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The Voleon Group

Runs machine-learning hedge funds for institutions

Senior Cluster Site Reliability Engineer

Site Reliability EngineeringConfirmed live in the last 24 hours
Full-timeBerkeley, CA, USA$205k - $235k /yrRemote

About the Job

Requirements

  • At least 5 years of experience in SRE or DevOps roles, preferably as a senior engineer or tech lead.
  • Knowledge of HPC or batch compute frameworks such as Slurm, Kueue, AWS Batch, or GCP Batch, and/or machine-learning training systems such as Kubeflow, MLflow, or Horovod.
  • Ability to develop scripts and utilities of moderate complexity in a common scripting language such as Python or Ruby.
  • Familiarity with infrastructure-as-code and configuration-management tools such as Terraform and Ansible.
  • Experience with cloud infrastructure such as AWS or GCP.
  • Familiarity with designing and implementing modern observability stacks such as Prometheus, Grafana, Loki, ELK, or OpenTelemetry.
  • Experience with distributed storage technologies such as Lustre, Ceph, or S3.
  • A systems-engineering mindset focused on systematic problem-solving and automation rather than system administration.
  • A bachelor's degree in computer science.

Responsibilities

  • Respond to cluster outages and issues by triaging and resolving urgent problems.
  • Maintain high cluster uptime and define and track service-level agreements to quantify reliability.
  • Diagnose systemic or recurring problems and engineer precise solutions in collaboration with engineering teams.
  • Develop metrics and observability for cluster health and use them to guide work; build custom observability mechanisms when off-the-shelf tools are insufficient.
  • Help software and research teams design fair cluster-usage policies and develop mechanisms to enforce them.
  • Forecast cluster growth, select appropriate scale-up strategies, and optimize operations for cost and usability.

Desired Qualifications

  • Hands-on experience with HPC frameworks such as Slurm or Grid Engine; Kubernetes-based job orchestrators such as Airflow, Kueue, or Kubeflow Pipelines; and distributed computing frameworks such as Ray, Modin, Dask, or Spark.
  • Familiarity with machine-learning frameworks such as PyTorch, TensorFlow, JAX, Horovod, or DeepSpeed.
  • Familiarity with hybrid and on-premises environments.
  • Experience with containerization tools such as Docker, Podman, or Singularity, particularly in HPC or batch-compute environments.
  • Experience with HPC networking such as InfiniBand or RDMA.
  • Strong security and identity-and-access-management foundations, including identity-management systems, AWS or GCP IAM, or Zero Trust.

About the Company

The Voleon Group

Runs machine-learning hedge funds for institutions

The Voleon Group runs hedge funds for institutions such as pension plans and sovereign wealth funds, using machine-learning models instead of human judgment to choose every trade. It began live trading in 2008 as one of the early hedge funds to hand all of its trading decisions to machine-learning models. Those models comb through years of price and trading data for patterns that predict market moves, then trade automatically in stocks, bonds, futures, currencies and derivatives. Voleon earns management fees plus a share of the gains it makes for investors. A computer scientist and a statistician founded Voleon in 2007, and it is based in Berkeley, California, near the University of California campus.

Company size
201-500
Company stage
Not listed
Total funding
Not listed
Headquarters
Berkeley, CA
Founded
2007

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