Data Scientist 2

Reads like a ML Engineer role: putting models into production.

SF - 2 Folsom

Pay: $206,565–216,565/yearAnnual period assumed. Location-specific pay — pay source
Designs, develops, and maintains production-scale distributed machine learning pipelines using PySpark, Databricks, Delta Lake, MLflow, and Unity Catalog to support e-commerce recommendation systems across multiple brands and markets. Implements advanced machine learning and deep learning models, including neural collaborative filtering and transformer-based sequential recommendation systems, and integrates them into end-to-end production workflows. Owns complete machine learning workflows, including data ingestion, feature engineering, model training, inference, versioning, and automated refresh processes, operating with minimal technical supervision. Develops advanced feature engineering frameworks that combine multi-modal product attributes, customer behavioral signals, and temporal weighting logic to enable large-scale personalization. Collaborates directly with Product Managers and cross-functional partners to translate business requirements into technical system designs, define success metrics, and document implementation constraints. Builds and maintains CI/CD workflows and deployment automation using GitHub Actions and Databricks workflows to support testing, validation, and production releases of machine learning systems. Designs and implements data governance, validation, and monitoring mechanisms to ensure data quality, lineage, reliability, and compliance of production ML assets. Optimizes distributed processing performance and cloud resource utilization to balance model accuracy, processing latency, and infrastructure cost constraints. Telecommuting permissible from any location in the US. Salary Range: $206,565 - $216,565 Employee pay will vary based on factors such as qualifications, experience, skill level, competencies and work location. We will meet minimum wage or minimum of the pay range (whichever is higher) based on city, county and state requirements.What You'll Do 1. Transformer-based deep learning architectures, embedding-based modeling techniques, recommendation systems, personalization systems, collaborative filtering, content-based filtering, matrix factorization, re-ranking techniques, production-scale machine learning systems, MLOps practices, model lifecycle management, ML pipeline automation;
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Work setting: Not stated
Employment: Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting

What you’ll work on

Salary Range: $206,565 - $216,565 Employee pay will vary based on factors such as qualifications, experience, skill level, competencies and work location.

Build distributed ML pipelines in PySpark and Databricks for recommendation systems
Implement neural collaborative filtering and transformer-based sequential recommendation models
Own end-to-end ML workflows including feature engineering, training, inference, and refresh
Build and maintain CI/CD workflows and deployment automation for ML releases
Design data governance, validation, and monitoring for production ML assets

From the employer’s posting

Designs, develops, and maintains production-scale distributed machine learning pipelines using PySpark, Databricks, Delta Lake, MLflow, and Unity Catalog to support e-commerce recommendation systems across multiple brands and markets. Implements advanced machine learning and deep learning models, including neural collaborative filtering and transformer-based sequential recommendation systems, and integrates them into end-to-end production workflows. Owns complete machine learning workflows, including data ingestion, feature engineering, model training, inference, versioning, and automated refresh processes, operating with minimal technical supervision. Develops advanced feature engineering frameworks that combine multi-modal product attributes, customer behavioral signals, and temporal weighting logic to enable large-scale personalization. Collaborates directly with Product Managers and cross-functional partners to translate business requirements into technical system designs, define success metrics, and document implementation constraints. Builds and maintains CI/CD workflows and deployment automation using GitHub Actions and Databricks workflows to support testing, validation, and production releases of machine learning systems. Designs and implements data governance, validation, and monitoring mechanisms to ensure data quality, lineage, reliability, and compliance of production ML assets. Optimizes distributed processing performance and cloud resource utilization to balance model accuracy, processing latency, and infrastructure cost constraints. Telecommuting permissible from any location in the US.

Salary Range: $206,565 - $216,565 Employee pay will vary based on factors such as qualifications, experience, skill level, competencies and work location. We will meet minimum wage or minimum of the pay range (whichever is higher) based on city, county and state requirements.What You'll Do

Designs, develops, and maintains production-scale distributed machine learning pipelines using PySpark, Databricks, Delta Lake, MLflow, and Unity Catalog

Implements advanced machine learning and deep learning models, including neural collaborative filtering and transformer-based sequential recommendation systems

Owns complete machine learning workflows, including data ingestion, feature engineering, model training, inference, versioning, and automated refresh processes

Builds and maintains CI/CD workflows and deployment automation using GitHub Actions and Databricks workflows

Designs and implements data governance, validation, and monitoring mechanisms to ensure data quality, lineage, reliability, and compliance of production ML assets

What you’ll bring

All qualifications

Core experience

Master’s degree in Data Science, Computer Science, Computer Information Systems or related field and 4 years of work experience in the job offered or a related role.

Qualification wording

Master’s degree in Data Science, Computer Science, Computer Information Systems or related field and 4 years of work experience in the job offered or a related role.

Education & alternatives

Who You Are Master’s degree in Data Science, Computer Science, Computer Information Systems or related field and 4 years of work experience in the job offered or a related role.

