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 ML development, data processing, and application logic.
Deep experience with traditional ML techniques (e.g., regression, classification, clustering, time series).
Proven experience building and deploying ML models in production environments.
Hands‑on experience with Databricks, including Databricks application development (notebooks, workflows, dashboards, ML pipelines).
Strong understanding of feature engineering, model evaluation, and explainability.
Experience collaborating with data engineering, BI, and application teams.
Full description
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Company:
Qualcomm Incorporated
Job Area:
Information Technology Group, Information Technology Group > Data Science
General Summary:
The StaffAnalytics Engineer (AI & Predictive) is a senior, hands‑on individual contributor responsible for designing, building, and operationalizing predictive analytics, traditional machine learning models, agentic AI systems, and Databricks‑native data applications that drive real business outcomes. This role operates at the intersection of data science, ML engineering, and full‑stack data application development, with a strong focus on production‑grade solutions.
This position requires deep expertise in classical ML techniques, agent‑based AI workflows, and Databricks application development, along with strong ownership of end‑to‑end delivery—from data preparation and modeling to deployment, monitoring, and user‑facing experiences.
This role requires full-time onsite work in San Diego, CA (5 days per week).This position is not eligible for Qualcomm immigration sponsorship.
Design, develop, and deploy traditional machine learning models, including regression, classification, clustering, time‑series forecasting, and anomaly detection.
Perform feature engineering, model selection, training, validation, and performance tuning on large‑scale enterprise datasets.
Apply sound statistical and ML best practices to ensure model robustness, explainability, and business relevance.
Agentic AI & Intelligent Automation
Design and implement agentic AI workflows, where autonomous or semi‑autonomous agents orchestrate data access, ML inference, decision logic, and actions.
Build multi‑step agent pipelines that combine rules, ML models, and reasoning components to solve complex business problems.
Integrate agentic systems with enterprise data, ML models, and applications to enable intelligent automation and decision support.
Databricks Application Development
Design and develop Databricks‑native applications, including notebook‑based apps, interactive dashboards, and parameterized data/ML workflows.
Build data and ML services/APIs leveraging Databricks, Python, and Lakehouse capabilities.
Staff Analytics Engineer (AI & Predictive) at Qualcomm (San Diego, California, United States of America) — aijobsearch
Partner with analytics, BI, and application teams to embed ML insights, predictions, and agent outputs directly into Databricks apps and business workflows.
Ensure Databricks apps meet performance, security, governance, and usability standards.
ML Engineering & Productionization
Operationalize ML models and agentic workflows into production pipelines, ensuring scalability, reliability, and monitoring.
Collaborate with data engineering teams to leverage curated Lakehouse data, feature stores, and governed datasets.
Implement model monitoring, drift detection, and retraining strategies to maintain long‑term model effectiveness.
Full‑Stack Data Enablement
Develop end‑to‑end solutions that span data ingestion, modeling, ML inference, agent execution, and user‑facing applications.
Translate business and analytical requirements into scalable, maintainable ML‑powered data products.
Enable downstream consumption through Databricks apps, dashboards, APIs, and integrated enterprise applications.
Production Support & Operational Excellence
Own production ML models, agentic systems, and Databricks applications, including monitoring, troubleshooting, and root‑cause analysis.
Implement logging, alerting, and observability for models, agents, and applications.
Drive continuous improvements in model accuracy, system reliability, and user experience.
Technical Leadership & Influence
Serve as a technical authority in traditional ML, agentic AI, and Databricks application patterns.
Influence architectural decisions, best practices, and technical standards across teams.
Mentor peers and raise the bar on ML rigor, engineering quality, and production readiness.
QualificationsRequired Skills & Experience
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 ML development, data processing, and application logic.
Deep experience with traditional ML techniques (e.g., regression, classification, clustering, time series).
Proven experience building and deploying ML models in production environments.
Hands‑on experience with Databricks, including Databricks application development (notebooks, workflows, dashboards, ML pipelines).
Strong understanding of feature engineering, model evaluation, and explainability.
Experience collaborating with data engineering, BI, and application teams.
Preferred / Nice‑to‑Have Qualifications
Experience designing and implementing agentic AI systems or autonomous decision‑making workflows.
Familiarity with Lakehouse architectures, feature stores, and ML lifecycle management.
Experience with ML Ops practices, CI/CD, model monitoring, and retraining pipelines.
Exposure to cloud platforms (e.g., AWS) and scalable ML infrastructure.
Experience embedding ML and agent outputs into enterprise applications or analytics platforms.
Knowledge of data governance, access controls, and secure ML deployment.
What Defines Success at the Staff Level
Independently delivers high‑impact ML models, agentic AI workflows, and Databricks applications.
Bridges data science, ML engineering, and full‑stack data app development.
Influences architecture, standards, and technical direction beyond assigned projects.
Acts as a trusted technical partner to business, analytics, and engineering stakeholders.
Raises organizational maturity in traditional ML, agentic AI adoption, and production ML practices.
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Pay range and Other Compensation & Benefits:
$148,200.00 - $222,200.00
The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer – and you can review more details about our US benefits at this link.
If you would like more information about this role, please contact Qualcomm Careers.