AI Engineer

• AI Implementation - Design, build, and maintain all of our AI systems across the company. Everything from internal marketing, sales, finance, and operations systems to local AI systems in our engineering workflows, and the Chamber itself.
• AI Adoption - Educate, Train, Support, and Uplift everyone in the company with their AI adoption. Running office hours, making tutorials, providing resources, sourcing solutions, and more.
• AI Infrastructure - Architect and operate a scalable set of infrastructure that allows non-technical, and technical teams to use AI in a secure and productive manner.
• AI Systems & Agents - In conjunction with departments, design, build and maintain all AI systems and agents. This includes administering and managing our off the shelf AI systems like Claude, Viktor, Cursor, etc. It also includes our custom built Agents, Skills, Plugins, Tools, and more.
• AI Security - You are responsible for creating, maintaining, and enforcing all data, privacy, and security policies across our AI systems and Agents. Organization, Department, and Role based permissions and settings across all our products.
• Other Projects - As this is a technical role, you will also be asked to help build other internal systems like automations, micro apps, and more.

AI Solutions Lead

• Define and maintain enterprise architecture standards, reference patterns, and governance guardrails for AI-enabled solutions moving from pilot to production.
• Establish decision models for build, buy, or integrate choices; model and vendor selection; data access; security; and responsible AI adoption.
• Design scalable architecture patterns for AI-enabled workflows and agentic processes across data, APIs, identity, security, and cloud platforms.
• Test new AI models, LLMs, agent frameworks, and tools to inform enterprise adoption.
• Design proof-of-concepts, define success criteria, and guide solutions from pilot to production.
• Lead architecture reviews and communicate trade-offs, risks, and growth to partners.
• Partner with teams to translate needs into governed, implementation-ready solutions.
• Ensure teams through demos, workshops, and reusable templates to support safe AI adoption.

Principal Architect – Enterprise AI

Machine Learning Engineer

• Own small to medium components of machine learning systems from technical design through implementation and delivery
• Translate technical requirements into high-quality, maintainable code and deliver workstreams according to plan
• Build and maintain data pipelines and feature engineering workflows to support machine learning and AI solutions
• Design, train, evaluate, and refine machine learning models with minimal supervision, applying sound statistical and engineering practices
• Implement ML solutions that can be deployed into production environments as microservices, APIs, batch jobs, or streaming components
• Support production monitoring efforts by helping define and implement metrics for model performance, data drift, anomalies, and retraining triggers
• Collaborate with Data Engineers, Software Engineers, Data Scientists, Product partners, and business stakeholders to deliver project objectives
• Understand system design, data models, and technical artifacts well enough to contribute to implementation decisions and tradeoffs
• Follow governance, documentation, coding, and source control standards consistently
• Demonstrate flexibility and proactively support teammates with day-to-day responsibilities as needed
• Clearly document and communicate work progress, technical decisions, and outcomes to technical and non-technical audiences

• Build agentic workflows for decision-making in healthcare
• Design and ship AI systems for healthtech
• Raise the technical bar across the organization
• Support and close exceptional founders in health technology

Principal Applied AI Engineer

• As a Senior AI Engineer within Bristol Myers Squibb's AI Venture Studio team, you will be a hands-on individual contributor responsible for building secure cloud-hosted applications.
• Design APIs, services, infrastructure patterns, deployment pipelines, and semantic-layer evolution patterns for agent context engineering.
• Collaborate with frontend engineers to define clean API contracts, streaming response patterns, error handling, and service-level behaviors for AI-powered user experiences.
• Create and maintain CI/CD pipelines, environment configuration, automated tests, and release processes for cloud AI applications.
• Partner with AI Engineers, Data Engineers, Data Scientists, and product teams to solve complex delivery challenges.
• Continuously refine delivery priorities and deliver MVP transition activities by maturing AI capabilities.

