abilities, and a collaborative mindset. Preferred Qualifications, Capabilities, and Skills: Master’s degree in Computer Science, Machine Learning, or a related field. Experience with distributed training frameworks such as Ray, MLFlow, or similar. Deep understanding of advanced ML techniques, including search and ranking, recommender systems, and graph-based methods. Expertise in LLM-based approaches, including Agents, Planning, and Reasoning. Familiarity More ❯
abilities, and a collaborative mindset. Preferred Qualifications, Capabilities, and Skills: Master’s degree in Computer Science, Machine Learning, or a related field. Experience with distributed training frameworks such as Ray, MLFlow, or similar. Deep understanding of advanced ML techniques, including search and ranking, recommender systems, and graph-based methods. Expertise in LLM-based approaches, including Agents, Planning, and Reasoning. Familiarity More ❯
completion. Attention to detail, excellent communication skills, and a team-oriented mindset. Additional Qualifications, Capabilities, and Skills Master's degree in Computer Science, ML, or related areas. Experience with Ray, MLFlow, or other distributed training frameworks. Understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies. Deep knowledge of Large Model (LLM) techniques, including Agents, Planning, and More ❯
completion. Attention to detail, excellent communication skills, and a team-oriented mindset. Preferred Qualifications, Capabilities, and Skills Master's degree in Computer Science, ML, or related areas. Experience with Ray, MLFlow, and/or other distributed training frameworks. Understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies. Deep knowledge of LLM techniques, including Agents, Planning, and More ❯
Strong attention to detail, excellent communication skills, and a team-oriented mindset. Preferred qualifications, capabilities, and skills Master's degree in computer science, ML, or related areas. Experience with Ray, MLFlow, and/or other distributed training frameworks. Understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies. Deep knowledge of LLM techniques, including Agents, Planning, Reasoning. More ❯
Docker) Strong knowledge of cloud platforms like Azure, AWS or GCP for deploying and managing ML models Familiarity with data engineering tools and practices, e.g., distributed computing (e.g., Spark, Ray), cloud-based data platforms (e.g., Databricks) and database management (e.g., SQL) Strong communication skills, capability to present technical concepts to technical and non-technical stakeholders Experience in developing AI applications More ❯
Passion for detail and follow through. Excellent communication skills and team player Preferred qualifications, capabilities, and skills Master's degree in computer science, ML or related areas Experience with Ray, MLFlow, and/or other distributed training frameworks. In-depth understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies. Deep understanding of Large Language Model (LLM More ❯
Passion for detail and follow through. Excellent communication skills and team player Preferred qualifications, capabilities, and skills Master's degree in computer science, ML or related areas Experience with Ray, MLFlow, and/or other distributed training frameworks. In-depth understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies. Deep understanding of Large Language Model (LLM More ❯
Passion for detail and follow through. Excellent communication skills and team player Preferred qualifications, capabilities, and skills Master's degree in computer science, ML or related areas Experience with Ray, MLFlow, and/or other distributed training frameworks. In-depth understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies. Deep understanding of Large Language Model (LLM More ❯
Passion for detail and follow through. Excellent communication skills and team player Preferred qualifications, capabilities, and skills Master's degree in computer science, ML or related areas Experience with Ray, MLFlow, and/or other distributed training frameworks. In-depth understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies. Deep understanding of Large Language Model (LLM More ❯
Passion for detail and follow through. Excellent communication skills and team player Preferred Qualifications, Capabilities, And Skills Master’s degree in computer science, ML or related areas Experience with Ray, MLFlow, and/or other distributed training frameworks. In-depth understanding of Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies. Deep understanding of Large Language Model (LLM More ❯
pandas Knowledge of open source datasets and benchmarks in NLP/Computer Vision Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.) Able to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholders. Preferred qualifications, capabilities, and skills Experience designing/implementing … pipelines using DAGs (e.g. Kubeflow, DVC, Ray) Experience of big data technologies (e.g. Spark, Hadoop) Have constructed batch and streaming microservices exposed as REST/gRPC endpoints Familiarity with GraphQL About Us J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals and institutional investors. Our More ❯
pandas Knowledge of open source datasets and benchmarks in NLP/Computer Vision Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.) Able to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholders. Preferred qualifications, capabilities, and skills Experience designing/implementing … pipelines using DAGs (e.g. Kubeflow, DVC, Ray) Experience of big data technologies (e.g. Spark, Hadoop) Have constructed batch and streaming microservices exposed as REST/gRPC endpoints Familiarity with GraphQL About Us J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals and institutional investors. Our More ❯
environments) Hands-on with Docker, Kubernetes, and Terraform Strong scripting skills in Python or Bash Familiar with ML lifecycle tools, model monitoring, and versioning Exposure to tools like KServe, Ray Serve, Triton, or vLLM a big plus Bonus Points: Experience with observability frameworks like Prometheus or OpenTelemetry Knowledge of ML libraries: TensorFlow, PyTorch, HuggingFace Exposure to Azure or GCP Passion More ❯
environments) Hands-on with Docker, Kubernetes, and Terraform Strong scripting skills in Python or Bash Familiar with ML lifecycle tools, model monitoring, and versioning Exposure to tools like KServe, Ray Serve, Triton, or vLLM a big plus Bonus Points: Experience with observability frameworks like Prometheus or OpenTelemetry Knowledge of ML libraries: TensorFlow, PyTorch, HuggingFace Exposure to Azure or GCP Passion More ❯
