Support the DOE-funded HyperFORMANCE project by developing and applying global AI/ML Earth system emulators and regional downscaling techniques to generate large ensembles of physically credible extreme weather scenarios for water and energy system risk assessment.
Responsibilities
Evaluate where global AI/ML emulators credibly reproduce extreme weather characteristics and where physics-based models remain necessary
Combine physics-based simulations, existing model archives, and certified AI/ML emulation to build libraries of decision-relevant extreme events
Develop and apply machine learning clustering techniques to distill large event libraries into representative scenario sets and event archetypes
Generate large ensembles of spatially coherent, temporally compounded hazard scenarios at testbed-relevant scales
Work with hydrologic, energy, and urban impacts teams to ensure scenarios are suitable for downstream applications
Requirements
Ph.D. in atmospheric science, Earth science, computer science, physics, applied mathematics, or closely related field
Nice to have: physics-based and/or data-driven climate/weather models
Independent staff scientists to advance the use of quantum algorithms such as Quantum Phase Estimation (QPE), Variational Quantum Eigensolver (VQE), Quantum Adiabatic Evolution & Optimization, and Quantum Machine Learning Algorithms (QML) to accelerate the discovery of new materials and molecules.
Compensation
$65,122.00 - $150,000 (salary will be assessed based on skills and experience)
Responsibilities
Quantum simulation and quantum AI algorithm tool development
Working with the AC community, including faculty and partners, to determine how quantum or quantum-inspired tools can increase the capabilities of the AC’s SDLs or materials discovery campaigns
Working independently to develop research programs that leverage the AC’s SDLs and support the research objectives of AC faculty and industry partners
Working with the AC software engineering team to create software packages to deploy quantum computational tools
Managing the research and development projects of AC’s industry partners when implemented in AC labs
Staff Hardware Enablement Engineer to help bring vision to new accelerator platforms, working across the Modular software stack from Mojo kernels and compiler infrastructure to the graph compiler, runtime, and MAX model serving.
Locations (2)
Los Altos, California, United States
Edinburgh, Scotland, United Kingdom
Compensation
The estimated base salary range for this role to be performed in the US, regardless of the state, is $180,000.00 - $270,000.00 USD.
The estimated base salary range for this role to be performed in Canada, regardless of the province, is $172,400.00–$258,600.00 CAD.
The estimated base salary range for this role to be performed in the United Kingdom is £99,600.00 – £150,000.00 GBR.
Responsibilities
Bring up and validate support for new hardware architectures across the Modular software stack
Write, port, and optimize Mojo kernels for novel accelerator architectures
Investigate performance and correctness issues across layers of the stack
Develop a detailed understanding of new hardware platforms
Help map important AI operators and workloads onto new architectures
Lead the Hardware Bringup team, owning the end-to-end delivery of accelerator bring-up across the Modular stack from Mojo kernels and the graph compiler to MAX model serving across multiple concurrent hardware partnerships.
Locations (2)
Los Altos, California, United States
Edinburgh, Scotland, United Kingdom
Compensation
The estimated base salary range for this role to be performed in the US, regardless of the state, is $222,500–$333,700 USD.
The estimated base salary range for this role to be performed in Canada, regardless of the province, is $207,000–$310,000 CAD.
The estimated base salary range for this role to be performed in Edinburgh is £135,800–£206,400 GBR.
Responsibilities
Lead, mentor, and grow a 2 to 7 people team of mixed skillset (compiler, kernel, and runtime engineers) focused on bringing up new accelerator architectures across the Modular stack.
Participate directly in design reviews, contribute to architecture and prototypes, write and review code for non-trivial portions of the stack (Mojo kernels, MLIR passes, runtime integration).
Support end-to-end delivery of one or more hardware platform enablement efforts, from initial viability and dev-environment bring-up through merged compiler integration, kernel development, MAX serving, and production-quality model deployment.
