Top Machine Learning Jobs in Sacramento, CA

Own end-to-end modeling for vendor matching, order routing, and supply-chain optimization. Build predictive, causal, RL and OR solutions; design autonomous decision logic; quantify counterfactual impact; ship production models with data engineering; mentor senior data scientists; write technical design docs; partner on quarterly priorities and expand into revenue cycle and finance domains.

Lead and build a data science organization to create a prediction engine for vendor matching, order routing, and supply chain optimization. Anchor projects to P&L, design measurement and experiment frameworks, ship rapid wins, hire and coach data scientists, work hands-on in modeling and technical design, partner with product and engineering (Snowflake), and evolve systems toward agentic AI while reporting impact in dollar terms.

Build, ship, and own end-to-end ML models for vendor matching, order routing, and supply-chain optimization. Partner with data engineering to productionize models, quantify impact, document work, and implement decision logic components toward agentic AI in a fast-moving healthcare startup environment.

Lead technical strategy and hands-on engineering for Airbnb's AI-powered ads growth engine. Architect scalable systems for creative generation, bidding, budget optimization, and audience management across paid channels. Write and review code, set standards for availability and quality, collaborate with Marketing/Product/ML teams, roll out staged automation, and mentor engineers to advance platform autonomy while ensuring brand safety and human-in-the-loop controls.

Lead and deliver complex, cross-team AI product programs, drive agent-first SDLC adoption, ensure integration and operational readiness of AI workflows, define metrics, communicate risks and status, and standardize TPM best practices across Product and Engineering.

Transfyr seeks adaptable builders for various roles across CS, biology, and business. Responsibilities include developing sensor stacks, data harmonization, computer vision, machine learning systems, lab operations, project management, and business development.

Partner with a retained search firm to place software engineers and quant developers at top hedge funds. Provide candidate flow from your network; firm supplies clients, process, and fees. Remote, commission-only role with placement and pipeline incentives. Apply with placement examples, outreach metrics, and a candidate submission.

Serve as primary client contact for a portfolio of loyalty-program clients. Scope and manage end-to-end actuarial analyses rooted in predictive modeling and machine learning, validate model outputs, translate results into clear recommendations and presentations, and partner with analysts and data scientists to deliver high-quality, client-facing work in a fully remote consulting environment.

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 and scale engineering teams building Protect, a real-time multi-signal fraud detection platform. Set technical strategy, develop managers and senior engineers, deliver distributed systems and APIs, improve architecture, observability, reliability, and developer productivity while partnering with Product, Research, Security, and exec stakeholders.

Lead client-facing Data & Analytics engagements across pre-sales, architecture, and delivery. Define strategy, roadmaps, solution designs, and SOWs for modern data platforms, pipelines, governance, analytics, and AI enablement. Drive practice development, create reusable IP, mentor teams, and ensure quality delivery and strong business outcomes.

As a Senior Technical Product Manager, you will lead the AI Platform team to define roadmaps, collaborate with technical experts, and improve healthcare delivery by leveraging AI technologies.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Lead and execute product strategy for Everyday Money: define product thesis and roadmap, ship product lines, run AI innovation lab for agentic consumer experiences, manage and develop PMs, partner with design, Credit, and external partners, and own business economics and compliance for durable retention and monetization.

Senior presales role driving planning solution strategy for large enterprises. Lead executive discovery, design tailored demos, articulate value of Workday Adaptive Planning and AI-enabled forecasting, support sales motions (RFPs, workshops, POCs), mentor peers, and travel up to 50% to engage customers and represent Workday at events.

Provide transactional counsel on open source licensing/compliance, AI-related licensing, and IP licensing. Review external publications and developer-facing materials, negotiate university research and data agreements, draft IP terms in commercial agreements, build playbooks/templates, and partner with engineering, data science, marketing, and international colleagues to manage IP risk and govern open source releases.

Lead end-to-end supply chain planning science: own forecasting tournaments, inventory placement optimization, vendor performance systems, and raw-material signals. Define methodology, production models, and iteration loops; drive AI-native workflows (LLM-aided EDA, agentic feature engineering), partner with planning operators and engineering, and mentor junior scientists to raise methodological standards and deliver measurable business impact.

Lead strategic GTM and co-selling initiatives with Google Cloud to drive NVIDIA AI adoption. Develop and maintain technical enablement assets, run developer activations and events, collaborate with Google Cloud field and engineering teams, convert samples into demos and labs, and translate customer feedback into product and roadmap insights.

Lead and deliver complex, cross-team AI platform programs: manage schedules, risks, integration of AI workflows and agents, enable operational readiness, drive TPM best practices, and communicate status and metrics to stakeholders in a remote, multi-timezone environment.

Lead and supervise stability studies and quality records to ensure cGMP compliance. Manage CAPAs, OOS/OOT investigations, stability data analysis and reporting, mentor laboratory staff, coordinate cross-functional stability projects, support audits, and drive continuous improvement and sustainability initiatives to optimize product shelf-life and regulatory readiness.

Own RevOps data models and BI infrastructure to drive GTM strategy. Clean and analyze datasets, build predictive models, define and track leading/lagging KPIs, deploy dashboards, document data definitions, and deliver executive-ready insights while partnering cross-functionally with Finance and GTM teams.

Lead go-to-market strategy for Magnet Forensics' security, incident response, and AI product portfolio. Influence executive, product, and sales stakeholders; translate technical AI/ML and forensic capabilities into messaging; develop buyer personas; create sales enablement and demand-generation content; evangelize products at events; partner with competitive intelligence and customer advocacy; mentor product marketing team members.