Senior AI Engineer

• You will develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for human-like agents.
• You will work on novel techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort and realism.
• You will contribute to our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field
• You will develop metrics and tools to analyze errors and understand improvements of our systems
• You will collaborate with engineers on Perception, Planning, Simulation, and Validation to solve the overall Autonomous Driving problem.

Machine Learning Engineer – ML Agents, Planning

• Performance optimization, analysis, and tuning of DL models in various domains like LLM, Multimodal and Generative AI.
• Scale performance of DL models across different architectures and types of NVIDIA accelerators.
• Contribute features and code to NVIDIA’s inference libraries, vLLM and SGLang, FlashInfer and LLM software solutions.
• Work with cross-collaborative teams across frameworks, NVIDIA libraries and inference optimization innovative solutions.

Deep Learning Software Engineer, Inference

• Explore and incorporate contemporary research on generative AI, agents, and inference systems into the NVIDIA LLM software stack.
• Conduct in-depth analysis, profiling, and optimization of agentic LLM workloads to significantly reduce request latency and increase request throughput while maintaining workflow fidelity.
• Design and implement scalable systems to accelerate agentic workflows and efficiently handle sophisticated datacenter-scale use cases.
• Advise future iterations of NVIDIA software, hardware, and system by engaging with a diverse set of teams at NVIDIA and external partners and formalizing the strategic requirements presented by their workloads.

Principal Deep Learning Algorithm Engineer

LLM Engineer

• Collaborate closely with our AI and ML research teams to understand their infrastructure needs and obstacles
• Monitor and optimize the performance of our infrastructure ensuring high availability, scalability, and efficient resource utilization
• Help define and improve important measures of AI researcher efficiency, ensuring that our actions are in line with measurable results
• Collaborate with diverse teams, including researchers, data engineers, and DevOps professionals, to build a seamless and coordinated AI/ML infrastructure ecosystem
• Stay on top of the latest advancements in AI/ML technologies, frameworks, and effective strategies, and promote their implementation within the company

AI and ML Infra Software Engineer, GPU Clusters

• Leading cloud VM platform initiatives from architecture and systems design through implementation, testing, and deployment.
• Designing, building, and operating scalable services that support secure, isolated agent workloads.
• Developing automation for infrastructure provisioning, VM lifecycle management, application delivery, configuration, monitoring, and remediation.
• Collaborating with engineering teams across NVIDIA to translate business and AI workflow requirements into dependable platform capabilities.
• Improving the platform’s reliability, scalability, observability, performance, and operational efficiency.
• Continuously raising standards for code quality, testing, documentation, infrastructure security, and production readiness.

Senior Software Engineer, AI Agent Compute

• Define and evolve the standards, architecture patterns, and tooling choices that govern how engineers use AI across the codebase — including where to invest in codebase readiness (instrumentation, specification, evaluation) versus targeted rewrites.
• Own execution of the multi-milestone plan (readiness → validated → governed) applied to corgi and secondary repos, in partnership with squad leads — without being their manager.
• Decide what AI tooling, agent infrastructure, and evaluation pipelines the org needs, and build or oversee the build of the ones that don't exist yet.
• Set the policy and technical controls for where human-in-the-loop review is required versus where autonomous agent action (PR review, deployment) is safe — resolving the tension between adoption speed and security/quality risk.
• Write the guidance engineers actually use — AGENTS.md/CLAUDE.md conventions, review norms for AI-generated code, eval standards — and make sure it's adopted, not just published.