Applied Scientist, Ads Brand Safety and Suitability
As an Applied Scientist on the Search L1 Ranking team, you will build search ranking models that work for thousands of product types, billions of queries, and hundreds of millions of customers spread around the world. This role focuses on efficient models for early stage ranking phase, and techniques to personalize these models and optimize them automatically.
Locations (2)
Seattle, Washington, United States
Palo Alto, California, United States
Compensation
USA, CA, Palo Alto - 171,600.00 - 222,200.00 USD annually
USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually
Responsibilities
Build search ranking models for thousands of product types and billions of queries
Find next set of big improvements to ranking
Leverage large datasets to understand customer behavior
Annapurna Labs is looking for an experienced Engineering Manager to think big in defining the strategic direction, roadmap, and execution for EDA infrastructure supporting SoC, IP, physical design, and verification teams.
Compensation
USA, TX, Austin - 184,900.00 - 250,200.00 USD annually
Responsibilities
Build, inspire, and develop a high-performing, globally distributed team of 6–10 software development engineers and systems engineers; set clear expectations, conduct regular performance reviews, and foster a culture of ownership, operational excellence, and customer obsession.
Recruit, hire, and retain top engineering talent; own the hiring bar for your team and actively build a diverse pipeline of candidates who strengthen the organization.
Coach and develop engineers at all levels; create individualized growth plans, identify future leaders, and invest in the career development of every team member to build the next generation of engineering leaders.
Define the team’s vision, strategy, and multi-year roadmap for EDA infrastructure; align priorities with organizational goals and communicate the strategy clearly to stakeholders at all levels.
Define and track success metrics for the team and the services it provides, using data-driven dashboards and AI-assisted analytics to inform priorities, measure impact, and hold the team accountable to results.
Requirements
Bachelor's degree or above in computer science, electrical engineering, or related field
Amazon.com’s Product Detail Page team is looking for a talented applied scientist to design and develop scalable multi-tiered shopping application features to enhance the online shopping experience worldwide.
Responsibilities
Develop next-generation optimization algorithms for Detail Page
Identify business opportunities and propose new features
Design and enforce company-wide technical standards
As a senior member of the LLM inference framework team, you will be responsible for building and optimizing production-grade single-node and distributed inference runtimes for large language models on AMD GPUs.
Responsibilities
Architect and optimize distributed LLM inference runtimes based on in-house LLM engines or open-source stacks such as vLLM, SGLang, and llm-d
Design and improve TP / PP / EP (MoE) hybrid execution, including KV-cache management, attention dispatch, and token scheduling
Implement and optimize multi-node inference pipelines using RCCL, RDMA, and collective-based execution
Drive throughput, latency, and memory efficiency across single-GPU and multi-GPU clusters
Contribute to Statement of Work (SOW) scoping, milestone definition, and acceptance criteria with hardware partners, including translating engineering plans into contractual milestones, negotiating timelines, and managing delivery against commercial commitments.
Partner with Kernels, Graph Compiler, MAX, and Cloud teams to align roadmaps, resolve cross-team dependencies, and produce credible FTE plans and forecasts for executive review.
Requirements
Bachelor's degree in Engineering, Information Systems, Computer Engineering, Electrical Engineering, Computer Science, or related field
Master's degree in Engineering, Information Systems, Computer Engineering, Electrical Engineering, Computer Science, or related field
PhD in Engineering, Information Systems, Computer Engineering, Electrical Engineering, Computer Science, or related field
health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
Health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
Staff Scientist (Quantum for AI-Accelerated Materials Discovery)NewUniversity of Toronto · Toronto, Canada
Staff Engineer, Hardware EnablementNewModular · Los Altos, US+1 more
Applied Scientist, Ads Brand Safety and SuitabilityNewAmazon · London, United Kingdom
AI Framework Eng.(VLLM CI)Advanced Micro Devices, Inc · Shanghai, China