Top Senior Machine Learning Jobs in Sacramento, CA

Lead hands-on engineering for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Define APIs and abstractions, make architecture and scalability decisions, improve observability and reliability, and lead a small team of senior engineers through technical reviews, mentorship, and production delivery.

Lead net-new enterprise sales into Fortune 500 and large mid-market accounts building AI/ML systems. Prospect and create outbound pipeline, run long consultative sales cycles, orchestrate pilots and phased rollouts, influence technical and executive stakeholders, and partner with solution architects to close complex, high-value deals.

Lead the technical side of enterprise sales for autonomous systems and physical AI customers. Own discovery, architecture reviews, demonstrations, proofs of concept, evaluations, deployments, and expansions. Translate customer workflows into solutions, address technical objections, and collaborate with Product and Engineering. Build scalable sales engineering processes, technical content, evaluation frameworks, and operating models while supporting strategic automotive OEM and enterprise opportunities.

Lead product strategy, requirements, and roadmap development for Honeywell Forge cloud applications serving utilities. Own AI and analytics capabilities for grid operations, DER management, rate design, and utility programs. Conduct customer and market discovery, competitive analysis, and voice-of-customer research. Partner with solution architects, sales, customers, and engineering to prioritize and deliver B2B SaaS products that generate measurable utility operational value.

Lead product strategy, requirements, roadmap, and AI capabilities for Honeywell Forge cloud applications serving utilities. Conduct customer and competitive discovery, translate utility workflows into product investments, define commercial opportunities, and partner with engineering to prioritize and deliver cloud-native B2B software. The role focuses on AI for grid operations, DER management, rate design, and utility program performance while ensuring measurable operational value.

As a Senior Software Architect for AI Networking, you'll design and implement AI networking technologies, optimize data processing in AI systems, and collaborate on cutting-edge solutions.

Coordinate the CMM Data Modernization and Governance Program across workstreams, technical teams, stakeholders, and government organizations. Maintain schedules, sprint boards, deliverable trackers, risks, dependencies, documentation, training records, and compliance activities. Support status reporting, meetings, technical reviews, audits, budget tracking, resource updates, change requests, executive communications, and customer training while ensuring deliverables meet contractual and quality requirements.

Lead Agile delivery for a data modernization and governance program. Coach Scrum teams and Product Owners, facilitate ceremonies, manage JIRA boards, coordinate dependencies, mitigate risks, maintain RAID logs, and provide transparent reporting to stakeholders and senior leadership. Support SAFe implementation, backlog prioritization, release planning, continuous improvement, and delivery of data engineering, analytics, AI/ML, and cloud capabilities.

Own the global consumer engagement and personalization domain across digital channels. Assess current capabilities, define multi-year strategy and roadmap, recommend personalization infrastructure investments, and translate priorities into shipped products. Partner with engineering, data science, design, marketing, legal, and regional teams to deliver scalable experiences. Establish metrics, run cross-market experiments, optimize performance, and make global versus localized decisions. Influence executives, mentor product managers, and apply AI, identity resolution, and generative AI to personalization.

Deploys and optimizes enterprise infrastructure across on-premises systems, networks, AWS, Azure, and Microsoft 365. Engineers Terraform automation, Intune endpoint management, AI initiatives, SaaS integrations, SSO, and IT service workflows. Designs and maintains cybersecurity architecture, Zero Trust controls, security baselines, monitoring, and compliance controls aligned with CIS, NIST, and SOC 2. Provides advanced troubleshooting, technical support, VoIP administration, vendor coordination, and root-cause remediation.

Manage the AI opportunity roadmap for Risk Services, prioritizing use cases, coordinating internal data science teams and external vendors, translating business needs into requirements, and supporting AI governance, compliance, explainability, auditability, implementation, adoption, and benefits realization. The role focuses on business outcomes and solution delivery rather than hands-on model development.

Designs, develops, deploys, maintains, and optimizes ETL processes and cloud-based system integrations for healthcare data warehousing and business intelligence. Works with DHA and Cerner inpatient and outpatient data, develops database procedures, validates migrations, performs data mapping and modeling, and supports data science and machine learning teams. The role also documents processes, tunes performance, captures client requirements, and manages data transformation initiatives.

