Top Senior Machine Learning Jobs in San Diego, CA

Gather and document functional requirements for agent-facing servicing applications, define integrations/APIs with core banking systems, consult with stakeholders, identify AI/ML opportunities (fraud, KYC, automation), support testing and implementation, and act as SME while managing risks and compliance.

Lead procurement strategy and supplier selection for inbound products and services. Manage RFIs/RFPs, negotiate contracts with Legal, administer Coupa purchase orders, reconcile invoices, and apply AI/ML and reporting tools to optimize procurement processes while collaborating with cross-functional senior stakeholders.

Lead technical execution across backend, frontend, mobile, AI, and data. Translate ambiguous product goals into technical plans, unblock hard engineering problems, coordinate cross-domain work, raise engineering quality, define hiring needs and technical evaluation standards, and build internal AI workflows to foster an AI-native engineering culture.

Design and build custom machine learning and statistical solutions from first principles. Collaborate with engineers and analysts to architect data workflows, write modular tested code, optimize algorithms on on-prem infrastructure, and champion software engineering best practices (CI/CD, testing, documentation) across data systems.

Design, develop, and implement secure, scalable cloud systems (Azure/AWS); perform system engineering, risk assessments, and NIST-compliant ATO/ATD processes; manage secure data ingestion and orchestration; integrate AI/ML concepts; secure communications and network infrastructure; present technical findings to senior USG leadership and provide hands-on support.

Lead end-to-end healthcare fraud investigations for Clover's Medicare Advantage business. Own caseloads, gather and analyze claims and medical evidence, coordinate legal support, prepare defensible referral packages, leverage AI and analytics tools, and collaborate cross-functionally to drive recoveries and provider actions.

Lead architecture and implementation of high-performance software to deploy and operate AI/ML models for network threat detection. Build autonomous AI agent workflows, production ML pipelines, and services; drive cross-team engineering standards, mentor engineers, and represent technical direction to leadership.

Lead GTM technical engagements with data-platform ISVs to design architectures, build POCs, benchmarks, and enablement for NVIDIA acceleration across analytics, databases, and GenAI workflows. Create reference assets, demos, and training while partnering with product, engineering, sales, and customers.

Design, prototype, and deploy production-ready AI products using ML and generative AI. Lead cross-functional teams, collaborate with stakeholders to solve workflow problems, build scalable prototypes and proofs-of-concept, and apply latest ML/GenAI research techniques.

Own growth and performance for Kits AI across funnel optimization, activation, retention, pricing, and monetization. Productize ML capabilities into creator-facing workflows, partner with research and engineering, run experiments and analytics (e.g., Amplitude), conduct customer research, and leverage AI tools to accelerate shipping and internal insight systems.

Evaluate and strengthen AI model behavior on cybersecurity topics by writing expert prompts, annotating and grading model responses for accuracy and safety, and applying structured guidelines to classify sensitive content. Work part-time on AI safety tasks with emphasis on secure, responsible handling of dual-use information.

Design, develop, and deploy production forecasting models and agentic AI solutions on GCP. Perform EDA, feature engineering, experiment design, and model interpretability. Build and integrate AI agents (LangChain, Vertex AI Agents) into quotation and forecasting workflows, maintain MLOps pipelines (Vertex AI Pipelines, Cloud Composer), and present results to stakeholders.

Translate cutting-edge ML and physics research into production-grade scientific code, architect and scale large-scale simulation and training pipelines on GPU infrastructure, optimize hardware-level performance, and support models throughout the product lifecycle to accelerate in silico drug and materials discovery.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

As a Machine Learning Engineer, you will design, build, and deploy ML models for underwriting at Stripe Capital, ensuring performance while collaborating with multiple teams.

The role involves designing, training, and evaluating ML models, optimizing performance, and collaborating with teams to implement AI technologies in products.

The AVP, Digital Portfolio Management oversees P&L for small business products, driving underwriting profitability and digital transformation through analytics, cross-functional collaboration, and strategic initiatives.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

Seeking a Senior Full Stack .NET Developer to work on a social good project, with strong skills in C#, AngularJS, design, and UX. Requires 5+ years of experience.

Design, optimize, and deploy deep learning models for customer behavior prediction. Perform feature engineering, implement research-informed architectures, scale training pipelines, collaborate with client teams, document findings, and uphold engineering best practices.

Build and maintain backend and full-stack marketplace features while supporting ML Ops infrastructure (model deployment, feature stores, real-time pipelines, monitoring). Collaborate with data scientists to bring models to production, write well-tested code, address performance and scalability issues, and contribute to technical planning and cross-functional communication.

Build and maintain full-stack marketplace features and support ML Ops infrastructure (model deployment, feature stores, real-time pipelines, monitoring) on Chalk and Vertex AI. Collaborate with data scientists to productionize models, write well-tested code, address performance/scalability issues, and contribute to technical planning.

Lead a multidisciplinary team to design, deploy, and optimize ML-driven bidding systems and measurement for a nine-figure marketing budget. Drive experimentation, partner strategy, media-mix optimization, and end-to-end measurement while aligning Marketing, Analytics, Engineering, and Product to improve marketing ROI and scale performance.

Lead development of scalable AI/ML capabilities for Mail Intelligence: build LLM- and ML-based personalization, data pipelines, tools, and observability. Mentor engineers, collaborate cross-functionally, run A/B tests, implement feedback loops, and balance cost/quality/speed tradeoffs to deliver production-grade solutions.

The Senior ML Engineer will develop AI solutions for healthcare data, collaborating with various teams, contributing to AI strategy, mentoring, and maintaining expertise in AI technologies.