Production ML Engineer — Onsite, Equity & Learning Budget

Qubeaxis; San Francisco (CA); On-site; USD 130,000 - 180,000; Full time

Benefits offered by this job

Competitive salary guidance

Performance bonus up to 20%

Equity options

Health insurance for employee + depend

Learning & development budget

Cloud compute access for ML work

Flexible hours, 25 days leave

Job summary

Qubeaxis is seeking a Machine Learning Engineer to design, train, deploy, and optimize ML models that power predictions, personalization, automation, and intelligent product experiences at scale.

You will work across the full ML lifecycle—from data prep and feature engineering to model validation, deployment, monitoring, and retraining—collaborating with data scientists, backend engineers, and product teams to turn data into measurable product impact.

Qualifications

• Proven Python production experience with clean architecture and production-ready code.
• Strong understanding of supervised and unsupervised learning, feature engineering, cross-validation, metrics, and model selection.
• Proficient in data handling with pandas, NumPy, SQL, and working with structured and unstructured data.
• Experience with ML frameworks such as scikit-learn and at least one deep learning framework (PyTorch or TensorFlow).
• Ability to deploy models via APIs or batch pipelines using FastAPI, Flask, Docker, or similar tools.
• Knowledge of MLOps practices including model versioning, experiment tracking, CI/CD, monitoring, and retraining workflows.
• Familiarity with Git, version control, code reviews, and documentation practices.

Responsibilities

• Design, train, deploy, and optimize ML models that solve business problems.
• Handle data preparation, feature engineering, validation, and monitoring across the ML lifecycle.
• Collaborate with data scientists, backend engineers, and product teams to translate data into product impact.
• Maintain production-grade pipelines and improve model reliability through retraining and monitoring processes.

Skills

Python programming

ML fundamentals

Data handling

ML frameworks

Model deployment

MLOps basics

Git and collaboration

Education

B.Tech / B.S. / M.S. in Computer Science, Data Science, Statistics, Mathematics, or related field

Tools

scikit-learn

PyTorch/TensorFlow

Docker

FastAPI/Flask

Git

Job description

Qubeaxis is seeking a Machine Learning Engineer to design, train, deploy, and optimize ML models that power predictions, personalization, automation, and intelligent product experiences at scale.

You will work across the full ML lifecycle—from data prep and feature engineering to model validation, deployment, monitoring, and retraining—collaborating with data scientists, backend engineers, and product teams to turn data into measurable product impact.