Own and develop SandboxAQ’s intellectual property function, including patent portfolio strategy, invention harvesting, prosecution, lifecycle management, outside counsel oversight, freedom-to-operate analyses, trademark clearance, and trade secret programs. Partner with scientists, engineers, and business teams to protect innovations, inform product strategy, support transactions, and build scalable IP processes in a fast-growing AI-focused company.

Manage a team of data scientists to develop fraud detection models, lead planning and collaboration with leadership, and communicate outcomes effectively while maintaining strong business intuition and technical leadership.

Design, develop, and deploy machine learning models and pipelines to optimize operations. Lead full ML project lifecycle, ensure model evaluation/monitoring, collaborate cross-functionally, mentor junior engineers, and drive continuous improvement in ML applications and processes.

Own complex customer escalations from troubleshooting through resolution across web services, APIs, SDKs, databases, and integrations. Diagnose issues using logs, network traces, code debugging, and technical tools; collaborate with Support, Product, and Engineering to identify root causes and document bugs. Apply AI-powered diagnostics and machine learning to detect patterns and automate troubleshooting. Create technical documentation, prioritize cases, and communicate solutions to technical and non-technical audiences.

Advise clients on antitrust eDiscovery compliance using machine learning and generative AI. Build and validate TAR/AI models (Epiq AI, Relativity, Reveal, Brainspace), craft LLM prompts, draft defensible protocols, train/transfer workflows to review teams, and support client-facing, pre-sales, and thought-leadership activities. Must manage multiple projects and meet deadline-driven schedules.

Manage paid media strategies and campaigns across search, social, display, and programmatic channels. Advise clients using integrated paid, organic, and analytics data; oversee campaign performance, keyword research, ad copy, landing page recommendations, and presentations. Build client relationships, mentor account associates, collaborate with SEO and data teams, and use AI to improve workflows and outcomes.

Develop and lead paid media strategies across search, social, display, and programmatic channels. Manage campaign strategy, performance analysis, testing, optimization, and integrated paid and organic recommendations. Consult with clients, present results, build relationships, mentor account associates, and identify data-driven growth opportunities. Use AI tools to improve workflows and contribute insights across the team.

As a Senior Machine Learning Engineer, you will design and implement systems for ML and NLP, optimizing models and tools for Clover's healthcare solutions.

As a Senior Fullstack Engineer, you'll architect and build a high-concurrency auction engine, leveraging AI for enhanced user experiences and system reliability.

Designs and operates scalable data infrastructure, pipelines, backend services, and cloud platforms powering LLM, machine learning, analytics, and experimentation workflows. The role develops Python services, APIs, data models, retrieval-augmented generation capabilities, vector search, and production AI systems. Responsibilities include data quality, observability, security, Infrastructure as Code, CI/CD, architecture, documentation, cross-functional collaboration, technical leadership, and mentoring across PlayStation Studios.

Leads AI-enabled Finance transformation initiatives from roadmap development through implementation and adoption. The role analyzes Finance processes, identifies automation opportunities, documents requirements, coordinates cross-functional Technology and Finance teams, manages change and training, tracks ROI and KPIs, and supports governance, compliance, SOX controls, and system integration across enterprise applications.

Lead NVIDIA’s AI for Quantum product strategy, roadmap, and go-to-market initiatives. Translate AI and quantum research into scalable platforms, developer tools, APIs, libraries, and workflows. Partner with researchers, engineers, quantum hardware companies, institutions, and developers to identify applications across quantum design, control, calibration, error correction, and algorithms. Evangelize the platform through technical communications, identify new AI, HPC, and quantum opportunities, and present product strategy to executives.

Develop HPC and AI software workflows for experimental data processing, imaging, plasma, high-energy physics, and materials simulations. Profile applications, benchmark performance, identify bottlenecks, and advise collaborators and product teams. Build tutorial examples, translate research into software tools, and represent NVIDIA at scientific conferences and industry events.