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 ❯
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 ❯
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 ❯
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 ❯
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 ❯
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 ❯
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 ❯
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 ❯
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 ❯
Leigh, Greater Manchester, UK 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 ❯
Bolton, Greater Manchester, UK 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 ❯
Altrincham, Greater Manchester, UK 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 ❯
City of London, Greater London, UK 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 ❯
Ashton-Under-Lyne, Greater Manchester, UK 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 ❯
data modeling, architecture, and processing unstructured data. Experience with processing 3D geometric data. Experience with large-scale, data-intensive systems in production. Knowledge of distributed computing frameworks (Spark, Dask, Ray). Experience with cloud platforms (AWS, Azure, GCP). Proficiency with Docker, Linux, and bash. Ability to document code, architectures, and experiments. Preferred Qualifications Experience with databases and data warehousing More ❯
user base. As a team, we are a collaborative, cross-functional group with backgrounds in information retrieval, natural language processing, and distributed systems. We work with Go microservices, Python, Ray Serve, Kubernetes/KubeRay, and work on AWS, GCP & Azure. We provide thought leadership across a variety of mediums including open code repositories, publishing blogs, and speaking at conferences. We … Bring 5+ years working in an MLOps or related ML Engineering role Production experience self-hosting & operating LLMs at scale for generative tasks via an inference framework such as Ray or KServe (or similar) Production experience with running and tuning specialized hardware for Generative AI workloads, especially GPUs via CUDA Measured and articulate written and spoken communication skills. You work More ❯
Our Mission Our mission is to restore cell health and resilience through cell rejuvenation to reverse disease, injury, and the disabilities that can occur throughout life. For more information, see our website at Our Value Our Single Altos Value: Everyone More ❯
