• Lead, coach, and develop a team of AI and Experience Orchestration Directors (individual contributors) operating as specialist overlay sellers • Establish clear operating models that define how AI specialists work with and through core Account Executives on accounts and opportunities – as well as drive autonomous activities and sales cycles in accounts where appropriate • Drive consistent execution of AI focused sales motions that support pipeline and revenue goals • Partner with subregional direct sales leadership to align AI specialist coverage, priorities, and engagement models • Ensure AI specialists effectively translate customer business challenges into AI-driven use cases, measurable outcomes, and value narratives • Model effective executive presence, thought leadership, and consultative selling techniques in customer and internal engagements, setting the standard for specialist sellers • Support complex, strategic enterprise opportunities by engaging with senior customer stakeholders alongside Genesys sales leadership and specialist sellers • Monitor AI market trends, competitive dynamics, and customer feedback to continuously evolve specialist enablement, messaging, and sales approach • Collaborate cross functionally with Product, Marketing, Enablement, Customer Success, and Professional Services to bring customer insights back into the organization –leveling up the field and continuously improving Genesys go-to-market effectiveness

• 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.

• Build and maintain AI-powered internal tools, web applications, and automation systems. • Develop end-to-end solutions, including frontend, backend, databases, and API integrations. • Integrate LLMs such as Claude, GPT, and Anthropic APIs into real-world business workflows. • Create AI pipelines involving data scraping, processing, analysis, and structured output generation. • Enhance and maintain existing tools, including Adcubator’s AI landing page builder. • Develop integrations with platforms such as Slack, ClickUp, Google Workspace, and Google Ads API. • Build web scrapers, automation workflows, dashboards, and reporting systems. • Collaborate with marketing, media buying, and creative teams to identify challenges and deliver scalable solutions. • Rapidly prototype, test, and deploy new tools while continuously improving performance and user experience.

• Review oncology patient records to determine whether cases meet clinical and payer policy requirements. • Serve as an escalation point for complex cases that cannot be accurately classified by AI workflows. • Analyze challenging clinical scenarios, including patients with multiple cancer diagnoses, complex treatment histories, and ambiguous documentation. • Document findings and recommendations within internal systems including Salesforce, LIMS, and other workflow tools. • Ensure accuracy and consistency of clinical review outcomes. • Perform ongoing audits of AI-generated clinical determinations and data classifications. • Evaluate AI performance against established clinical review standards. • Identify errors, trends, and opportunities to improve automation accuracy. • Participate in validation activities for new AI models, workflows, and clinical review processes. • Provide structured feedback to improve future AI performance. • Partner with Product, Engineering, and Data Science teams to improve clinical AI capabilities. • Help define clinical decision logic and requirements used in AI-driven workflows. • Translate clinical review findings into actionable recommendations for automation improvements. • Support testing and implementation of new AI-enabled solutions. • Contribute to the long-term evolution of Natera's oncology clinical intelligence platforms. • Develop expertise in payer coverage policies relevant to our Signatera product and oncology testing. • Evaluate clinical documentation against coverage requirements. • Assist in identifying cases that may require additional clinical review or reimbursement support. • Stay current on evolving oncology standards, treatment pathways, and reimbursement trends.