Top Machine Learning Jobs in San Diego, CA

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

Develop and deploy ML-based perception algorithms for robotics, optimize pipelines, and collaborate across disciplines for integration.

The Staff Engineer will design and manage ML infrastructure while mentoring teams, driving MLOps strategies, and ensuring compliance in healthcare data systems.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.

Design and develop machine learning systems for construction robots, including scalable infrastructure, computer vision models, and data analysis for performance improvement.

Build and design backend services, AI agents, and machine learning models. Collaborate cross-functionally to deliver AI features and maintain code quality.

As a Senior Machine Learning Engineer at ZoomInfo, you will enhance core datasets through ML/NLP solutions, manage entity resolution tasks, and drive cross-functional impact by mentoring teams and collaborating on ML KPIs.

Lead and scale Vibrant's data science, ML, and AI function: set strategy and governance, oversee applied research and production ML (especially NLP), ensure HIPAA-compliant deployments, partner with execs and engineering, recruit and mentor teams, and deliver measurable outcomes for crisis services and enterprise initiatives.

Build and maintain backend services and infrastructure for ML and generative AI features. Develop scalable, resilient, high-performance Python services (async frameworks, streaming), deploy to Kubernetes, integrate RAG and LLM orchestration, instrument LLM observability, and collaborate cross-functionally to deliver production-ready 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.

Design and productionize LLM/NLP pipelines for regulation parsing and semantic search (RAG). Build forecasting, risk-scoring, and optimization models for supply chain. Implement MLOps: containerized deployments (Docker/Kubernetes), pipeline orchestration (Kubeflow/MLflow), inference optimization, and monitoring with retraining loops. Apply transformer models and prompt engineering for domain-specific tasks and convert natural language requirements into executable rule formats.

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.

Design, develop, and deploy AI-driven Salesforce applications, autonomous agents, intelligent workflows, and decision-support capabilities. Implement Agentforce, Einstein AI, Prompt Builder, Data Cloud, LLMs, RAG, and integrations with enterprise systems. Build human-in-the-loop experiences, AI-native UX, analytics dashboards, and scalable architectures while ensuring security, governance, and Agile/DevSecOps practices.

Lead design, training, fine-tuning, and deployment of ML and generative models (LLMs, diffusion). Build scalable MLOps pipelines, vectorization and data ingestion, integrate models into microservices for low-latency inference, implement evaluation/alignment/bias-mitigation, optimize models (quantization, distillation), and mentor engineers while influencing the AI product roadmap.

Design, build, and deploy high-throughput ML and LLM-powered personalization and agentic systems at consumer scale. Process petabyte-scale data, optimize real-time inference, implement resilient fallback paths, and balance latency, cost, and model quality while collaborating across engineering and product teams.

Lead architecture and delivery of scalable, production-grade ML/AI platforms and Python SDKs. Design Agentic AI and multi-agent systems, build FastAPI services, implement CI/CD, observability with Galileo, mentor engineers, and troubleshoot distributed AI systems for high availability and performance.

Design real-world ML and NLP tasks, build reference solutions and evaluation frameworks in Python, assess LLM outputs for correctness and reasoning, identify failure modes, create scoring rubrics, and collaborate with experts to improve model capabilities. Part-time, remote contractor (~20 hours/week).

The role involves preparing traffic data, developing and deploying machine learning models on Google Vertex AI, and collaborating with teams to improve project workflows.

Develop scalable web applications using React and Next.js, implement backend solutions in Python, maintain DevOps practices, and collaborate with AI/ML teams to deploy applications.

The Forward Deployed ML Engineer will integrate AI/ML models into clinical software, assist in model fine-tuning, and engage with clients to ensure success in healthcare deployments.

Design, research, and deliver LLM and GenAI solutions for healthcare SaaS at scale: architect systems, handle healthcare data, implement PEFT and RAG pipelines, ensure responsible AI, mentor teams, and translate technical outcomes to business impact.

As a Senior Machine Learning Engineer, you will design and maintain ML systems for content recommendations, search, and spam detection, while augmenting user experience and system performance.