• Optimize training and multi-node inference across next-gen networking silicon and systems • Partner with architecture, firmware, software, and lighthouse customers • Own end-to-end performance for distributed AI workloads • Benchmark, characterize, and tune open-source & industry workloads • Design and optimize distributed serving topologies • Build crisp proof points that compare Cornelis Omni-Path to competing interconnects • Evangelize best practices on distributed inference patterns and anti-patterns

• You will design and maintain automated pipelines that ingest, clean, and normalize: • News, filings, earnings calls, macro releases • Social and sentiment data • Alternative and proprietary datasets • Your job is to replace manual refresh workflows with push-based alerts that surface only what matters — mapped directly to: • Watchlists • Live positions • Risk exposure • Reliability matters. Latency matters. Silence matters. • You will deploy LLM-powered systems to summarize, extract, compare, and reason over: • 10-Ks, 10-Qs, earnings calls, central bank minutes • Sell-side research and internal notes • You will build “chat with our data” tools that allow traders to query proprietary research in natural language. • You will also create tools for fast discretionary back-testing: • How did this asset behave during the last three macro shocks of this type? • You will build the filters that decide what breaks through. • Sentiment and relevance scoring • Entity recognition that maps events to exposure • Dashboards that surface regimes, anomalies, and dislocations — not vanity metrics • If something matters, it should scream. If it doesn’t, it should disappear. • You own the compute layer that runs the intelligence system. • Cloud and/or local GPU infrastructure • Vector databases and retrieval systems • Data-privacy-first architectures (local models where required) • You choose the architecture. You ship what runs fastest and breaks least. Our proprietary data and strategies do not leak. Ever.

• Pick one specific growth wedge and own it end-to-end (programmatic SEO, paid ads, lifecycle, etc.) • Build an AI-driven system to execute it at scale. • Run tight loops: ship, measure, learn, repeat. • Turn what works into durable infrastructure we keep scaling.

• Own the technical vision and strategy for AI across the Asteri platform • Define and evolve Asteri’s approach to LLM-based systems, including retrieval, agentic workflows, and evaluation • Establish standards for AI quality, reliability, safety, and performance in production • Partner closely with the VP of Engineering to ensure AI systems are delivered, operated, and scaled reliably • Lead the design, implementation, and continuous improvement of AI architectures and pipelines • Define evaluation frameworks and metrics to measure AI accuracy, usefulness, and business impact • Guide decisions around model selection, prompting, fine-tuning, routing, and cost-performance tradeoffs • Stay current with advances in applied AI and translate relevant research into practical product improvements • Hire, mentor, and lead AI engineers and applied researchers as the team grows • Support enterprise customer conversations around AI behavior, reliability, and system design when needed