Top Senior Machine Learning Jobs in Sacramento, CA

Lead design and production deployment of ML models focused on pricing, availability, forecasting, and optimization. Build data infrastructure, governance, testing/monitoring, and dashboards. Partner with executives and cross-functional teams, mentor others, and establish scalable data-science practices and experimentation frameworks.

Develop, deploy, and maintain scalable ML models (batch and real-time), build end-to-end MLOps pipelines (versioning, CI/CD, containerization, monitoring), analyze large healthcare datasets, bridge research-to-production gaps, present solutions to stakeholders, mentor ML engineers, and apply best practices for model monitoring, drift detection, and retraining.

Lead a small actuarial team to design and evaluate value-based payment models, pricing, forecasting, reserves, and provider reimbursement strategies. Perform advanced analytics using Python, SQL, SAS, Excel and machine learning, develop executive reporting and dashboards, drive cross-functional initiatives, and advise senior leadership on strategic, financial, and operational decisions.

Lead and grow a team of Conversation Designers to build and scale conversational AI experiences. Partner with Product, Engineering, Analytics, and ML teams to drive strategy, tooling, evaluation, and quality practices. Recruit and remove roadblocks, own strategic initiatives, and remain hands-on in designing, testing, and improving AI-driven conversational products.

Lead and scale teams building offensive and defensive AI agents and the ML, data, and safety systems that power them. Drive strategy, production-grade engineering, safety and governance, cross-team collaboration, and hiring/leadership development to deliver secure, reliable agentic security capabilities for customers.

Design and implement agent harnesses, evaluation benchmarks, and post-training pipelines. Develop RL environments and reward models, run and optimize multi-GPU training, profile and debug training jobs, and iterate on model fine-tuning to improve capabilities for agentic, edge-aware applications.

Lead full sales cycle to drive CTV performance advertising growth in North America: prospect, close, launch and scale CTV budgets, manage key advertiser relationships, collaborate cross-functionally, represent Jampp at events (20% travel), and provide local feedback to improve global initiatives.

Lead development and deployment of cutting-edge ML models for pricing, design and analyze experiments, collaborate with product and engineering, present findings to stakeholders, and lead technical projects to influence team output and business metrics.

Senior Business Systems Analyst responsible for capturing functional requirements for agent-facing servicing applications, defining integrations/APIs with core banking systems, translating needs into user stories and acceptance criteria, supporting testing and releases, analyzing workflows to improve agent productivity, and identifying AI/ML opportunities for fraud detection and automation.

Lead development and optimization of web scraping pipelines to extract, clean, and store large-scale web data. Handle dynamic content, pagination, distributed scraping, database design with NoSQL, deploy jobs to cloud, and monitor systems for reliability and data quality. Support ML-based data cleaning and categorization.

Lead end-to-end enterprise customer engagements and onboarding for large insurance clients, run program timelines and executive reporting, design and deploy AI agents and internal tooling, own the CE operations roadmap and knowledge base, drive cross-functional resolution of technical/operational issues, and continuously improve efficiency and quality across the Client Engagement team.

The role involves owning and delivering AI-powered features for Calendly, collaborating with multiple teams, and applying machine learning solutions for product enhancement.

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.

Design, build, and scale backend systems and APIs for high-transaction consumer products. Collaborate with product, credit, compliance, support, and analytics teams. Contribute to code reviews, technical planning, security, and operational excellence. Mentor peers and promote engineering best practices.

Lead the development and implementation of machine learning solutions, managing a team to drive strategic decisions and innovative business outcomes.

Design, implement, and scale ML-based solutions for search and advertising; define data collection, evaluation, and production serving strategies; analyze data to inform development; communicate cross-functionally; produce technical reports; and stay current with advances in LLMs, vision models, and generative AI.

Own end-to-end production ML systems for search, recommendations, and ranking: architecture, training pipelines, inference, deployment, monitoring, drift detection, and retraining. Partner with data, product, and backend teams to ship reliable, low-latency ML-driven product experiences and set standards for ML infrastructure and model health.

Lead development and refinement of machine learning pricing models (especially reinforcement learning) for Airbnb hosts. Collaborate with data scientists, product, and engineers to design, test, integrate, and monitor models, analyze performance, and prioritize projects to improve host outcomes.

The Senior Machine Learning Engineer at Grailed will enhance personalization and recommendations, develop AI/ML solutions, and collaborate across departments for data-driven insights.

Industry Expert who reviews and defends advanced AI/ML and systems engineering projects, chairs architecture and executive review panels, hosts occasional technical sessions, and sets standards for instructors. The role requires production-scale engineering experience, live architecture critique, a public technical footprint, strong English, and expertise in agentic systems, LLM evaluation, model serving, cloud infrastructure, distributed systems, observability, RAG, and enterprise deployments.

Build and deploy LLM-powered agents, workflows, and production integrations using platform templates, pipelines, and guardrails. Develop and run evaluation suites, integrate business systems and data sources, instrument cost and quality telemetry, and collaborate with business users to iterate within bounded scopes. The role requires strong Python and TypeScript, API development, prompt and context engineering, and production experience with agents, RAG, and tool use.

Build and deploy LLM-powered agents and workflows using templates, pipelines, and guardrails. Develop comprehensive evaluations, integrate AI solutions with business systems and data sources, and instrument applications for cost and quality telemetry. Collaborate with business users to iterate within defined scopes. The role requires strong Python and TypeScript skills, production experience with LLM applications, API development, prompt and context engineering, retrieval, and healthcare data handling.

Own end-to-end ML lifecycle for pricing and underwriting: improve model accuracy and coverage, reduce infrastructure cost, deploy and monitor production models on AWS, run experiments/backtests, and collaborate with domain experts to inform model improvements and product APIs.

Build and operate production machine learning systems for Airbnb Payments, including LLM agents, real-time fraud detection, anomaly detection, personalization, and checkout optimization. Design, train, deploy, monitor, and continuously improve batch and streaming AI applications at global scale. Partner with product, engineering, operations, and data science teams, establish model governance and testing standards, and mentor engineers.

Lead development of new credit line management programs by designing, testing, and scaling policies. Own end-to-end analytical work using SQL/Python, partner with Data Science and Product, and present data-driven recommendations to senior stakeholders to improve spend recovery, utilization, and line-management performance.