AI Architect

• Own and refine the overarching AI platform story, with special emphasis on context-first document management, AI and agentic automations, scalability, security, and extensibility.
• Define differentiated messaging and value propositions for platform capabilities across personas and industries.
• Define differentiated FedRAMP messaging and value propositions for platform capabilities across personas and industries while supporting related content programs.
• Ensure consistency and clarity of AI platform-level messaging across all marketing, enablement, and sales touchpoints.
• Lead platform feature releases, orchestrating cross-functional launch strategies for AI, cloud, and core platform enhancements.
• Partner with Product Management to influence roadmap decisions based on market opportunity, customer needs, and competitive insights.
• Drive content strategy for platform launches, including pitch decks, solution briefs, whitepapers, demos, customer stories, webpages, and sales tools.
• Own competitive and market intelligence for platform-level capabilities, especially in AI-driven information management and cloud content services.
• Identify market trends, customer pain points, and emerging competitors to guide product and GTM strategy.
• Deliver actionable insights to Sales, Product, and Executive teams.
• Partner with Sales Enablement to ensure clear understanding of platform capabilities, differentiation, and value drivers.
• Support customer-facing teams with platform-level messaging guides, battlecards, objection handling, and training content.
• Collaborate with cross-functional teams to position M-Files as an industry leader in AI-powered information management.
• Represent M-Files in webinars, events, analyst briefings, and customer engagements as a platform subject-matter expert.

Senior Product Marketing Manager, M-Files AI Platform, Cloud

• Improve operational efficiency, resilience, and process quality by establishing scalable AI automation architecture standards and governance guardrails
• Accelerate transformation delivery and reduce rework through clear design frameworks, decision models, and reusable automation patterns
• Enhance employee and customer experience by reducing manual work, process fragmentation, and automation risk
• Embed AI-first architectural practices that improve automation scale, decision quality, and measurable business outcomes
• Lead evaluation of AI automation, agentic workflow, and transformation initiatives across the organization
• Define architectural decision frameworks and long-term platform strategy for n8n, Boomi, APIs, event-driven orchestration, and AI services to reduce duplication and increase reuse
• Establish governance guardrails for AI-enabled workflows, agents, prompts, models, data flows, and human-in-the-loop controls to ensure scalability, reliability, auditability, and responsible use
• Anticipate growth, compliance, security, and operational risks across automation platforms and transformation programs before they materialize
• Influence cross-functional roadmaps with AI Automation Engineers, BSAs, and business leaders based on architectural trade-offs, platform fit, and measurable outcomes
• Translate enterprise capability needs into clear, implementable technical designs that accelerate AI automation delivery
• Create architectural blueprints for intelligent workflows, agentic process patterns, and cross-system orchestration that reduce ambiguity and minimize rework
• Design secure, scalable, and resilient automation patterns across SaaS systems, internal platforms, APIs, data ecosystems, and AI/ML services
• Drive API-first, event-driven, and human-in-the-loop architecture standards that improve interoperability, control, and long-term maintainability
• Lead architecture reviews and represent AI automation and transformation strategy in executive and cross-functional forums

Principal Enterprise AI Architect

• AI Configuration: Design, prompt, and configure custom AI Agents to seamlessly integrate into the claims process. This includes co-pilot for adjuster guidance, Photos & Documents analysis (data extraction, summarization, automation), and designing code-less AI Agent workflows.
• Architect and Build: Proactively develop the industry’s best operational flows, utilizing Snapsheet’s Claim Platform and the most advanced AI and automation tools, developing objective metrics to show impact and ROI, and help customers adopt these new processes across their organizations.
• Client Advisement: Serve in a strategic, account management style capacity, leading weekly client meetings to understand operational pain points, desired outcomes, and roadmap priorities.
• Technical Unblocking: Proactively unblock customer bottlenecks related to AI configurations, prompting techniques, and system parameters (e.g., model settings, context window data).
• Cross-Functional Liaison: Act as the primary conduit between the client, engineering, and support teams. Translate customer needs into actionable feature requests and serve as the first line of technical support for the engineering team.
• Consultative Strategy: Make expert recommendations on system configurations and AI utilization to drive value, automation, and efficiency for the customer's claims organization.
• Continuous Improvement: Provide internal feedback on existing AI features and actively contribute to the design and implementation of new capabilities.

