Artificial Intelligence

• Run daily dispatch execution from our playbook: load assignment, driver communication, check-calls, and exception handling
• Keep loads covered and customers informed, escalating with judgment when something can’t wait
• Use AI tools daily to research, troubleshoot, draft, analyze, and automate your own work
• Improve the playbook as you go — document what works, flag what doesn’t, and ship a better process every week
• Call out problems early and come with proposed solutions, not just escalations
• Partner with engineering and product to turn recurring manual work into automated workflows
• Track the KPIs of your operation — load coverage, on-time performance, driver utilization — and use them to improve

AI Operations Associate

• Design, develop, test, and deploy features that directly impact CrowdStrike products
• Collaborate with engineers, data scientists, and product teams to build scalable backend systems and AI-powered capabilities
• Deploy machine learning techniques to develop highly accurate models
• Solve complex technical problems with a high degree of ownership and autonomy
• Build and maintain reliable APIs, data pipelines, and backend services
• Apply machine learning and AI technologies to improve product capabilities and user experiences
• Develop automated tests and contribute to overall software quality and reliability
• Participate actively in architecture, design, and code reviews
• Continuously learn new technologies, industry trends, and emerging research relevant to AI and cybersecurity
• Contribute ideas and innovations in a fast-paced, customer-focused environment

Engineer I, Agentic AI

• Work collaboratively with our team to design and implement experiments measuring cardiovascular dynamics (heart rate variability, PPG) and autonomic physiology (EDA) during media consumption across different content types and viewing contexts.
• Develop EEG-based neural signature models for media components and events combining naturalistic media stimuli with AI-based content analysis.
• Create biosignal transfer learning approaches that establish robust mappings between high-fidelity neural signatures and accessible physiological measures from consumer wearable devices.
• Build foundation models for physiological data representation that can generalize across individuals, devices, and measurement contexts to enable scalable engagement prediction systems.
• Implement temporal engagement models to predict user state trajectories and optimize content adaptation timing for sustained engagement across diverse media experiences.
• Develop multimodal AI systems that integrate physiological signals, content features, and contextual information to predict and enhance user engagement in real-time media applications.
• Leverage large-scale physiological datasets to train foundation models that capture universal patterns in human engagement responses while preserving individual personalization capabilities.
• Contribute to the development of research papers, patents, and technical presentations advancing the field of AI-driven and engagement-aware media systems.

PhD Research Intern – Neuroscience, AI

• Maintain strong awareness of the rapidly evolving AI ecosystem
• Translate new AI capabilities into practical engineering patterns
• Accelerate adoption of the enterprise AI platform across engineering teams
• Ensure experimentation leads to reusable capabilities rather than isolated prototypes
• Provide a clear feedback loop to the AI Platform organization on emerging needs
• Continuously evaluate emerging AI technologies and techniques that can meaningfully improve enterprise AI systems
• Develop prototypes and reference implementations that validate new AI approaches
• Translate experimentation into practical assets that accelerate the development of production AI systems across the organization
• Help engineering teams adopt AI capabilities effectively

VP, AI Innovation – Enablement

• Analyze system bandwidth, latency, and compute bottlenecks
• Model performance across CPU/GPU/NPU for AI workloads
• Simulate workload execution (PnP tradeoffs, resource contention)
• Optimize model execution (initialization time, caching, KV reuse)
• Drive HW/SW co-design (memory, caching, scheduling)
• Partner with ML teams to align model structure with system architecture
• Develops technology roadmaps for electrical/hardware engineering team; identifies gaps and solutions for future growth.
• Communicates strategy and technology roadmaps to executive staff, industry partners and customers.
• Provides innovative solutions and integrates advanced technologies, theories, or techniques with a significant impact across disciplines, championing large projects for long-range goals.
• Provides consultation, design input, and feedback for new product development and design reviews across multiple organizations and architectures.
• Works closely with product management, engineering stakeholders from related departments and empowers the teams to deliver outstanding and impactful results.
• Initiates industry-leading research and development, serving as a recognized lead authority internally and externally.
• Provides mentoring and guidance to less-experienced staff members to set an example of electrical hardware design and development, innovation, and excellence.
• Participates in and provides input on process for selection of future technical leaders.

Distinguished Technologist – AI Model Performance Architect

• Identify opportunities where AI can improve repeatable marketing tasks improving the quality, speed, scale, and effectiveness of marketing.
• Scope ambiguous problems, prioritize opportunities, and drive execution from prototype to production.
• Design and deploy workflows that combine agents, tools, and human review in production environments.
• Rapidly prototype and ship using automation platforms, our API platform, and Codex.
• Build and maintain a small set of centrally managed workflows that support critical marketing programs.
• Develop reusable templates, tooling, and documentation that allow teams to safely self-serve over time.
• Help operators adopt frontier capabilities through clear documentation, hands-on training, programming, and direct collaboration.
• Partner with Engineering, IT, Security, Legal, and Privacy to deploy tools and workflows that meet security and compliance expectations.
• Act as a hands-on project manager and builder, driving cross-functional projects from idea to MVP to scaled adoption.

AI Tooling & Enablement Manager

• Build and fine‑tune models for detection, tracking, segmentation (2D/3D), pose & activity recognition, and scene understanding (incl. 360° and multi‑view)
• Train/evaluate vision–language models (VLMs) for grounding, dense captioning, temporal QA, and tool‑use; design retrieval‑augmented and agentic loops for perception‑action tasks
• Prototype perception‑in‑the‑loop policies that close the gap from pixels to actions (simulation + real data)
• Curate datasets, author high‑signal evaluation protocols/KPIs, and run ablations that make results irreproducible impossible
• Package research into reliable services on a modern stack (Kubernetes, Docker, Ray, FastAPI), with profiling, telemetry, and CI for reproducible science
• Orchestrate multi‑agent pipelines (e.g., LangGraph‑style graphs) that combine perception, reasoning, simulation, and code‑generation to self‑check and self‑correct

Vision/Physical AI Intern

• 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

• Define and evolve reference architectures for AI, machine learning, and generative AI systems
• Establish technical standards, patterns, and best practices for scalable and secure AI development
• Guide platform decisions across data, model development, and deployment infrastructure
• Evaluate and recommend tools, frameworks, and vendors aligned with long term architectural direction
• Design and implement end to end AI workflows for complex data interpretation, knowledge extraction, and reasoning
• Build systems that convert unstructured content into structured representations
• Develop and optimize retrieval approaches that combine semantic and lexical search techniques
• Prototype, evaluate approaches, test feasibility, and productionize generative AI solutions for real world use cases
• Partner with engineering teams to develop technology infrastructure
• Optimize systems for latency, cost, and throughput
• Design and implement evaluation approaches to assess performance, relevance, and effectiveness of AI solutions
• Apply techniques to improve consistency, accuracy, and trustworthiness of outputs
• Implement methods for scoring, explainability, and ongoing quality assurance
• Monitor deployed solutions and data pipelines for performance, stability, and data quality over time
• Incorporate responsible AI practices including transparency, bias mitigation, and risk controls
• Work closely with cross functional team to align data science initiatives with business priorities
• Partner with leadership to identify and prioritize high impact opportunities for data science applications
• Present insights and recommendations through clear visualizations tailored for audiences across different roles and expertise levels

AI Architect

• Adopt and run AV models to support GM’s long-term GPU system strategy and 'evergreen' infrastructure roadmap.
• Conduct deep-dive analyses of production workloads to identify bottlenecks and propose high-impact optimization strategies.
• Partner with AI/ML Research, Infrastructure Engineering, and Cloud Vendors to spearhead projects that enhance engineering velocity and cost-efficiency.
• Identify opportunities for architectural improvements to ensure the scalability and reliability of large-scale ML training and inference environments.

Senior AI/ML Performance Engineer

• Lead enterprise AI strategy and digital delivery for HR and workforce systems
• Oversee the implementation and optimization of people systems
• Champion the use of AI and advanced analytics to streamline workforce management
• Develop and execute a forward-looking AI-enabled workforce strategy
• Lead transformation initiatives that enable intelligent, data-driven, and digital-first operating models
• Deliver actionable insights into workforce trends, performance, and behaviors

Senior Director, AI, Workforce Innovation, Digital Delivery

• Drive DRAM layout activities for tightly coupled, high-performance memory for AI workloads
• Apply expert knowledge to deliver high-quality layout designs across analog, mixed-signal, and custom digital domains
• Drive layout designs for early architecture concepts to demonstrate feasibility and identify optimizations
• Collaborate and communicate across domains to ensure successful architecture exploration

Staff/Principal Layout Designer, AI-Optimized Memory

• Architect, design, and implement optimizations for AI model execution on graph compilers to improve performance, reduce latency, and maximize hardware utilization.
• Work closely with ML researchers, hardware engineers, and software developers to design and deploy AI models, understanding and addressing hardware-specific challenges.
• Work on performance optimizations for neural network models, such as layer fusion, operator fusion, and graph-level transformations.
• Develop compiler optimizations and passes that convert high-level AI models (e.g., from TensorFlow, PyTorch) into intermediate representations (IR).
• Implement parsing, semantic analysis, and IR generation for deep learning frameworks.
• Research and integrate the latest advancements in compiler design, ML model optimizations, and hardware acceleration into graph compilers.
• Provide leadership, mentorship, and technical guidance to a team of engineers focused on graph compiler optimizations.

