Assist in AI/ML model design, development, training, and testing within a multi‑disciplinary engineering team.
Responsibilities
Gain an understanding of typical AI/ML documentation and assist in preparing them.
Learn commonly used AI and software engineering tools and best practices (e.g., Git, ADO, Python libraries, ML frameworks, VSCode).
Provide basic support for AI and software development across various software environments and target hardware (e.g., Linux, Android, Windows).
Expand your AI/ML knowledge, including model development, data preparation, and evaluation techniques.
Participate as a member of a multi‑disciplinary engineering team.
Requirements
Enrolled in an accredited college or university pursuing a technical degree in Computer Engineering, Electrical Engineering, Computer Science, or a related STEM field
Nice to have: MS Office
Nice to have: Previous engineering or AI/ML project experience or internships
Nice to have: Ability to work effectively in a team environment
Build scalable cloud-based data solutions that enable analytics and data-driven decision-making across the organization.
Locations (3)
Raleigh, North Carolina, United States
Phoenix, Arizona, United States
Dallas, Texas, United States
Compensation
If hired in North Carolina, the base pay for this position is generally between $86,625 and $100,000.
Responsibilities
Design, build, and support scalable data pipelines and cloud-based data solutions utilizing AWS Services, Snowflake, dbt, Python, SQL, and related technologies.
Implement data warehouse solutions, dimensional data models, and ETL/ELT processes that support reporting, analytics, and business intelligence capabilities.
Implement data quality validations, test cases, and controls to ensure accurate, consistent, and reliable data assets.
Gerente responsable de impulsar la adopción responsable de IA y automatización en la DGA de Riesgos de Banco Santander, diseñando y desarrollando soluciones de IA, automatizaciones y BI.
Compensation
Recibe un salario altamente competitivo con bonos basados en el desempeño
Responsibilities
Coordinar reuniones y agendas con equipos multidisciplinarios
Identificar y evaluar casos de uso con áreas solicitantes
Diseñar y desarrollar agentes y soluciones de IA mediante programación e integración de herramientas
Desarrollar automatizaciones y componentes de datos y BI con Python, SQL, ETL, APIs y Power BI
Estructurar y ejecutar proyectos con alcance, prototipos y entregables
Requirements
Licenciatura en Actuaría, Ciencias de la Computación, Ingeniería en Sistemas, Software, Datos o IA; Matemáticas, Estadística o Física
Lead technical planning, design, and execution of utility infrastructure projects, mentor junior engineers, and ensure regulatory compliance.
Compensation
Annual salary will vary based on a candidate’s skills, qualifications, experience, and other factors: $112,000.00/Yr. – $154,000.00/Yr.
Annual Bonus for eligible positions: 15%
Responsibilities
Direct engineering studies and performance evaluations
Set priorities for safety, reliability, and regulatory compliance
Approve budgets, work packages, and technical specifications
Lead cross-functional coordination and external partnerships
Supervise field activities and resolve complex issues
Senior member of the LLM inference framework team 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
Support the validation, testing, and failure analysis of AI NIC, switch, and networking platforms.
Responsibilities
Perform failure analysis on AI NIC, switch, and networking hardware from engineering validation, manufacturing, and field returns.
Investigate and reproduce hardware and system failures, analyze diagnostic logs and test results, and isolate issues across hardware, firmware, and software.
Debug board-level and system-level failures, including boot issues, Ethernet/PCIe connectivity, interface bring-up, power, thermal, and stability problems.
Work with hardware, firmware, software, and manufacturing teams to identify root causes, implement corrective actions, and validate fixes.
Analyze manufacturing test data and failure trends to identify recurring issues, improve test coverage, and reduce failure rates.
Requirements
BS/MS in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
Lead GPU kernel development and performance optimization for large‑model inference scenarios, including GEMM, Attention, MoE, and other core operators.
Responsibilities
Develop GPU kernels for large‑model inference
Optimize kernel performance using tiling, fusion, and other techniques
Automate kernel generation, performance analysis, and tuning
Validate results and analyze bottlenecks
Requirements
Master's or PhD in Computer Science, Software Engineering, Artificial Intelligence, High Performance Computing
Seeking an experienced Standard Cell Design & Characterization Engineer to develop standard cell libraries, support third-party standard cell IP, and enable process characterization infrastructure for all AMD products.
Responsibilities
Design, analyze, and optimize standard cell architectures to achieve performance, power, area, reliability, and manufacturability targets.
Develop circuit-level solutions including transistor sizing, topology optimization, electrical debugging, and design tradeoff analysis.
Drive characterization methodologies for timing, power, noise, variation, reliability, and electromigration modeling.
Generate, validate, and release high-quality library deliverables including Liberty, CCS, LVF, LEF, OA, extracted netlists, and reliability views.
Define characterization requirements, validation methodologies, quality metrics, and signoff criteria.
Requirements
Bachelors or Masters in Electrical Engineering or Computer Science
Nice to have: Experience with Synopsys PrimeLib
Nice to have: Cadence Liberate
Nice to have: Experience across multiple technology generations
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