Senior AI Implementation Engineer

• Partner with internal and external stakeholders to re-envision and scale Core Engineering technologies, including the design and build of a suite of AI Agents
• Drive technical excellence and governance of AI Agents as use-cases are identified across Snap Engineering
• Collaborate across teams to understand product requirements, evaluate architectural trade-offs, and deliver solutions needed to build innovative products and services
• Champion best practices in availability, scalability, operational excellence, and cost management
• Serve as a technical mentor to senior engineers and a thought partner to engineering leaders
• Contribute to the technical roadmap for AI Agents and Core SWE infrastructure, ensuring alignment with organizational goals
• Partner closely with Product, Finance, Sales, and Engineering teams distributed across multiple offices
• Influence key architectural and implementation decisions for scalable, reliable, and cost-effective engineering solutions
• Iterate quickly without compromising quality and continuously raise the bar on operational excellence
• Adapt technical plans nimbly in response to evolving customer and business needs

Staff Software Engineer, DevProd AI

• Design & Implement ML Solutions: Take ownership of the end-to-end ML lifecycle for your projects, from ideation and research to deployment and monitoring.
• Test, Learn, and Iterate: Design and analyze tests to validate your models and quantify their business impact and design future iterations.
• Collaborate and Communicate: Partner closely with product managers, engineers, and business stakeholders to understand requirements, define problems, and communicate your findings and results effectively.

Machine Learning Scientist II

• Collaborate with customers to develop agent capabilities and workflows
• Design, ship, and operate agentic systems for large-scale data
• Build dependable tools and interfaces
• Develop and maintain the MCP server
• Work across the entire stack

Software Engineer, Full Stack – Agentic AI

• Talk directly to customers and partner with product to turn real review workflows into agent capabilities: generating dashboards, writing analysis scripts, and surfacing insights buried in their telemetry
• Design, ship, and operate agentic systems that reason over large-scale time-series data and hardware domain context
• Build everything around the model that makes agents dependable: tool interfaces, sandboxed execution for agent-generated code, context and memory management, custom compaction algorithms, opinionated skills, and guardrails
• Develop and maintain Sift’s MCP server, the tool surface that lets both our agents and our customers’ AI tools query telemetry directly
• Work across the whole stack
• Build the frontend surfaces where Sift Agents live, and make them feel great
• Design and implement the APIs that power our agentic capabilities
• Run agents reliably at scale, both in the cloud and on-prem
• Build evaluation suites that measure whether agents actually help engineers, and instrument quality, latency, cost, and failure modes in production
• Integrate and assess frontier models across providers

• talk directly to customers and partner with product to turn real review workflows into agent capabilities
• design, ship, and operate agentic systems that reason over large-scale time-series data
• build everything around the model that makes agents dependable
• develop and maintain Sift’s MCP server
• work across the whole stack
• build the frontend surfaces where Sift Agents live
• design and implement the APIs that power our agentic capabilities
• run agents reliably at scale
• build evaluation suites that measure whether agents actually help engineers

Machine Learning Engineer jobs in California

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AI Solutions Lead

Principal Architect – Enterprise AI

Machine Learning Engineer

Principal Applied AI Engineer

Senior AI Engineer

Machine Learning Engineer – ML Agents, Planning

Deep Learning Software Engineer, Inference

Principal Deep Learning Algorithm Engineer

AI and ML Infra Software Engineer, GPU Clusters

Senior Software Engineer, AI Agent Compute

AI Architect

Senior Product Marketing Manager, M-Files AI Platform, Cloud

Principal Enterprise AI Architect

Senior AI Implementation Engineer

Staff Software Engineer, DevProd AI

Machine Learning Scientist II

Software Engineer, Full Stack – Agentic AI

Software Engineer, Full Stack – Agentic AI

Senior Software Engineer, Full Stack – Agentic AI

Senior Software Engineer, Full Stack – Agentic AI