Machine Learning Engineer - Core Signals

Remote (United States)

Job Details

Location: United States

Workplace: Remote

Employment Type: Full-Time

Core Areas: Machine Learning, Representation Learning, Behavioral Modeling, Embeddings, CTR Modeling, Sequence Modeling, ML Model Training, ML Infrastructure

Compensation: $180,000–$275,000 annually (base salary), plus equity and performance-based bonus eligibility.

About the Role

The Machine Learning Engineer - Core Signals position focuses on developing representation learning approaches for large-scale user behavioral modeling and applying core machine learning models to new and existing products. The role covers the full machine learning lifecycle, including data processing, model training, deployment, and evaluation, with an emphasis on translating research and development advances into production applications.

This position combines applied machine learning research, model engineering, and ML infrastructure development. Key technical areas include representation learning, embedding drift, click-through rate (CTR) modeling, and sequence modeling. The engineer collaborates closely with Product and Applications teams, contributes to data and model pipelines, and supports product development decisions in a growing engineering environment.

What You'll Do

• Develop and improve representation learning approaches for fundamental user behavioral modeling problems.
• Apply core machine learning models to support the development of new products and enhance existing applications.
• Manage machine learning workflows across data processing, model training, deployment, and evaluation.
• Investigate novel machine learning problems and identify opportunities to improve existing modeling approaches or introduce new methodologies.
• Work with large-scale behavioral datasets and machine learning techniques relevant to embeddings, CTR prediction, and sequence modeling.
• Collaborate with Product and Applications teams to translate machine learning research and development improvements into practical product capabilities.
• Contribute to the non-research components of machine learning pipelines, including data orchestration, build systems, and experiment tracking.
• Work with engineering infrastructure and development tools such as Apache Airflow, Bazel, GitHub CI/CD, and Apache Spark.
• Participate in product and technical implementation decisions, balancing modeling improvements with product requirements and product-market fit considerations.
• Adapt to related engineering and product responsibilities as needed in a growing startup environment.

Qualifications

Required Experience

• Practical machine learning experience sufficient to apply ML techniques to novel problems, improve existing modeling approaches, and propose alternative solutions.
• Hands-on experience with at least some of the following areas: representation learning, embedding drift, CTR modeling, or sequence modeling.
• Ability to contribute to non-research engineering components of machine learning pipelines, including data orchestration, build systems, and experiment tracking.

Required Skills

• Strong understanding of machine learning concepts and the ability to apply them to new modeling challenges.
• Ability to evaluate existing machine learning approaches and recommend incremental improvements or substantially different techniques.
• Working knowledge of machine learning engineering workflows and supporting data and model infrastructure.
• Comfort working directly with data orchestration, build automation, continuous integration and deployment, and experiment tracking systems.
• Product-oriented judgment, including the ability to assess implementation choices and contribute to product-market fit decisions.
• Ability to collaborate across machine learning, Product, and Applications functions while taking ownership of technical work.
• Flexibility to contribute across research-oriented machine learning tasks and broader engineering responsibilities.

Preferred Qualifications

• Experience applying representation learning, embedding techniques, CTR modeling, or sequence modeling to large-scale datasets in an industrial environment.

Prior experience with Airflow, Bazel, GitHub CI/CD, or Spark is not specifically required. Previous research publication experience is not required.

Benefits

• Meaningful equity participation with potential financial upside.
• Annual bonus target based on individual and company performance.
• Health, dental, and vision insurance options.
• Unlimited paid time off.
• 401(k) retirement plan with company matching.
• Generous parental leave benefits.