6 months ago
Base Salary
$217k - $303k/yr
Responsibilities
- Design end-to-end MLOps lifecycle patterns covering data preparation, model management, and experiment tracking.
- Develop and support a graph machine learning codebase and platform that abstracts common patterns and improves model scalability and iteration.
- Collaborate with ML engineers to tune performance, reduce training time, improve efficiency, and lower GPU costs in a distributed training environment.
- Optimize batch data processing in a data warehouse using Apache Beam, Apache Spark, Ray Data, and related tools.
- Architect pipelines that build and maintain graph data structures containing billions of nodes and tens of billions of edges.
Requirements
- 5+ years of experience in ML infrastructure, including model training and deployment.
- Hands-on experience with ML optimization, memory profiling, and GPU profiling.
- Deep experience with cloud-based technologies supporting ML platforms, including BigQuery, Google Cloud Storage, and Terraform.
- Hands-on experience administering and integrating MLOps tools for experiment tracking, model serving, and model registries, such as MLflow or Wandb.
- Proficiency with Python, PyTorch, TensorFlow, and common machine learning frameworks.
- Deep experience with distributed training frameworks, including Ray and Kubernetes.
- Strong focus on scalability, reliability, performance, and platform usability, along with strong organizational and communication skills.
- Experience with graph databases such as Neo4j, JanusGraph, or TigerGraph is preferred.
- Experience with graph neural networks and graph ML frameworks such as PyTorch Geometric or Deep Graph Library is preferred.
Benefits
- U.S.-based employees may receive medical, dental, and vision insurance, a 401(k) program with employer match, vacation time, and parental leave.
- Interviews may be recorded, transcribed, and summarized by AI, with an option to opt out before the interview.
- The role is eligible for equity in the form of restricted stock units and may be eligible for commission depending on the position offered.
Tech Stack
Apache BeamApache SparkGoogle BigQueryGoogle CloudGoogle Cloud PlatformMLflowNeo4jPythonPyTorchTensorFlowTerraform
Categories
About Reddit
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