u002Fde-global\u002Fjobs-by-skill\u002Fdistributed-training":149,"featured-jobs-\u002Fde-global\u002Fjobs-by-skill\u002Fdistributed-training":280},{},{"total":5,"jobs":6},2,[7,64],{"id":8,"slug":9,"title":10,"raw_title":11,"is_featured":12,"company":13,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":16,"processed_country_iso_code_gmaps":18,"processed_home_office":20,"processed_salary_min":21,"processed_salary_max":22,"processed_salary_currency":23,"processed_salary_source":24,"processed_working_hours":25,"processed_employment_types":27,"processed_it_skills":28,"processed_it_skills_labels":31,"processed_job_expertise_skills":49,"processed_job_expertise_skills_labels":50,"max_cpc":63,"actual_cpc":63},69486,"senior-data-scientist-foundation-model-ai-factory-bcobilbao-vizcaya-argentaria","Senior Data Scientist - Foundation Model - AI Factory",null,false,{"name":14,"slug":15,"logo_url":11},"BCO.BILBAO VIZCAYA ARGENTARIA","bcobilbao-vizcaya-argentaria",[17],"Bilbao",[19],"ES","hybrid",70000,120000,"EUR","estimated",[26],"FULL_TIME","permanent",{"lora":29,"python":30,"pytorch":30,"checkpointing":29,"experiment tracking":29,"memory optimization":29,"sequential modeling":30,"distributed training":30,"performance profiling":29,"optimization algorithms":30,"representation learning":30,"mixed precision training":29,"transformer architectures":30,"graph neural networks (gnn)":29,"software engineering practices":30,"parameter-efficient fine-tuning":29,"large language model (llm) pre-training":30},3,4,[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"Python","PyTorch","Distributed Training","Transformer Architectures","Parameter-Efficient Fine-Tuning","LoRA","Experiment Tracking","Mixed Precision Training","Checkpointing","Memory Optimization","Performance Profiling","Graph Neural Networks (GNN)","Large Language Model (LLM) Pre-training","Sequential Modeling","Representation Learning","Optimization Algorithms","Software Engineering Practices",{"deep learning":30,"responsible ai":30,"risk management":29,"data preparation":30,"machine learning":30,"model evaluation":30,"scalable ai systems":30,"production deployment":30,"regulatory compliance":29,"financial data analysis":29,"research and development":30,"model architecture design":30},[51,52,53,54,55,56,57,58,59,60,61,62],"Machine Learning","Deep Learning","Model Architecture Design","Data Preparation","Model Evaluation","Production Deployment","Financial Data Analysis","Risk Management","Regulatory Compliance","Research and Development","Scalable AI Systems","Responsible AI",1,{"id":65,"slug":66,"title":67,"raw_title":11,"is_featured":12,"company":68,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":71,"processed_country_iso_code_gmaps":73,"processed_home_office":75,"processed_salary_min":22,"processed_salary_max":76,"processed_salary_currency":77,"processed_salary_source":24,"processed_working_hours":78,"processed_employment_types":27,"processed_it_skills":79,"processed_it_skills_labels":80,"processed_job_expertise_skills":97,"processed_job_expertise_skills_labels":98,"max_cpc":63,"actual_cpc":148},59582,"principal-machine-learning-engineer-gbr006-refinitiv-limited","Principal Machine Learning Engineer",{"name":69,"slug":70,"logo_url":11},"Refinitiv Limited","refinitiv-limited",[72],"London",[74],"GB","onsite",180000,"GBP",[26],{"iam":30,"shap":30,"ci\u002Fcd":30,"mlops":30,"python":30,"pytorch":30,"xgboost":30,"telemetry":30,"tensorflow":30,"aws sagemaker":30,"observability":30,"data pipelines":30,"explainability":30,"feature stores":29,"drift detection":30,"gpu optimization":29,"model governance":30,"distributed training":29,"infrastructure as code":30},[32,81,33,82,83,84,85,86,87,88,89,90,91,92,93,94,95,34,96],"AWS SageMaker","TensorFlow","XGBoost","SHAP","CI\u002FCD","Infrastructure as Code","MLOps","Model Governance","Explainability","Drift Detection","Observability","Telemetry","IAM","Data Pipelines","Feature Stores","GPU