Staff Analytics Engineer (AI & Predictive)

Qualcomm • San Diego, CA

2d • Onsite

About The Position

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. 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.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Benefits

• competitive annual discretionary bonus program
• opportunity for annual RSU grants
• highly competitive benefits package designed to support your success at work, at home, and at play.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees