Artificial Intelligence

• Own the full AI engagement arc from discovery through sustained workflow adoption
• Drive requirements and delivery precision to track against plans and surface risks early
• Champion continuous quality through testing and feedback loops during AI engagements
• Hold the R&D feedback loop accountable to ensure field learnings flow back
• Build playbooks to identify reusable patterns and scalable frameworks for the team
• Own engagement economics and track cost structure across active engagements
• This is a field-first role with expected travel to partner locations

AI Engagement Manager

• Lead end-to-end delivery of AI Perception and Skills capabilities, from research conception through production deployment on robot hardware.
• Own the roadmap and success of both teams, setting clear goals and holding yourself and the team accountable for quality, reliability, and pace.
• Accelerate our data flywheel strategy — combining teleoperation, synthetic data, and fleet learning — to systematically expand robot performance and reliability at scale.
• Champion your team's talent, represent their needs to senior leadership, and foster an inclusive environment where engineers feel safe to experiment and learn from failure.
• Drive performance cycles, build individualized development plans, and lead hiring to grow both teams with high-caliber researchers and engineers.

Senior Manager, AI Perception and Skills

• Set the multi-year strategy for ML, data, and BI
• Build and scale the Data Science / AI, Data Engineering, and BI Engineering organization
• Manage managers and tech leads
• Run performance management, career development, and retention
• Operate as a peer to VP-level leaders across various functions
• Own the architecture, deployment, and operation of the production ML

VP of AI, ML and Data Strategy

• Develop and extend C++ and Python simulators that model system-level network and compute traffic for agentic LLM workloads in datacenter environments
• Characterize real-world LLM serving workloads and distill them into representative simulator inputs
• Run simulations at scale and apply statistical techniques to post-process and interpret results
• Identify performance bottlenecks and translate findings into concrete architectural recommendations
• Collaborate with hardware, software, and research teams to influence the design of future AI systems

Systems Performance Engineer – Agentic AI Workloads

• Validate performance of RAG-based LLM models using real field escalation scenarios
• Analyze and structure internal data sources including logs, knowledge base, and support documentation
• Develop and optimize retrieval pipelines including embeddings and indexing strategies
• Improve prompt engineering techniques to enhance response accuracy and relevance
• Benchmark RAG models against baseline LLM approaches
• Identify data gaps and recommend improvements for knowledge coverage
• Collaborate with software, applications, and product teams to align solutions with business needs
• Document findings and present results to stakeholders

AI/ML Internship

• Reporting to the Director of Communities and Ecosystem you are the primary commercial architect for SUSE’s most strategic AI relationships (NVIDIA, Intel, AMD)
• Lead the creation of the SUSE AI Factory narrative, developing technical GTM solutions that move at the speed of the AI market
• Identify and productize top use cases into validated "Blueprints" for OEM and GSI partners
• Architect the GTM strategy for our AI Developer Bundles to win mindshare at the workstation level
• Define and execute the "Better Together" narrative
• Author high-fidelity technical content
• Develop specialized training for global teams
• Lead SUSE’s technical presence at key AI shows using data-driven insights
• Assemble and lead a specialized team

AI Partner Marketing

• Architect internal productivity revolution and analyze workflows.
• Rapidly prototype and refine agentic solutions.
• Help build and deploy production grade AI agents.
• Integrate AI agents with enterprise resources.
• Define and track productivity & ROI.
• Stay at the forefront of AI developments in the media industry.

Director, Applied AI – Strategy

• Define model governance for AI/ML at Mercury.
• Continuously build and enhance the frameworks for validation, monitoring, and governance.
• Own validation, monitoring, and governance of Mercury’s AI/ML model portfolio.
• Partner closely with data scientists, engineers, compliance leads, and product teams.
• Shape Mercury’s approach to model risk management in the context of AI.
• Perform independent validation across predictive ML models and generative AI systems.
• Assess risks in LLM-powered applications and identify model limitations.
• Serve as a trusted advisor throughout the AI/ML lifecycle.
• Help shape responsible AI standards including explainability and bias assessment.
• Develop AI-enabled automation tools and modernize the MRM function.
• Champion MRM as a strategic enabler for AI/ML adoption across teams.

Senior Model Risk Manager – AI/ML

• Lead teams that design, build, and sustain automation, AI‑enabled services, analytics, and insights that support HR operations and service delivery.
• Drive thoughtful application of practical technologies—such as Power Automate, Power BI, Copilot, and agent‑based solutions—to improve scale, consistency, and efficiency.
• Enable data governance, reporting, and analytics that support operational decision‑making and performance management.
• Lead and develop high‑performing teams responsible for operational execution.
• Drive ongoing improvement by identifying opportunities to simplify work, reduce manual effort, and improve service quality.

Manager, Automation, AI & Insights

• Develop production-grade integration and automation solutions: Design and implement production-ready automation workflows that integrate cloud services, internal data sources, and LLM endpoints.
• Lead the development and deployment of domain-specific LLMs: Fine-tune lightweight models on internal data from Gong, Notion, BigQuery, and other sources, then make them accessible to teams across the organisation through intuitive interfaces.
• Establish best practices and patterns for AI and automation across the company: Create templates, documentation, and training materials that enable non-technical teams to build and deploy their own automation workflows confidently.
• Enable widespread adoption: Run hands-on workshops, provide ongoing support, and mentor teammates so anyone at UpGuard can leverage AI and automation tools effectively.
• Embed security & governance: Bake privacy, compliance, and cost controls into every workflow and model deployment, ensuring enterprise-grade reliability and data protection.

AI and Automation Engineer

• Design, develop, and optimize new features and algorithms for oneDNN targeting Intel processors, Intel Processor Graphics, and Intel discrete GPUs.
• Perform performance analysis and optimization to achieve best‑in‑class deep‑learning inference and training throughput on current and next‑generation Intel platforms.
• Develop hardware‑specific parallel algorithms, including multithreading, vectorization, and memory‑layout optimizations.
• Contribute to assembly‑level programming and low-level performance tuning for Intel microarchitectures.
• Collaborate with cross‑functional teams across software engineering, architecture, and AI performance to ensure strong integration with Intel’s broader AI ecosystem.
• Engage with the open‑source community, participate in code reviews, and maintain high-quality coding and documentation standards.

Senior AI Algorithm Engineer

• Be the domain credibility of the pod. You know grocery retail and CPG — merchandising, supply chain, store ops, trade, e-commerce — well enough to walk into any room at any level and earn it immediately. As the pod scales, you'll deepen into a vertical and become Instacart's sharpest expert in that space.
• Get to the real problem. Embed with partner teams to identify where agentic AI will actually move the business — not just what's technically possible, but what will change how operators work day to day. Translate those problems into AI-solvable use cases the pod can build against.
• Lead discovery and requirements. Run structured workshops at partner sites, surface and prioritize the use case map, and turn field learnings into clear inputs for the pod and R&D. You're the bridge between what partners need and what gets built.
• Show, don't just tell. Lead hands-on demos and platform walkthroughs tailored to the partner's specific workflows — making abstract AI capability concrete and compelling in the room. The ability to demonstrate the art of the possible is as important as the ability to articulate it.
• Own adoption. Drive the change management that makes deployments stick — new operating cadences, workflow redesigns, user training. Success is measured by whether team behavior changes, not whether software ships.
• This is a field-first role. Travel is a core part of the job — expect to be on-site at partner locations regularly, potentially 50%+ of the time. If you do your best work embedded with a team rather than behind a desk, you'll thrive here.

AI Field Consultant

• Identify and work through seams and bottlenecks across platform workflows
• Define and build end-to-end, AI-native workflow proofs
• Build production-like implementations that prove what works in practice
• Test and compare approaches in real workflows
• Create undeniable proof points for AI-native workflows
• Translate successful approaches into reusable patterns
• Ensure high-impact approaches propagate across the platform
• Evaluate trade-offs and risks when using external solutions

Distinguished Engineer, AI Builder

• Serve as EnerSys’ executive leader and single point of accountability for enterprise AI strategy, execution, and outcomes.
• Translate enterprise and business strategies into a clear, multi‑year AI vision and roadmap aligned with growth, productivity, quality, safety, and risk objectives.
• Advise the CIO, ELT, and Board on AI trends, risks, opportunities, and competitive implications.
• Represent EnerSys externally with strategic AI partners, technology providers, and industry forums.
• Design, implement, and continuously evolve the enterprise AI operating model, including governance forums, funding and intake processes, prioritization methods, and decision rights.
• Chair and run the Executive AI Steering Committee, ensuring disciplined decision making, clear accountability, and execution follow through; set agendas, align key stakeholders, and communicate decisions and actions clearly.
• Establish enterprise standards for responsible AI, data usage, security, privacy, and risk management in partnership with Legal, Cybersecurity, IT Architecture, HR, and Compliance.
• Own AI investment governance, ensuring alignment with capital planning, transformation funding, and enterprise portfolio priorities.
• Own the global AI use case portfolio and prioritization framework, balancing short term value delivery with long term strategic capability building.
• Ensure AI initiatives progress efficiently from ideation to pilot to scaled production—eliminating “pilot only” stagnation.
• Define, track, and report enterprise AI value metrics, including operational efficiency, cost reduction, quality improvement, risk mitigation, revenue enablement, and customer impact.
• Regularly recalibrate the AI roadmap based on realized outcomes, business performance, and evolving priorities.
• Orchestrate cross-functional delivery across IT, data, digital platforms, business units, and external partners—creating shared plans, clear ownership, and fast decisions.
• Resolve enterprise level dependencies and escalations impacting delivery, adoption, or value realization by facilitating trade-off discussions, aligning decision makers, and driving timely, documented outcomes.
• Ensure AI initiatives are integrated with core enterprise platforms (ERP, data environment, digital products) rather than delivered as isolated solutions.
• Build scalable execution playbooks, delivery standards, and reusable components to accelerate adoption across regions and functions.
• Partner with HR and Communications to lead enterprise-wide change management, AI literacy, and workforce enablement.
• Sponsor AI capability building, including training programs, citizen developer models, communities of practice, and leadership education.
• Drive cultural adoption by positioning AI as a core business capability embedded in how work gets done—not as a technology experiment.
• Support workforce transformation planning, including role evolution, ethical considerations, and future skills development.
• Build, lead, and develop a high performing enterprise AI organization with clear roles, succession, and career paths.
• Set performance expectations, accountability, and development plans for direct and indirect reports.
• Foster a strong culture of accountability, collaboration, and execution discipline across the AI ecosystem.

VP Enterprise AI Enablement

• Independently partner with AI development tools to break BRDs into features and stories with accurate time estimates in ADO (Azure DevOps)
• Build POC solutions using AI-assisted development tools, delivering functional prototypes within single iteration cycles
• Harden POCs to MVP standards including architecture documentation, infrastructure notes, markdown continuity files, and comprehensive test suites
• Write and maintain comprehensive test suites; validate code quality for teammates at the MVP level
• Run quality scanning tools (e.g., SonarQube) and resolve findings to maintain clean code standards
• Prepare complete development team handoff packages for innovations moving to production, including all documentation and architectural context
• Partner with Catalysts through the Paired Innovation model, translating business requirements into technical solutions and providing build status updates
• Contribute to the team's skills repository and participate in the monthly skill review rotation
• Evaluate new AI tools, models, and development approaches, sharing findings with the team
• Participate in team ceremonies, rotate through scrum master responsibilities, and demo completed work

AI Creator

• Build analytical and financial models that quantify the business impact of AI capabilities across Autodesk’s platform and industry groups
• Develop forecasts for AI adoption, usage, revenue influence, and long-term planning
• Construct business cases and decision-ready analysis to guide investment, prioritization, and leadership reviews
• Model and optimize inference cost economics (COGS), including GPU utilization, cloud strategy, and scaling drivers
• Translate product telemetry into insights supporting monetization, pricing, and cross-industry growth opportunities
• Create executive- and Board-ready presentations, narratives, and slide materials that communicate strategic recommendations, investment plans, and business priorities with clarity and impact
• Contribute to building repeatable dashboards, reporting frameworks, and tools to enable data-driven execution across Product, Finance, and Engineering teams

Business Strategist, AI Foundation Models

• Lead the development and execution of strategic marketing initiatives to support portfolio growth and innovation
• Collaborate with product management, commercial teams, and regional stakeholders to deliver aligned marketing strategies
• Develop impactful content and campaigns to drive demand generation and strengthen market presence
• Execute integrated, multi-channel marketing programs to enhance customer value and engagement
• Position the organization as a thought leader across key markets through targeted messaging and insights
• Partner cross-functionally to drive business results

Global Market Development Manager, AI

• Contribute to the design, development, and optimization of AI algorithms
• Assist in implementing and tuning models for performance and accuracy
• Hardware/software integration
• Contributing to the development of AI software solutions in partnership with internal product teams
• Accomplishing meaningful work that supports Intel business goals in a collaborative environment

AI Algorithm Engineer

• Plan, implement and administer the day-to-day operations of Data and AI Governance program.
• Support administrative functions for governance meetings, prepare materials, record decisions, track/close actions.
• Create and update playbooks, SOPs, decision logs, and audit-ready document repository.
• Confirm deployment readiness, monitor live systems, manage retirements, and maintain a central inventory and dashboard.
• Enforce internal AI policy, track local/state/federal AI rules and guidance, and support incidents, remediation, and audits.
• Gather adoption and outcome metrics and produce dashboards and one-page value summaries for leadership.
• Align internal and external messaging, prepare executive briefs, and support stakeholder and executive updates.

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