AI Compiler Engineer

• 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

• Support enterprise-wide change management initiatives focused on AI adoption, workforce transformation, and organizational effectiveness.
• Partner with stakeholders to develop and execute change strategies, communications, adoption plans, and enablement materials that drive successful implementation of AI-related initiatives.
• Develop and execute change management plans, communications, stakeholder engagement strategies, and adoption materials for AI transformation initiatives.
• Support programs related to AI policy, governance, enterprise capability-building, and workforce transformation.
• Create business-friendly content that translates complex AI and technical concepts into clear, actionable messaging.
• Enable AI Champion networks through targeted communications, training materials, and adoption tools.
• Coach and support change managers to ensure consistent application of change management methodologies across AI initiatives.
• Support change adoption metrics, dashboard reporting, and data collection activities.
• Collaborate with cross-functional stakeholders to drive engagement, readiness, and sustained adoption of new ways of working.

Change Management Lead – AI Workforce Transformation

• Testing and improving Spanish-language voice AI agents
• Providing customer support for live deployments during US business hours (Pacific Time)
• Review call recordings, transcripts, and AI interactions
• Identify language, pronunciation, conversational, and workflow issues
• Provide structured feedback to improve AI agent performance
• Support dedicated customers through email, tickets, and operational requests
• Troubleshoot basic issues and coordinate with internal teams for resolution
• Track customer feedback and communicate product improvement opportunities
• Help maintain a high-quality experience for live customers

Spanish Conversational-AI Specialist

• Conduct deep-dive analysis into LLM architectures and agentic frameworks to identify potential security weaknesses, including risks associated with prompt injection and safety filter bypasses.
• Evaluate the security boundaries in Agent-to-LLM and Agent-to-Application interactions, focusing on how autonomous loops and multi-step reasoning processes can be secured against manipulation.
• Develop methodologies to test the robustness of RAG (Retrieval-Augmented Generation) pipelines and third-party tool integrations, ensuring resilience against adversarial inputs.
• Map research findings and defensive strategies to industry standards, such as the MITRE ATLAS framework and the OWASP Top 10 for LLM Applications.
• Contribute to the security community by publishing whitepapers or presenting research on the safety and security challenges of AI agents at industry conferences.

Senior AI/LLM Threat Researcher – AI Detection and Response

• Lead delivery of CHAI’s AI clinical decision support and agentic healthcare delivery initiatives end-to-end
• Own the program's forward view: track the frontier of clinical and agentic AI
• Forge and maintain relationships with the Ministry of Health
• Evaluate adjacent health system factors affecting initiative’s impact
• Directly manage at least one Senior Associate / Manager / Sr. Manager
• Own status reporting and stakeholder communications for senior CHAI leadership and external partners
• Anticipate and prepare for scale: regulatory pathways, infrastructure dependencies, workforce readiness
• Provide informed technical support as needed
• Travel internationally as needed (25-40% annually) and regularly within the US

Associate Director – AI Clinical Decision Support

• Build and evolve TENEX's internal AI platform: a reusable foundation of tools, custom skills, Claude Projects, integrations, and department-specific specialists that scale AI impact across every team.
• Partner with teams across TENEX (Sales, Customer Success, Marketing, Finance, Legal, HR, Detection Engineering, Security Operations, Forward-Deployed Engineering) to surface needs that shape our internal AI platform roadmap.
• Own and execute against a published roadmap. Predictable, visible execution is a core expectation of this role.
• Maintain, expand, and refine our AI work companion based on user feedback and emerging needs.
• Build integrations between Claude and our internal tools (e.g., Ashby, Wonderlic, Fireflies, Asana, Google Chat) using webhooks, MCP connectors, and APIs. Favor reusable patterns over one-off solutions.
• Run enablement: training sessions, documentation, AMAs, and 1:1 onboarding to help every TENEX'er become more AI-native.
• Contribute to AI governance, monitoring, and security tooling decisions as TENEX scales its AI usage.

AI Enablement Engineer

• Own the client-CEO relationship and the executive steering committee — as a peer, with real decision rights.
• Run the transformation cadence: weekly reviews, monthly finance-verified value reviews, and quarterly business reviews.
• Drive a fast, fixed-scope first win on the client's real data, then lead the full rollout (leadership first, then the wider team).
• Translate client problems into crisp, build-ready specs for remote engineers; own adoption and sign-off.
• Be personally accountable for a finance-verified outcome the client can see in their P&L.
• Build a portfolio of 2–3 marquee CEO accounts as each engagement matures.
• Own end-to-end rollout management — from kick-off through adoption, sign-off, and handover — ensuring every phase lands on time and to a measurable outcome.
• Codify the method after each deployment so the next one requires less ramp-up.

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