Lead design and operation of production systems for real‑time forecasting and optimization in Lyft’s marketplace.
Compensation
The expected base pay range for this position in the Seattle area is $136,160 - $170,200, not inclusive of potential equity offering, bonus or benefits.
Responsibilities
Lead projects end to end
Architect ML systems
Partner with science to ship
Own signal contracts
Operate what you build
Requirements
BS/MS or equivalent in Computer Engineering, Computer Science, or related field or relevant work experience
AI Research Engineer focused on building systems around research models, defining the AI roadmap, building retrieval, memory, and evaluation systems, and shipping them into production.
AMD is seeking a Principal Software Engineer, Agentic AI to rapidly build and prove new AI software opportunities, and to help our organization stay at the leading edge of agentic software development.
Locations (4)
Austin, Texas, United States
Bellevue, Washington, United States
Seattle, Washington, United States
Santa Clara, California, United States
Responsibilities
Rapidly build prototypes and proofs of concept using agentic development workflows.
Optimize prototypes on AMD platforms, going down the stack when needed.
Work directly with partner engineering teams on joint technical work.
Lead technical engagement with Nebius' most strategic customers across the Gulf region, shaping solution strategy, driving complex PoCs, and ensuring successful adoption of Nebius AI Cloud for mission‑critical AI/ML workloads.
Responsibilities
Own end‑to‑end technical presales and solution delivery for strategic and enterprise customers across the Middle East
Serve as a trusted technical advisor to senior customer stakeholders
Lead complex ML/AI infrastructure and MLOps architectures
Design, validate, and document reference architectures and Infrastructure‑as‑Code solutions
Drive and execute advanced PoCs, workshops, architecture reviews, and executive‑level presentations
Manage and develop an assigned portfolio of AI-native companies, startups, and developer platforms to grow relationships and drive revenue on NVIDIA platforms.
Compensation
Your cash compensation will be determined based on your location, experience, and other objective job-related factors, with 85% paid through base salary and 15% through variable compensation. The expected cash compensation range for this position is 120,000 USD – 189,750 USD.
Responsibilities
Achieve 100% of assigned quota through pipeline creation, opportunity progression, qualified customer engagement, and revenue-generating activity
Manage and develop an assigned portfolio of emerging, underserved, whitespace, startup, Inception, and other net-new accounts by creating account plans, prioritizing opportunities, mapping stakeholders, and executing focused outreach
Build and progress pipeline across the complete sales cycle, including prospecting, discovery, solution positioning, proposals, pricing coordination, internal approvals, negotiation, contracting support, forecasting, and closing activities
Engage founders, developers, and technical leaders to uncover model development, training, inference, data, and infrastructure requirements
Lead business and technical discovery, including workload identification, solution mapping, preliminary sizing, technical validation, demonstrations, and proof-of-concept coordination
Enable financial transparency, accountability, and optimization across cloud, on-premises, and AI services.
Responsibilities
Drive cost optimization across cloud, on-premises, SaaS, and AI services
Establish FinOps for AI practices covering cost visibility, allocation, forecasting, budgeting, anomaly detection, and AI business value tracking
Proactively equip, guide, and train stakeholders on FinOps, cloud optimization, and AI cost management practices
Ensure reliable cost management tooling, including access control, data quality, cost mapping, tagging/labeling standards, dashboards, reporting, and vendor engagement
Identify, analyze, and report cost opportunities and risks across Azure, Google Cloud, on-premises, and AI consumption
Requirements
Bachelor's or Master's degree in System Engineering, Business Management, Data Analytics / Data Science, Finance, IT, or equivalent experience
Nice to have: AI cost drivers such as tokens, API calls, model selection, inference, training, GPU/TPU usage, vector databases, SaaS AI licensing, shared AI platforms
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