environments) Hands-on with Docker, Kubernetes, and Terraform Strong scripting skills in Python or Bash Familiar with ML lifecycle tools, model monitoring, and versioning Exposure to tools like KServe, Ray Serve, Triton, or vLLM is a big plus Bonus Points Experience with observability frameworks like Prometheus or OpenTelemetry Knowledge of ML libraries: TensorFlow, PyTorch, HuggingFace Exposure to Azure or GCP More ❯
paced, collaborative, and dynamic environment. Nice to haves: Prior experience with PCB design, EDA tools, or related optimization problems. Hands-on experience in high-performance computing environments (e.g., Kubernetes, Ray, Dask). Contributions to open-source projects, publications, or top placements in ML competitions (e.g., Kaggle). Expertise in related fields such as Computer Vision, Representation Learning, or Simulation Environments. More ❯
London, England, United Kingdom Hybrid / WFH Options
Autodesk
processing skills with varied unstructured data representations Processing unstructured data, such as 3D geometric data Large scale, data-intensive systems in production Distributed computing frameworks, such as Spark, Dask, Ray Data etc. Cloud platforms such as AWS, Azure, or GCP Docker Documenting code, architectures, and experiments Linux systems and bash terminals Preferred Qualifications Databases and/or data warehousing technologies More ❯
role involves collaborating across multiple business units to architect and optimise large-scale, compute-intensive work flows spanning global locations. You will work with cutting-edge platforms such as Ray and YellowDog, driving the integration and support of distributed computing solutions to enhance performance and scalability in complex environments. Key Responsibilities: Partner with business teams to embed distributed computing into … Optimise applications for high performance on distributed platforms. Provide architectural and technical leadership in the design and development of distributed systems. Design, implement, and manage distributed computing solutions using Ray and YellowDog. Required Skills & Experience: Bachelor's degree in Computer Science, Engineering, or a related field. Deep understanding of loosely and tightly coupled workloads. Hands-on experience with HPC platforms … in cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP). Proven experience working with large-scale systems (1000+ nodes, 10,000+ tasks). Advanced expertise in Ray for machine learning pipelines, hyperparameter tuning, and distributed execution. Strong programming skills in Python and experience with Conda. Proficiency with Docker and Kubernetes for containerisation and orchestration. More ❯
role involves collaborating across multiple business units to architect and optimise large-scale, compute-intensive work flows spanning global locations. You will work with cutting-edge platforms such as Ray and YellowDog, driving the integration and support of distributed computing solutions to enhance performance and scalability in complex environments. Key Responsibilities: Partner with business teams to embed distributed computing into … Optimise applications for high performance on distributed platforms. Provide architectural and technical leadership in the design and development of distributed systems. Design, implement, and manage distributed computing solutions using Ray and YellowDog. Required Skills & Experience: Bachelor's degree in Computer Science, Engineering, or a related field. Deep understanding of loosely and tightly coupled workloads. Hands-on experience with HPC platforms … in cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP). Proven experience working with large-scale systems (1000+ nodes, 10,000+ tasks). Advanced expertise in Ray for machine learning pipelines, hyperparameter tuning, and distributed execution. Strong programming skills in Python and experience with Conda. Proficiency with Docker and Kubernetes for containerisation and orchestration. #J-18808-Ljbffr More ❯
Prior commercial experience in designing and implementing systematic trading systems Experience in developing and deploying RESTful APIs and microservices for model serving Familiarity with Big Data technologies such as Ray, Dask, and Spark Experience with data visualization and reporting tools Familiarity with databases and query languages for data extraction and transformation Understanding of and experience with modern software development practices More ❯
for GCP. Expertise in Terraform and infrastructure-as-code practices. Solid experience deploying workloads with Kubernetes, including cluster and node management. Familiarity with ML workload orchestration using Docker, Kubeflow, Ray, or similar tools. Skilled in Python and comfortable working with SQL and data processing tools. Understanding of the machine learning lifecycle from data ingestion to inference. Experience handling large-scale More ❯
City of London, London, United Kingdom Hybrid / WFH Options
Enigma
for GCP. Expertise in Terraform and infrastructure-as-code practices. Solid experience deploying workloads with Kubernetes, including cluster and node management. Familiarity with ML workload orchestration using Docker, Kubeflow, Ray, or similar tools. Skilled in Python and comfortable working with SQL and data processing tools. Understanding of the machine learning lifecycle from data ingestion to inference. Experience handling large-scale More ❯
East London, London, United Kingdom Hybrid / WFH Options
Enigma
for GCP. Expertise in Terraform and infrastructure-as-code practices. Solid experience deploying workloads with Kubernetes, including cluster and node management. Familiarity with ML workload orchestration using Docker, Kubeflow, Ray, or similar tools. Skilled in Python and comfortable working with SQL and data processing tools. Understanding of the machine learning lifecycle from data ingestion to inference. Experience handling large-scale More ❯
Bury, Greater Manchester, United Kingdom Hybrid / WFH Options
Enigma
for GCP. Expertise in Terraform and infrastructure-as-code practices. Solid experience deploying workloads with Kubernetes, including cluster and node management. Familiarity with ML workload orchestration using Docker, Kubeflow, Ray, or similar tools. Skilled in Python and comfortable working with SQL and data processing tools. Understanding of the machine learning lifecycle from data ingestion to inference. Experience handling large-scale More ❯