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Posting record

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Status in our records: Live
First seen by us: Oct 20, 202615 days ago
Recorded sightings: 33
Last seen by us: Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

$166K–$240K is typical

The middle half of what similar US postings state. This one doesn’t list pay.

From 1,279 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 15

Similar jobs usually come down between day 6 and day 69.

From 4,296 similar postings that came down. Coming down is not always a hire.

Gapinc is hiring

10 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring at a steady pace
Takes jobs down at a usual pace

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
SQL
Azure
Databricks
Delta Lake
MLflow
Spark
PySpark

Where the posting names them

5. A/B testing methodologies, KPI analysis, advanced feature engineering, supervised learning, unsupervised learning; and 6. Python, SQL, production-grade code development, cross-functional collaboration. Who You Are

1. Transformer-based deep learning architectures, embedding-based modeling techniques, recommendation systems, personalization systems, collaborative filtering, content-based filtering, matrix factorization, re-ranking techniques, production-scale machine learning systems, MLOps practices, model lifecycle management, ML pipeline automation; 2. Azure Databricks, Apache Spark (PySpark), Delta Lake, and distributed data processing; 3. CI/CD using GitHub Actions;

About the Role Designs, develops, and maintains production-scale distributed machine learning pipelines using PySpark, Databricks, Delta Lake, MLflow, and Unity Catalog to support e-commerce recommendation systems across multiple brands and markets. Implements advanced machine learning and deep learning models, including neural collaborative filtering and transformer-based sequential recommendation systems, and integrates them into end-to-end production workflows. Owns complete machine learning workflows, including data ingestion, feature engineering, model training, inference, versioning, and automated refresh processes, operating with minimal technical supervision. Develops advanced feature engineering frameworks that combine multi-modal product attributes, customer behavioral signals, and temporal weighting logic to enable large-scale personalization. Collaborates directly with Product Managers and cross-functional partners to translate business requirements into technical system designs, define success metrics, and document implementation constraints. Builds and maintains CI/CD workflows and deployment automation using GitHub Actions and Databricks workflows to support testing, validation, and production releases of machine learning systems. Designs and implements data governance, validation, and monitoring mechanisms to ensure data quality, lineage, reliability, and compliance of production ML assets. Optimizes distributed processing performance and cloud resource utilization to balance model accuracy, processing latency, and infrastructure cost constraints. Telecommuting permissible from any location in the US. Salary Range: $206,565 - $216,565 Employee pay will vary based on factors such as qualifications, experience, skill level, competencies and work location. We will meet minimum wage or minimum of the pay range (whichever is higher) based on city, county and state requirements.What You'll Do

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About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025: $140,300
Expected change, 2025 to 2035: 21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

About the Role

Designs, develops, and maintains production-scale distributed machine learning pipelines using PySpark, Databricks, Delta Lake, MLflow, and Unity Catalog to support e-commerce recommendation systems across multiple brands and markets. Implements advanced machine learning and deep learning models, including neural collaborative filtering and transformer-based sequential recommendation systems, and integrates them into end-to-end production workflows. Owns complete machine learning workflows, including data ingestion, feature engineering, model training, inference, versioning, and automated refresh processes, operating with minimal technical supervision. Develops advanced feature engineering frameworks that combine multi-modal product attributes, customer behavioral signals, and temporal weighting logic to enable large-scale personalization. Collaborates directly with Product Managers and cross-functional partners to translate business requirements into technical system designs, define success metrics, and document implementation constraints. Builds and maintains CI/CD workflows and deployment automation using GitHub Actions and Databricks workflows to support testing, validation, and production releases of machine learning systems. Designs and implements data governance, validation, and monitoring mechanisms to ensure data quality, lineage, reliability, and compliance of production ML assets. Optimizes distributed processing performance and cloud resource utilization to balance model accuracy, processing latency, and infrastructure cost constraints. Telecommuting permissible from any location in the US.

Salary Range: $206,565 - $216,565
Employee pay will vary based on factors such as qualifications, experience, skill level, competencies and work location. We will meet minimum wage or minimum of the pay range (whichever is higher) based on city, county and state requirements.

What You'll Do

1. Transformer-based deep learning architectures, embedding-based modeling techniques, recommendation systems, personalization systems, collaborative filtering, content-based filtering, matrix factorization, re-ranking techniques, production-scale machine learning systems, MLOps practices, model lifecycle management, ML pipeline automation;

2. Azure Databricks, Apache Spark (PySpark), Delta Lake, and distributed data processing;

3. CI/CD using GitHub Actions;

4. Cloud-based machine learning platforms, eCommerce analytics, clickstream data analysis, customer behavior analytics, and large-scale data integration and cleansing;

5. A/B testing methodologies, KPI analysis, advanced feature engineering, supervised learning, unsupervised learning; and

6. Python, SQL, production-grade code development, cross-functional collaboration.

Who You Are

Master’s degree in Data Science, Computer Science, Computer Information Systems or related field and 4 years of work experience in the job offered or a related role.

Source details

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