Top Machine Learning Jobs in San Diego, CA

The Senior Technical Architect will lead AI SDLC transformations, create architectural solutions, mentor teams, and drive innovation in regulated industries.

Provide on-call MLOps support for machine learning teams by triaging pipeline issues, debugging workflows, identifying root causes, and maintaining reliable model development operations. Collaborate with senior engineers to improve tooling, documentation, and processes, while communicating technical findings across teams. The role requires Python, foundational PyTorch and model pipeline experience, plus familiarity with CI/CD, monitoring, logging, and orchestration tools.

Own the full lifecycle of clinical text extraction models, from problem framing and annotation strategy through production deployment, evaluation, monitoring, and retraining. Build clinical NLP pipelines, establish defensible quality and error-analysis systems, and apply LLMs using rigorous evaluation, observability, and cost controls. Partner with clinical data and backend engineering teams while addressing PHI protection, de-identification, auditability, and access controls. As the first ML hire, set technical direction and potentially build and lead the future ML team.

Lead end-to-end product strategy and delivery for a core marketplace area, collaborating with engineering, UX, and design. Conduct qualitative and quantitative research, run agile sprints, measure results with analytics, and pioneer AI/ML product features and workflows to improve user experience and platform impact.

Lead strategic vision and execution of next-generation bioinformatics technologies. Identify and evaluate emerging computational methods, translate research into production-grade pipelines and prototypes, support product roadmaps and technology transfer, and partner cross-functionally to commercialize genomic and AI-driven healthcare solutions.

Design, develop, and deploy advanced machine learning models for naval applications. Build cloud-native ML pipelines, apply distributed computing and MLOps/CI-CD practices, integrate models into operational DoD environments, and produce technical documentation per government standards. Collaborate with multidisciplinary teams and apply cybersecurity principles for secure shipboard and edge deployments.

Support SMEs to identify, evaluate, and integrate AI/ML capabilities (especially for waveform analysis) into workflows. Provide integration guidance, documentation, training, troubleshooting, and cross-functional collaboration. Support data analysis and build Power BI dashboards, enabling data-driven decisions in a DoD environment. Requires active clearance and stakeholder enablement.

Design, develop, and deploy machine learning models and data pipelines for naval applications. Build and optimize distributed, cloud-native analytics and event-streaming systems, work with multiple data formats and GIS tools, containerize and monitor deployments, apply DoD security practices, and collaborate with multidisciplinary teams to deliver mission-ready AI solutions in shipboard and operational Navy environments.

Lead design, development, and deployment of machine learning models and cloud-native MLOps pipelines for naval/DoD applications. Work across multidisciplinary teams to implement scalable, secure ML solutions, support CI/CD and DevSecOps, and meet shipboard and operational requirements.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

Lead design, development, and production deployment of ML and algorithmic solutions. Own end-to-end ML systems, support distributed production reliability, communicate tradeoffs, and apply generative AI, LLM fine-tuning, RAG, and agentic techniques to product problems.

Responsible for fine-tuning state-of-the-art LLMs, optimizing models for deployment, and driving AI architectural decisions. Collaborate with cross-functional teams to develop impactful AI products.

Build and maintain scalable ML and generative AI platforms and backend infrastructure; deploy and manage production applications; create services exposing ML/AI products; implement RAG solutions and vector databases; integrate LLM orchestration and foundation model APIs; collaborate cross-functionally and mentor teammates.

Lead development of an AI-enabled Ethereum Layer 2 platform: build front-end and back-end systems, deploy and optimize AI models, create data pipelines, collaborate on model training, and contribute to product innovation and other company projects.

The Manager of RevOps/Yield analyzes patterns and data to optimize ad operations, manage vendor relationships, and oversee a remote team for revenue generation.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

Own Oscilar's AML product suite (transaction monitoring, KYC/KYB, screening, case management, SAR filing, AI agents). Define detection rules, model calibration, data strategy, and defensible audit trails. Collaborate with engineers and compliance, use AI across discovery/design/analysis, deliver PRDs, validate models, and ship production features.

Lead the engineering vision for threat detection, architect scalable systems, foster community intelligence, and create a culture of technical excellence and innovation.

This is a general talent-community application inviting candidates across many functions to submit resumes. Recruiting will review and retain applications for future openings; candidates should highlight experience, accomplishments, and interests for potential remote roles.

As Director of AI/ML, lead the ML organization, set technical vision, mentor a high-performing team, and contribute to model development and production deployment.

The AI & Data Science Director will lead AI strategies, foster technical leadership, support team development, and integrate generative AI into products.

Develop and deploy end-to-end machine learning solutions, collaborate with cross-functional teams, and implement MLOps frameworks while monitoring model performance in production.

The Senior Generative AI Engineer will design and deploy AI/ML solutions, focusing on Generative AI systems and their operational excellence across various sectors like R&D and Clinical Trials.

Conduct ML research for query understanding, ranking, clustering, and AI-powered search. Prototype and productionize algorithms, define data and evaluation requirements, design scalable systems, collaborate with engineers, run experiments, and publish patents/papers to integrate state-of-the-art ML into Yahoo Search products.

Conduct research and development to enhance NLP models aimed at improving market research methodologies using AI and Crowd Intelligence.