Machine Learning Engineer

7Seventy; United States; Remote; USD 170,000 - 220,000; Full time

Benefits offered by this job

Health, dental, vision insurance forUS

MacBook Pro provided

Home office stipend

Meal stipend

Social meetup stipend

Health and wellness stipend

Learning and development stipend

401(k) matching 4%

Job summary

7Seventy is hiring a Machine Learning Engineer to build and operate production ML infrastructure for real-time fraud detection. You will own model serving systems, ensure sub-250ms latency, and manage feature pipelines and deployment workflows.

The role blends ML platform engineering, MLOps, and hands-on model development using Python, Go, Kubernetes, containers, and GCP. Security, observability, and compliance are emphasized.

Qualifications

• Hands-on experience designing and building model serving infrastructure.
• Experience owning production ML systems with incident response and reliability focus.
• Experience building self-service tooling for engineers and scientists.

Responsibilities

• Design, build, and maintain model serving infra for real-time ML inference and fraud detection.
• Develop low-latency inference systems and feature retrieval within sub-250ms budgets.
• Create deployment workflows enabling independent model deployment and rollbacks.

Skills

Python

Kubernetes

Go

Cloud (GCP)

Infrastructure as Code

CI/CD

Testing

Tools

Docker

Terraform

Git

Job description

Experience: Hands-on experience building model serving infrastructure and managing production ML systems; specific years of experience not stated.

Core Areas: Machine Learning Infrastructure, Real-Time Model Inference, Model Serving, MLOps, Feature Engineering, Python, Kubernetes, GCP

Compensation: Estimated base salary of $175,000–$220,000 annually. Equity offered.

About the Role

This Machine Learning Engineer position focuses on building, scaling, and operating production machine learning infrastructure for real-time fraud detection. The role owns model serving systems, low-latency inference, feature retrieval, and the infrastructure that enables data scientists to deploy custom ML models. These systems process millions of sessions using hundreds of device and behavioral signals, with inference operating within a sub-250ms latency budget.

The position combines ML platform engineering, MLOps, and hands-on model development. Responsibilities span model deployment, production monitoring, drift detection, retraining, incident response, and feature pipelines. The technical environment includes Python, Go, Kubernetes, containers, GCP, and infrastructure-as-code, with an emphasis on reliability, observability, security, and compliance in fraud prevention and financial risk systems.

What You'll Do

• Design, build, and maintain model serving infrastructure that supports real-time machine learning inference and production fraud detection.
• Develop and optimize low-latency inference systems and feature retrieval services to operate within a sub-250ms request processing budget.
• Build and maintain model deployment workflows that enable data scientists to independently deploy, update, and roll back machine learning models.
• Support bring-your-own-model (BYOM) capabilities for clients deploying their own machine learning models.
• Own the operational lifecycle of production ML systems, including model monitoring, drift detection, retraining, troubleshooting, and incident response.
• Participate in the on-call rotation and investigate production failures, model performance degradation, and infrastructure issues.
• Build and optimize feature engineering pipelines that transform raw device and behavioral signals into production-ready machine learning features.
• Develop and maintain Python-based ML infrastructure and integrate inference capabilities with Go backend services to support fast request processing.
• Build and improve internal tooling that enables engineers and data scientists to manage and deploy models efficiently through self-service workflows.
• Develop machine learning models directly when appropriate, representing approximately 20% of the role, with opportunities for additional model development.
• Improve the reliability and maintainability of ML infrastructure through automated testing, observability, code quality, and engineering best practices.
• Promote security, compliance, and production readiness across machine learning systems operating in a regulated environment.

Qualifications

Required Experience

• Hands-on experience designing and building model serving infrastructure rather than solely deploying or using existing ML platforms.
• Experience owning production machine learning systems, including responding to incidents, diagnosing failures, implementing fixes, and preventing recurring issues.
• Experience developing tools and infrastructure used by software engineers or data scientists, including evaluating appropriate self-service capabilities.
• Practical experience supporting the deployment, operation, and reliability of machine learning workloads in production environments.

Required Skills

• Strong Python programming skills and solid software engineering fundamentals, including automated testing, code reviews, and CI/CD practices.
• Proficiency with Kubernetes, containerized applications, and a major cloud platform, with GCP used as the primary cloud environment.
• Working knowledge of infrastructure-as-code for managing and maintaining reliable production infrastructure.
• Understanding of machine learning models sufficient to investigate performance degradation and distinguish between data quality issues, feature pipeline failures, and model-related problems.
• Ability to build, troubleshoot, and maintain reliable model serving and inference infrastructure used by other technical teams.
• Sound engineering judgment when designing developer tooling and determining which infrastructure capabilities should support self-service workflows.

Preferred Qualifications

• Domain knowledge of fraud detection, risk management, or cybersecurity.
• Additional familiarity with CI/CD, Docker, Kubernetes, and modern DevOps frameworks.
• Familiarity with using frontier large language models (LLMs) for automation.
• Competitive base compensation and equity opportunities, including early exercise of stock options before vesting.
• Flexible paid time off and a year-end break.
• Health, dental, and vision insurance for employees and dependents in the United States and Canada.
• 4% matching for eligible 401(k) or RRSP retirement plans.
• MacBook Pro provided for work.
• One-time home office setup stipend for equipment such as a desk, chair, and monitor.
• Monthly meal stipend.
• Monthly social meetup stipend.
• Annual health and wellness stipend.
• Annual learning and professional development stipend.