Optimization",{"slos":30,"encryption":30,"versioning":30,"a\u002Fb testing":30,"ml security":30,"model cards":30,"auditability":30,"pii handling":30,"reason codes":30,"traceability":30,"scoring logic":30,"ranking models":29,"stem education":29,"canary rollouts":30,"entity matching":29,"golden datasets":30,"model hot paths":30,"reproducibility":30,"benchmark suites":30,"model validation":30,"privacy policies":30,"search relevance":29,"behavioural tests":30,"cost optimization":30,"cross-account iam":30,"data minimisation":30,"model calibration":30,"proactive failover":30,"scale optimization":30,"security standards":30,"shadow deployments":30,"compliance policies":30,"feature engineering":30,"shadow-mode testing":30,"telemetry pipelines":30,"similarity modelling":29,"confidence thresholds":30,"continuous validation":30,"regulatory compliance":30,"feature store adoption":29,"data quality guardrails":30,"optimization strategies":30,"performance engineering":30,"compliance documentation":30,"reliability optimization":30,"data minimisation policies":30,"model lifecycle management":30,"latency-sensitive inference":30,"machine learning architecture":30,"multi-account aws ml platforms":29},[99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,59,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147],"Machine Learning Architecture","Model Lifecycle Management","Model Validation","Performance Engineering","Security Standards","Compliance Documentation","Model Cards","A\u002FB Testing","Shadow Deployments","Canary Rollouts","Ranking Models","Search Relevance","Entity Matching","Similarity Modelling","Feature Engineering","Data Quality Guardrails","Model Calibration","Scoring Logic","Confidence Thresholds","Optimization Strategies","Reason Codes","Data Minimisation","PII Handling","Encryption","Auditability","Reproducibility","Versioning","Traceability","Golden Datasets","Behavioural Tests","Benchmark Suites","Latency-Sensitive Inference","Model Hot Paths","Cost Optimization","Scale Optimization","Reliability Optimization","SLOs","Telemetry Pipelines","Privacy Policies","Data Minimisation Policies","Compliance Policies","Proactive Failover","Shadow-Mode Testing","Continuous Validation","ML Security","Cross-Account IAM","Multi-Account AWS ML Platforms","Feature Store Adoption","STEM Education",0.5,{"industry":150,"employment_type":160,"expertise_skills":163,"it_skills":218,"salary_currency":275,"processed_working_hours":278},[151,154,157],{"key":152,"label":153,"count":63},"financial markets infrastructure","Financial Markets Infrastructure",{"key":155,"label":156,"count":63},"data","Data",{"key":158,"label":159,"count":63},"financial services","Financial Services",[161],{"key":27,"label":162,"count":5},"PERMANENT",[164,166,168,170,172,175,177,179,182,185,187,189,191,193,196,198,200,202,205,207,210,212,214,216],{"key":165,"label":59,"slug":165,"count":5},"regulatory-compliance",{"key":167,"label":120,"slug":167,"count":63},"data-minimisation",{"key":169,"label":128,"slug":169,"count":63},"behavioural-tests",{"key":171,"label":112,"slug":171,"count":63},"similarity-modelling",{"key":173,"label":174,"slug":173,"count":63},"cross-account-iam","Cross-Account Iam",{"key":176,"label":146,"slug":176,"count":63},"feature-store-adoption",{"key":178,"label":119,"slug":178,"count":63},"reason-codes",{"key":180,"label":181,"slug":180,"count":63},"responsible-ai","Responsible Ai",{"key":183,"label":184,"slug":183,"count":63},"research-and-development","Research And Development",{"key":186,"label":113,"slug":186,"count":63},"feature-engineering",{"key":188,"label":117,"slug":188,"count":63},"confidence-thresholds",{"key":190,"label":107,"slug":190,"count":63},"shadow-deployments",{"key":192,"label":99,"slug":192,"count":63},"machine-learning-architecture",{"key":194,"label":195,"slug":194,"count":63},"scalable-ai-systems","Scalable Ai