Senior Machine Learning Engineer, Forecasting

7Seventy; United States; Hybrid; USD 175,000 - 225,000; Full time

Benefits offered by this job

Stock options

Flexible vacation

Remote-first environment

Health, dental, and vision insurance

Parental leave (16 weeks)

Professional growth opportunities

Job summary

7Seventy is seeking a Senior Machine Learning Engineer, Forecasting, to own production ML lifecycle, including training automation, deployment pipelines, and model serving infrastructure. You will work on distributed ML systems, MLOps, and forecasting workflows in a remote, United States-based team.

You’ll collaborate with engineers, product, and data science, improve experimentation velocity, implement automated retraining, and strengthen reliability.

Qualifications

• 5+ years building and maintaining production ML systems, with deep expertise in MLOps, deployment automation, and model serving infrastructure.
• Strong software engineering skills and proficiency in Python.
• Hands-on experience with containerization and orchestration technologies, including Docker and Kubernetes.
• Experience with CI/CD systems such as GitHub Actions and ArgoCD.
• Experience with infrastructure-as-code and deployment tooling such as Terraform and Helm.
• Production ML deployment experience, including model training orchestration with Dagster, Airflow, or similar tools.
• Experience building automated model retraining pipelines and working with A/B testing and variant management.
• Systems design expertise, including scalable microservices, API design, and management of complex service dependencies.
• Demonstrated ownership of projects from concept through production, including ongoing maintenance and continuous improvements to system reliability.
• Ability to work effectively with data scientists to productionize research, backend engineering teams on API integration, and product teams to address customer needs.
• Commitment to writing tested, maintainable, and well-documented code that supports team velocity.

Responsibilities

• Collaborate with engineering, product, and data science teams to understand business challenges and identify opportunities for machine learning and AI solutions.
• Develop tools and automate manual processes to improve operational efficiency, accelerate experimentation, and reduce human error.
• Build, integrate, and monitor end-to-end lifecycles for large-scale, distributed machine learning systems.
• Investigate model performance and identify data-quality and system-performance issues.
• Improve the ML pipeline supporting the forecasting platform, including weekly automated model retraining and deployment across multiple production models.
• Advance MLOps best practices, deployment automation, and production ML capabilities across the team.

Skills

Python

Docker

Kubernetes

CI/CD

GitHub Actions

ArgoCD

Terraform

Helm

Dagster

Airflow

A/B testing

API design

Microservices

Tools

Docker

Kubernetes

GitHub Actions

ArgoCD

Terraform

Helm

Dagster

Airflow

Job description

Senior Machine Learning Engineer, Forecasting

Remote (United States)

Location: United States

Workplace: Remote

Employment Type: Full time

Experience: 5+ years building and maintaining production ML systems

Compensation: $175,000 – $225,000 per year, plus equity

About the Role

This opportunity is for a Senior Machine Learning Engineer, Forecasting focused on production machine learning systems, MLOps infrastructure, and forecasting workflows. The position owns critical parts of the production ML lifecycle, including training automation, deployment pipelines, model serving infrastructure, monitoring, and scalable systems that enable reliable model development and iteration.

The role combines Python software engineering, distributed ML systems, Docker and Kubernetes, CI/CD, infrastructure-as-code, model orchestration, and automated retraining. It works closely with engineering, product, and data science teams to productionize machine learning research, improve experimentation velocity, investigate model and data-quality issues, and strengthen the reliability and maintainability of production forecasting systems.

What You'll Do

• Collaborate with engineering, product, and data science teams to understand business challenges and identify opportunities for machine learning and AI solutions.
• Develop tools and automate manual processes to improve operational efficiency, accelerate experimentation, and reduce human error.
• Build, integrate, and monitor end-to-end lifecycles for large-scale, distributed machine learning systems.
• Investigate model performance and identify data-quality and system-performance issues.
• Improve the ML pipeline supporting the forecasting platform, including weekly automated model retraining and deployment across multiple production models.
• Advance MLOps best practices, deployment automation, and production ML capabilities across the team.

Qualifications

• 5+ years of experience building and maintaining production ML systems, with deep expertise in MLOps, deployment automation, and model serving infrastructure.
• Strong software engineering skills and proficiency in Python.
• Hands-on experience with containerization and orchestration technologies, including Docker and Kubernetes.
• Experience with CI/CD systems such as GitHub Actions and ArgoCD.
• Experience with infrastructure-as-code and deployment tooling such as Terraform and Helm.
• Production ML deployment experience, including model training orchestration with Dagster, Airflow, or similar tools.
• Experience building automated model retraining pipelines and working with A/B testing and variant management.
• Systems design expertise, including scalable microservices, API design, and management of complex service dependencies.
• Demonstrated ownership of projects from concept through production, including ongoing maintenance and continuous improvements to system reliability.
• Ability to work effectively with data scientists to productionize research, backend engineering teams on API integration, and product teams to address customer needs.
• Commitment to writing tested, maintainable, and well-documented code that supports team velocity.

Preferred Qualifications

• Experience in healthcare or another regulated industry.
• Experience with forecasting or time series algorithms.
• Experience with computer vision.
• Experience with DAG frameworks or Flink.

Interview Process

• Initial conversation with a recruiter to discuss background and the role.
• Interview with the hiring manager to review relevant experience and complete a role-related case or technical exercise.
• Virtual onsite interviews with several team members covering collaboration, culture, machine learning engineering coding skills, and system design. This stage typically consists of three to five interviews.
• Stock options.
• Flexible vacation policy.
• Remote-first work environment with virtual and in-person events designed to support team connection.
• Comprehensive health, dental, and vision insurance.
• 16 weeks of parental leave for all parents.
• Professional growth opportunities.