ML Systems Engineer: Offline Platform & Large-Scale Pipelines

Socket.dev; Bellevue (CA); On-site; USD 104,000 - 135,000; Full time

Benefits offered by this job

Health insurance

Stock ownership

Retirement plans

Generous vacation and personal days

Parental leave

Commuter subsidy

Employee resource groups

Employee assistance programs

Job summary

Unity Vector is building an offline ML platform that powers insight, experimentation, attribution, and AI-driven decision-making across the company. We seek a Machine Learning Engineer to join the Offline Infrastructure team and help scale data pipelines and distributed training workflows.

You will collaborate with experienced engineers and researchers to translate research ideas into reliable, scalable systems for training data generation and ML pipelines.

Qualifications

• Bachelor's degree required in a related field.
• Strong background in machine learning systems and distributed data processing.
• Experience with Python and data-intensive workloads.
• Familiarity with ML frameworks such as PyTorch or TensorFlow and distributed systems like Ray or Spark.
• Experience with data pipelines and large datasets.

Responsibilities

• Build and maintain data pipelines that generate training datasets for ML models and experiments.
• Contribute to infrastructure supporting distributed training workflows (e.g., PyTorch, Ray).
• Work with workflow orchestration tools (Airflow, Flyte) to support multi-stage ML pipelines.
• Improve reproducibility and reliability via dataset validation, monitoring, and testing.
• Partner with ML engineers to support experimentation and model iteration.
• Help optimize performance across data processing and training systems.
• Contribute to evolution of offline ML platform architecture as it scales.

Skills

Python

PyTorch

TensorFlow

Ray

Spark

Data pipelines

Model training

Distributed systems

Education

Bachelor's degree in CS/ML/Systems

Job description

Unity Vector is building an offline ML platform that powers insight, experimentation, attribution, and AI-driven decision-making across the company. We seek a Machine Learning Engineer to join the Offline Infrastructure team and help scale data pipelines and distributed training workflows.

You will collaborate with experienced engineers and researchers to translate research ideas into reliable, scalable systems for training data generation and ML pipelines.