Machine Learning Engineer - Applied / Product

7Seventy; United States; Remote; USD 180,000 - 275,000; Full time

Benefits offered by this job

Health, dental, and vision insurance

401(k) retirement plan with company匹配

Unlimited paid time off

Generous parental leave policy

Equity participation with potential金融起

Job summary

7Seventy is seeking a Machine Learning Engineer - Applied / Product to build scalable ML-powered applications using behavioral foundation models. The role focuses on developing ML models, metrics, pipelines, systems, and services that support production applications, personal experiences, and data-driven decisions.

Ground-up product development is a significant part of this role. This position blends hands-on ML engineering with product development and experimentation.

Qualifications

• Hands-on experience with consumer-facing ML applications or problems (e.g., recommender systems or personalization).
• Ability to explain technical contributions and their impact.
• Experience linking ML engineering workflows across models, data pipelines, infrastructure, and application development.

Responsibilities

• Develop and improve ML models, metrics, pipelines, systems, and services powering ML-driven apps.
• Build ML-powered products for personalization and decision-making.
• Contribute to end-to-end ML application development and product implementation.
• Develop new capabilities and systems from the ground up with ownership of substantial engineering work.
• Collaborate with Product teams and ML engineers to translate signals into practical applications.

Tools

Airflow

Bazel

GitHub CI/CD

Spark

Job description

Remote (United States)

Location: United States

Workplace: Remote

Employment Type: Full-time

Core Areas: Applied Machine Learning, Recommender Systems, Personalization, ML Pipelines, Data Orchestration, Experiment Tracking, ML-Powered Product Development, Model Infrastructure

Compensation: $180,000-$275,000 annually

About the Role

The Machine Learning Engineer - Applied / Product position focuses on building and improving scalable, machine learning-powered applications using foundation models of human behavior. The role involves developing ML models, metrics, pipelines, systems, and services that support production applications, personalized experiences, and data-driven decision-making. A significant part of the work involves developing new products and technical capabilities from the ground up.

This position combines hands-on machine learning engineering with product development and experimentation. The engineer will work closely with Product teams and core machine learning engineers to translate behavioral signals and model capabilities into practical applications. The technical environment includes data orchestration, build systems, experiment tracking, and large-scale data processing, with tools such as Airflow, Bazel, GitHub CI/CD, and Spark.

What You'll Do

• Develop and improve the machine learning models, metrics, pipelines, systems, and services that power ML-driven applications.
• Build scalable machine learning-powered products that apply behavioral foundation models to personalization and decision-making.
• Contribute to end-to-end ML application development, including data processing, pipeline infrastructure, experimentation, and product implementation.
• Develop new application capabilities and technical systems from the ground up, taking ownership of substantial engineering work.
• Work with data orchestration, build systems, and experiment tracking infrastructure to support machine learning development workflows.
• Interact with technologies such as Airflow, Bazel, GitHub CI/CD, and Spark across the machine learning pipeline.
• Collaborate with Product teams and core machine learning engineers to incorporate behavioral signals and model capabilities into applications.
• Evaluate product and engineering opportunities, contributing technical recommendations on implementation priorities and product direction.
• Support ongoing experimentation and product improvement through machine learning metrics, application performance insights, and feedback from deployed products.
• Contribute to the development of new ML-powered product opportunities, balancing technical implementation with product-market fit considerations.
• Help build applications that generate useful data for the continued improvement of core model embeddings.

Qualifications

Required Experience

• Hands-on experience contributing directly to impactful, consumer-facing machine learning applications or problems, such as recommender systems or personalization.
• Practical experience working with machine learning solutions and the ability to explain technical contributions and their impact.
• Experience contributing to machine learning engineering workflows that connect models, data pipelines, infrastructure, and application development.

Required Skills

• Practical understanding of machine learning concepts and their application to real-world, consumer-facing products.
• Ability to contribute across the machine learning pipeline, including data orchestration, build systems, and experiment tracking.
• Comfort working hands-on with engineering infrastructure and technical systems that support ML application development.
• Understanding of how machine learning models, metrics, pipelines, and services contribute to the functionality and performance of ML-powered products.
• Ability to make informed technical implementation decisions while considering product objectives and product-market fit.
• Strong product judgment and the ability to evaluate which technical capabilities and product improvements should be prioritized.
• Ability to collaborate with Product teams and machine learning engineers on integrated ML-powered applications.
• Ability to contribute to new product development in an environment where engineering responsibilities span multiple technical areas.

Technical Environment

• Data orchestration and workflow management using technologies such as Airflow.
• Build systems and engineering workflows using tools such as Bazel.
• Continuous integration and delivery workflows using GitHub CI/CD.
• Machine learning experimentation, model-related metrics, and application pipelines.

Prior experience with these specific tools is not required, but candidates must be comfortable working with comparable engineering systems and learning the technologies used in the environment. Academic publication experience in machine learning is not required.

• Health, dental, and vision insurance.
• 401(k) retirement plan with company matching.
• Unlimited paid time off.
• Generous parental leave policy.
• Equity participation with potential financial upside.
• Annual performance-based bonus opportunity.
• Fully remote working arrangement, with hybrid options available from hubs in the San Francisco Bay Area, Seattle, and New York City.

Recruitment notice: all communication only happens from authorized email addresses ending in @7seventy.net. We never ask for payments or sensitive documents. Suspect a scam? Email [e-mail]