Full Stack Engineer (Machine Learning)

OSI Engineering

Cupertino (CA)

On-site

USD 110,000 - 150,000

Full time

14 days+

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Job summary

A dynamic technology company in Cupertino, CA is seeking a Full Stack Engineer with strong backend and front end skills. You will support script generation, analyze metrics, and assist with data visualization for internal web portals. The role requires 5+ years of experience, strong database skills with SQL and MongoDB, and hands-on experience with PyTorch for developing ML workflows. Join a passionate team that values innovation and creativity.

Qualifications

  • 5+ years of experience in software development.
  • Strong database and backend skills with SQL, PostgreSQL, and MongoDB.
  • Experience with ML workflow and data preparation.

Responsibilities

  • Support script generation and data views.
  • Plot and analyze metrics.
  • Assist with data visualization on internal web portals.
  • Create user-friendly front-end interfaces.
  • Build end-to-end ML pipelines with large data sets.

Skills

Backend and front end skills
Data visualization
Script generation
Metrics analysis

Tools

SQL
PostgreSQL
MongoDB
PyTorch

Job description

We are seeking a talented and passionate Full Stack Engineer to join our dynamic and innovative team in Cupertino, CA. The ideal candidate will have strong backend and front end skills combined with being able to work with raw, unstructured data and build ML workflows from scratch.

Responsibilities:
  • Support script generation and creating data views.
  • Plot and analyze metrics.
  • Assist with data visualization for internal (enterprise) web portals.
  • Create user-friendly front-end interfaces.
  • Review and gather large data sets and build end-to-end ML pipelines.
Requirements:
  • 5+ years experience.
  • Strong database and backend skills with SQL, PostgreSQL, MongoDB, and scalable backend systems.
  • Hands‑on experience with PyTorch (for LLMs), data preparation, and creating ML pipelines.
  • Experience with gathering and organizing large data sets, including vector databases.
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