Top Machine Learning Jobs in San Diego, CA

Design and build machine learning systems for Coinbase, responsibly use generative AI tools and copilots, apply human-in-the-loop practices, and deliver measurable efficiency, cost, and quality improvements while collaborating in a remote-first environment with periodic in-person surges.

Lead ML feature strategy and engineering for fraud detection, owning end-to-end pipelines, validating features at scale, and translating analytics into production ML systems. Drive tooling and architecture decisions, mentor junior team members, partner cross-functionally with product and risk teams, and apply practitioner-level ML knowledge to detect and prevent account takeover and scam activity.

Lead development and production of underwriting and credit decisioning models across Cash App Borrow and Afterpay. Own full modeling lifecycle: problem formulation, feature engineering, training, calibration, experimentation, deployment, monitoring, and iteration. Build decision frameworks, agentic engineering workflows, and collaborate with cross-functional partners to align model behavior with business and regulatory goals.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

The role involves building and optimizing a machine learning and simulation platform, supporting MLOps, model deployment, and infrastructure modernization, while mentoring engineers.

The Executive Director will lead AI and enterprise data strategy, enhance operational efficiency, ensure regulatory compliance, and drive AI adoption across the organization.

Lead product management efforts at Genesis Molecular AI by defining product roadmaps, collaborating across teams, and ensuring execution of projects in drug discovery.

Senior individual contributor building and maintaining underwriting and credit decisioning ML systems for Cash App Borrow and Afterpay. Responsibilities include feature engineering, model training, calibration, experimentation, deployment, monitoring, and portfolio-level analysis. Collaborate with cross-functional teams to align models with business and regulatory goals and develop AI-native engineering workflows and governance for reliable, auditable model development.

Design and implement Palantir Foundry data architectures, pipelines, and ontologies to support mission workflows. Build, troubleshoot, and optimize production-grade ingestion and transformation pipelines, integrate AI/ML models, and iterate prototypes with stakeholders. Apply engineering best practices, document solutions, and travel to client sites for deployment and embedded support.

Perform data collection, analysis, modeling, and visualization to build and deploy scalable ML/AI solutions (including NLP/LLM) in operational defense environments. Collaborate with engineers, implement data processes, validate predictive models, and support secure, containerized deployments while training teams and writing quality procedures.

The VP, AI Innovation & Enablement Leader will drive AI technology adoption, develop reusable patterns for engineering teams, and enhance enterprise AI capabilities.

Lead zero-to-one product initiatives to transform knowledge discovery using ML/LLMs. Define vision, roadmap, and success metrics; prioritize trade-offs; partner with Design, Engineering, ML, and Data Science; deliver incremental wins while building an extensible platform and improving prospect and subscriber experiences across devices.

Design, implement, and scale event-driven, distributed backend systems in Python on AWS to process and enrich large-scale document and media datasets. Lead technical projects, mentor engineers, integrate ML/LLM components, optimize performance, maintain observability, and collaborate with Data Science, ML Engineering, and Product to deliver reliable high-throughput content processing services.

Design, build, and optimize event-driven, distributed backend systems and APIs in Python for large-scale content metadata extraction and enrichment. Deploy and operate resilient AWS-based pipelines, collaborate with ML teams to integrate models, and ensure system performance, observability, and data integrity.

Design, build, and scale event-driven, distributed backend systems in Python to extract and enrich metadata at massive scale. Lead architecture, mentor engineers, integrate ML/LLM components, optimize performance, and ensure observability and reliability across AWS-based pipelines.

Pre-sales technical lead supporting enterprise accounts in Southern California and Hawaii. Conduct demos, proof-of-value, onsite engagements, RFP responses, and enable channel partners. Drive adoption of Sophos MDR, XDR, firewall, endpoint, email, cloud and advisory services, influence product direction, and collaborate with sales to expand accounts and win new logos using Salesforce and Vivun.

Develop, deploy, and maintain quantitative optimization and forecasting models (BESS dispatch, renewables, gas dispatch, price forecasting) to support trading, origination, underwriting, and asset management. Build data pipelines and the internal CWENQuant platform, benchmark models vs. operations, and provide analytical insights to inform commercial and risk decisions across US power markets.

The Staff Data Scientist designs and optimizes credit strategies, integrates manual underwriting with analytics, and collaborates with various teams to drive sustainable growth while managing credit risk.

Apply computational and statistical methods to large-scale transcriptomic and genomic datasets (primarily prostate cancer), design and evaluate supervised and unsupervised ML models, document reproducible analyses to regulatory standards, communicate findings via presentations and publications, and collaborate with multidisciplinary teams to inform product development and scientific strategy.

Lead product strategy and lifecycle for enterprise revenue and order-to-cash platforms. Own backlog and roadmap, translate stakeholder needs into user stories, collaborate with engineering, clinical, and analytics teams, support releases, and drive automation and data-driven improvements to billing, reimbursement, and revenue operations.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Develop large-scale Foundation Models for Autonomous Driving & Robotics, collaborating with teams, optimizing model performance, and mentoring junior members.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

The Staff Auditor conducts operational audits, utilizes technology for efficiency, analyzes data for risk patterns, and collaborates on process improvements.