Job description

Company: Qualcomm Incorporated Job Area: Information Technology Group, Information Technology Group > Data Science General Summary: The Staff Analytics 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.** Key Responsibilities Traditional Machine Learning & Analytics • 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. • 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 . Qualifications Required 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 . Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here . Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries). To all Staffing and Recruiting Agencies : Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications. EEO Employer: Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification. Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law. 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 .

Requirements

Must-haves

Qualcomm Incorporated
Information Technology Group, Information Technology Group > Data Science
This role requires full-time onsite work in San Diego, CA (5 days per week).
Traditional Machine Learning & Analytics
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.
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 .

Nice-to-haves

Preferred / Nice‑to‑Have Qualifications

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlpythonci/cdawsrest

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Effort: medium