Systems",{"key":197,"label":123,"slug":197,"count":63},"auditability",{"key":199,"label":134,"slug":199,"count":63},"reliability-optimization",{"key":201,"label":116,"slug":201,"count":63},"scoring-logic",{"key":203,"label":204,"slug":203,"count":63},"stem-education","Stem Education",{"key":206,"label":127,"slug":206,"count":63},"golden-datasets",{"key":208,"label":209,"slug":208,"count":63},"pii-handling","Pii Handling",{"key":211,"label":141,"slug":211,"count":63},"shadow-mode-testing",{"key":213,"label":142,"slug":213,"count":63},"continuous-validation",{"key":215,"label":136,"slug":215,"count":63},"telemetry-pipelines",{"key":217,"label":58,"slug":217,"count":63},"risk-management",[219,222,224,226,229,231,233,235,237,240,242,244,247,250,253,255,257,259,262,264,266,268,270,273],{"key":220,"label":221,"slug":220,"count":5},"pytorch","Pytorch",{"key":223,"label":34,"slug":223,"count":5},"distributed-training",{"key":225,"label":32,"slug":225,"count":5},"python",{"key":227,"label":228,"slug":227,"count":63},"infrastructure-as-code","Infrastructure As Code",{"key":230,"label":46,"slug":230,"count":63},"representation-learning",{"key":232,"label":40,"slug":232,"count":63},"checkpointing",{"key":234,"label":48,"slug":234,"count":63},"software-engineering-practices",{"key":236,"label":88,"slug":236,"count":63},"model-governance",{"key":238,"label":239,"slug":238,"count":63},"mlops","Mlops",{"key":241,"label":91,"slug":241,"count":63},"observability",{"key":243,"label":45,"slug":243,"count":63},"sequential-modeling",{"key":245,"label":246,"slug":245,"count":63},"large-language-model-llm-pre-training","Large Language Model (Llm) Pre-Training",{"key":248,"label":249,"slug":248,"count":63},"aws-sagemaker","Aws Sagemaker",{"key":251,"label":252,"slug":251,"count":63},"xgboost","Xgboost",{"key":254,"label":89,"slug":254,"count":63},"explainability",{"key":256,"label":35,"slug":256,"count":63},"transformer-architectures",{"key":258,"label":90,"slug":258,"count":63},"drift-detection",{"key":260,"label":261,"slug":260,"count":63},"tensorflow","Tensorflow",{"key":263,"label":36,"slug":263,"count":63},"parameter-efficient-fine-tuning",{"key":265,"label":47,"slug":265,"count":63},"optimization-algorithms",{"key":267,"label":92,"slug":267,"count":63},"telemetry",{"key":269,"label":42,"slug":269,"count":63},"performance-profiling",{"key":271,"label":272,"slug":271,"count":63},"graph-neural-networks-gnn","Graph Neural Networks (Gnn)",{"key":274,"label":41,"slug":274,"count":63},"memory-optimization",[276,277],{"key":23,"label":23,"count":63},{"key":77,"label":77,"count":63},[279],{"key":26,"label":26,"count":5},{"total":63,"jobs":281},[282],{"id":283,"slug":284,"title":285,"raw_title":11,"is_featured":286,"company":287,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":291,"processed_country_iso_code_gmaps":293,"processed_home_office":295,"processed_salary_min":296,"processed_salary_max":297,"processed_salary_currency":23,"processed_salary_source":11,"processed_working_hours":298,"processed_employment_types":162,"processed_it_skills":300,"processed_it_skills_labels":11,"processed_job_expertise_skills":301,"processed_job_expertise_skills_labels":302,"max_cpc":308,"actual_cpc":11},52265,"privatkundenbetreuer-mwd-banking-frankfurt-am-main-white-horse-international-gmbh","Privatkundenbetreuer (m\u002Fw\u002Fd) – Banking | Frankfurt am Main",true,{"name":288,"slug":289,"logo_url":290},"White Horse International GmbH","white-horse-international-gmbh","https:\u002F\u002Fwhitehorse-international.com\u002Fwp-content\u002Fuploads\u002F2022\u002F03\u002Fwhite-horse-logo-final-gif.gif",[292],"Frankfurt am Main",[294],"DE","HYBRID",45000,65000,[299],"full_time",{},{"Financial Analysis":29,"Regulatory Compliance":29,"New Customer Acquisition":29,"Private Banking Advisory":29,"Banking Products Knowledge":29,"Customer Relationship Management":29},[303,304,305,59,306,307],"Private Banking Advisory","Financial Analysis","Banking Products Knowledge","Customer Relationship Management","New Customer Acquisition",0.8]