Top Machine Learning Jobs in San Diego, CA

Partner with healthcare clients to discover, design, and deliver production-grade AI/LLM solutions. Architect cloud-native systems, build and deploy ML/LLM applications, prototype rapidly, troubleshoot distributed systems, and lead client engagements to drive measurable business outcomes while collaborating across product, platform, and engineering teams.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

Lead B2B activation marketing to drive patient starts from commercial referral channels by building scalable systems, attribution frameworks, and repeatable playbooks. Implement MarTech and data-driven strategies, run A/B and incrementality tests, automate campaign execution using AI/ML and LLM tools, and templatize activation campaigns while managing and growing a cross-functional team.

Lead product vision and roadmap for the Hivemind AI Platform to enable training, evaluation, governance, and safe edge deployment of foundation and domain models. Drive MLOps, data/simulation pipelines, synthetic data generation, model governance, and integration with edge hardware. Partner cross-functionally to productionize research, ensure accreditation and safety, and support continuous learning and sovereign AI factory capabilities for mission-critical autonomy.

Lead design and operation of a centralized AI and data platform for autonomy: define compute strategy, build distributed training and simulation systems, manage multi-modal datasets and MLOps, enable model evaluation, optimization, and deployment to constrained edge and customer environments, and drive platform standardization and broad adoption across programs.

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.

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.

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.

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.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

Founding Product Engineer will build a vision-based Physical AI product end-to-end: device-to-cloud systems, real-time event ingestion and storage, multi-tenant access control, dashboards and workflows, and agentic/LLM-driven features. Work directly with customers, run pilots, and iterate rapidly across infrastructure and product surfaces.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Design, implement, and maintain multi-disciplinary AI programs. Plan, schedule, and coordinate AI/data science projects; engage stakeholders and senior leaders; anticipate and mitigate risks across workstreams; lead and guide junior staff; and ensure program delivery within DoD/classified environments.

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

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.

Triage AI opportunities, shape technical solutions, write precise SOWs and internal handovers, identify reuse vs. new engineering, and coordinate sales and engineering to deliver scoped AI engagements (5–10 concurrently, 30–45 day cycles). Ensure delivery-ready documentation and flawless handoffs to implementation teams.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

As a Systems Architect, you'll define and drive the infrastructure architecture for AI/ML workloads, design multi-cloud strategies, manage compute orchestration and storage systems, and lead capacity planning, while also collaborating with engineering leadership.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.

The VP of Enterprise AI & Automation drives the company's AI transformation, overseeing strategy, governance, technology deployment, and organizational adoption to enhance operational efficiency and customer experience.

The AI Application Engineer will design and deploy AI systems, collaborate with teams, implement Generative AI solutions, and promote continuous learning in AI practices.

Lead product strategy and execution for data and AI platforms, driving GenAI/LLM initiatives, cross-functional collaboration, responsible AI governance, experimentation (A/B testing), measurement of model impact, and mentoring product teams to embed scalable ML capabilities into clinical and member experiences.