Senior ML Engineer: Architect Production ML at Scale

Jupiteraiinc; San Francisco (CA); Hybrid; USD 180,000 - 260,000; Full time

Benefits offered by this job

Health, dental, vision coverage

401(k) with company match

Remote & Hybrid options

Learning budget for certifications

Parental leave

Home office stipend

Mentorship culture

Job summary

Jupiter AI is seeking a senior ML engineer to design and deploy production ML systems at enterprise scale. You will own the full lifecycle from experimentation to production monitoring and work at the core of our AI engineering practice.

You will design reproducible training and deployment workflows, implement drift detection and automated retraining, and collaborate with data engineers on feature stores and inference APIs to guide model choices and explainability.

Qualifications

• Proficient in Python with ML libraries and modeling workflows.
• Experience deploying production ML systems and pipelines.
• Strong software engineering and testing practices.

Responsibilities

• Design production ML pipelines with reproducible training and deployment workflows.
• Implement model monitoring, drift detection, and automated retraining strategies.
• Collaborate with data engineers and product teams on feature stores and inference APIs.
• Guide technical decisions on model selection, evaluation, and explainability.
• Document architectures and contribute to Jupiter AI engineering standards.

Skills

Python

ML Frameworks (PyTorch or TensorFlow)

scikit-learn

CI/CD for ML

Education

MS/PhD in CS/ML or related field

Tools

MLflow

Kubeflow

Docker

Kubernetes

Job description

Jupiter AI is seeking a senior ML engineer to design and deploy production ML systems at enterprise scale. You will own the full lifecycle from experimentation to production monitoring and work at the core of our AI engineering practice.

You will design reproducible training and deployment workflows, implement drift detection and automated retraining, and collaborate with data engineers on feature stores and inference APIs to guide model choices and explainability.