Staff Analytics Engineer, AI & Predictive

Qualcomm

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
San Diego, CA
Salary
$142,100–$213,100 / yr
Posted
17 days ago
Freshness
Confirmed live yesterday
Closes
Feb 21, 2027

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $207k
This role $178k
$128k most similar roles pay here $275k

This role pays less than 70% of similar roles. Most pay $167,687–$246,500 — the shaded band above. At the midpoint, this role pays about $178k versus about $207k for comparable roles.

Based on 240 similar postings.

Employer

About Qualcomm

Qualcomm is a leading American semiconductor and telecommunications company based in San Diego, CA.

Qualcomm currently has 623 open roles on FindRole.

Listed pay typically runs $148,300–$222,500 across 603 roles with salary data.

Most-posted roles

View all roles at Qualcomm

At a glance

TL;DR · Staff Analytics Engineer, AI & Predictive

The Staff Analytics Engineer (AI & Predictive) is a senior individual contributor within the Data Science team focused on designing, building, and operationalizing production-grade solutions. This role sits at the intersection of data science, machine learning engineering, and full-stack data application development to drive business outcomes through predictive analytics. The successful candidate will develop traditional machine learning models for regression, classification, and time-series forecasting while building agentic AI workflows and multi-step pipelines for intelligent automation. Key responsibilities include creating Databricks-native applications, including notebooks and interactive dashboards, and managing the full lifecycle of model deployment, monitoring, and drift detection. The role requires expertise in Python, Databricks, Lakehouse architectures, and feature stores to solve complex business problems by integrating ML insights into enterprise data systems and providing technical leadership on architectural standards for scalable, maintainable data products.

What you'll do

  • Design, develop, and deploy traditional machine learning models including regression, classification, and time-series forecasting.
  • Build agentic AI workflows where autonomous agents orchestrate data access, inference, and decision logic.
  • Develop Databricks-native applications, including notebook-based apps, interactive dashboards, and parameterized ML workflows.
  • Operationalize ML models into production pipelines with integrated monitoring, drift detection, and retraining strategies.
  • Build data and ML services using Python and Lakehouse capabilities to provide scalable enterprise solutions.
  • Translate complex business requirements into maintainable, full-stack data products including APIs and user-facing applications.
  • Provide technical leadership by influencing architectural decisions and establishing standards for ML engineering and production readiness.

What we're looking for

  • 5+ years of hands-on experience in data science, applied machine learning, or ML engineering with ownership of production systems.
  • Strong proficiency in Python for machine learning development, data processing, and application logic.
  • Deep experience with traditional machine learning techniques including regression, classification, clustering, and time-series forecasting.
  • Proven experience building and deploying machine learning models in production environments.
  • Hands-on experience with Databricks, specifically in developing notebooks, workflows, dashboards, and ML pipelines.
  • Strong understanding of feature engineering, model evaluation, and explainability.
  • Experience collaborating with data engineering, business intelligence, and application teams.
  • Preferred experience with agentic AI systems, Lakehouse architectures, MLOps practices, and cloud platforms like AWS.

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