Machine Learning Engineer - Applied​/Product

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

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, Git Hub CI/CD, and Spark.

• 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, Git Hub 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.

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 Git Hub CI/CD.
• Machine learning experimentation,…