Top Machine Learning Jobs in San Diego, CA

Lead and grow a senior machine learning and causal inference research team; define long-term research strategy; balance foundational and applied projects; partner cross-functionally to translate research into product impact; build external academic collaborations, publications, and a high-performing research program.

Design, develop, and scale software systems for machine learning algorithms powering personalization. Collaborate with researchers and product teams to implement production-ready, distributed solutions, enable offline experiments and A/B tests, and improve engineering practices and systems across teams.

Lead ML initiatives to improve Sales productivity and automation: define roadmap, design and deploy production ML models and AI agents, build measurement pipelines with analytics engineers, iterate from performance and seller feedback, and act as senior technical partner across product, engineering, and Sales to drive measurable revenue impact.

Lead ML technical strategy for Live Ads, designing and deploying forecasting, targeting, bidding, auction, and yield-optimization models. Build and evaluate large-scale production ML systems, create online/offline evaluation frameworks, partner with product to set objectives and trade-offs, and communicate results to stakeholders and leadership.

Design and build scalable training and inference systems for LLMs and multimodal/media models. Optimize end-to-end training (data pipelines, distributed training, mixed precision) and inference (KV cache, batching, quantization). Scale models into production, lead efficiency initiatives, and mentor engineers and scientists to improve latency, scalability, and reliability.

Design and execute ML research and end-to-end model development for generative and summary-generation systems. Lead rigorous experiments, translate findings for stakeholders, partner with engineers to deploy models, track state-of-the-art advances, and define roadmaps to improve promotional writing and discovery for members.

Design, prototype, and iterate supervised ML models to predict ad campaign delivery outcomes (reach, frequency, delivery risk). Engineer reusable features from ad-serving logs, build evaluation frameworks, prioritize model explainability, and partner with ML engineers and cross-functional teams to deploy and monitor models at scale.

Looking for a Machine Learning Engineer to enhance AI capabilities in image analysis and structured data. Responsibilities include developing computer vision models, deploying ML systems, and collaborating with engineering teams.

As a Machine Learning Engineer, you will fine-tune transformer models, deploy machine learning solutions, and work with cross-functional teams on innovative projects.

Lead a team of ML engineers to design, build, and productionize LLM/GenAI and agentic systems (retrieval, tool use, orchestration). Collaborate cross-functionally with MLOps, product, and data science, review code, mentor engineers, run technical evaluations and proofs-of-concept, and ensure reliable, safe ML deployments for a large SaaS product.

Embed with strategic higher-education partners to design, build, and ship production AI and platform integrations (LLM/RAG, LTI) across Cengage products. Own end-to-end deployments, write production code, ensure compliance and accessibility, evaluate AI safety and outcomes, mentor FDEs, and translate field learnings into product roadmap contributions.

The Machine Learning Engineer will develop and maintain scalable ML systems, collaborate across teams, and innovate solutions for real-time applications to enhance customer experiences.

Integrate LLMs, RAG, and agentic-AI into secure decision-support software. Design prompts and inference workflows, connect AI to backend services, evaluate outputs, build tests, document designs, and collaborate with engineering, security, and DevSecOps teams to deliver reliable mission-capable AI features.

Design, build, and deploy machine learning models for a payments platform, overseeing end-to-end solutions and experimentation, while monitoring models in production.

The Staff ML/AI Platform Engineer will design, prototype and deploy AI/ML models, optimize pipelines, mentor junior engineers, and collaborate closely with other teams to develop machine learning solutions.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

Seeking a Principal AI/ML Researcher and Engineer to lead research and development in Bayesian Learning and Reinforcement Learning. This role involves architecting large-scale AI systems, model development, and driving technical strategy for innovative AI platforms that enhance personalization and decision-making at Airbnb.

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.

Design, build, and operate cloud-based data pipelines, data lakes, and ML systems on AWS. Optimize databases and ingestion for structured and unstructured data, develop and deploy ML models using Jupyter and AWS AI/ML services, and support customers and internal teams on data architecture and migrations.

Lead QA for cloud-native AI/ML platforms: define test strategies, build and maintain automated test suites, validate data pipelines, models, and infrastructure (IaC), integrate tests into CI/CD, perform observability and non-functional testing, and collaborate with data scientists, engineers, and vendor teams to ensure reliability and scalability.

Design, develop, and maintain scalable ML and AI platform infrastructure for model training, deployment, feature engineering, candidate generation, agent deployment, and observability. Collaborate with data scientists and engineers, support platform operations, drive improvements, codify best practices, and influence platform investments to enable production ML systems at scale.

Build and scale production-ready generative AI applications: design multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deploy scalable FastAPI/Docker services with monitoring and optimization for latency, cost, and reliability.

Operate and monitor ML/AI models and agentic systems in production. Build AI observability, logging, tracing, and evaluation pipelines. Monitor LLM outputs, detect drift and model degradation, and maintain data/feature pipelines. Develop CI/CD, model versioning, experiment tracking, and automate alerts and incident remediation while collaborating with data scientists and platform teams.

Design, develop, and deploy machine learning and deep learning models; build end-to-end ML pipelines including preprocessing, training, evaluation, and deployment; perform feature engineering on large datasets; deploy and monitor models on cloud platforms; collaborate with cross-functional teams to integrate AI solutions and retrain models as needed.

Design, build, and deploy production generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector databases, integrate/benchmark LLMs, fine-tune models, and deploy scalable FastAPI-based services with monitoring, CI/CD, and Dockerized production pipelines.