Top Tech Jobs & Startup Jobs

Design and build software tooling, firmware, low-level drivers, BSPs, infrastructure, and validation systems supporting semiconductor design, verification, manufacturing, and post-silicon testing. Partner with hardware and EDA teams, develop scalable compute and data pipelines, optimize silicon workflows, lead technical reviews, document systems, evaluate toolchains, and mentor engineers.

Define and govern enterprise DevOps, DevSecOps, platform engineering, and observability standards. Establish CI/CD, infrastructure-as-code, Kubernetes, GitOps, security, testing, release management, telemetry, SLI/SLO, alerting, and dashboard standards. Evaluate technologies, lead proof-of-concepts, create reusable Terraform modules and CI/CD templates, and drive adoption across engineering, security, cloud, operations, and AIOps teams.

Designs and optimizes high-performance GPU workloads for AI, HPC, and data-processing applications. Responsibilities include developing CUDA kernels, profiling GPU code, tuning memory and execution performance, optimizing distributed multi-GPU systems, integrating custom operators into ML frameworks, and contributing to compiler-level optimizations. The role also develops benchmarks, evaluates GPU architectures, documents performance practices, and mentors engineers.

Design, evaluate, and deploy applied AI and machine learning systems across language, vision, recommendation, and structured data. Build scalable training and inference pipelines, optimize models for production, develop data-quality and monitoring tools, and apply rigorous evaluation, safety, fairness, and reliability practices. Collaborate with product and engineering teams, document research, mentor engineers, and influence the AI roadmap through research insights and shipped solutions.

Lead a team of 8–10 engineers and technicians supporting enterprise Windows endpoints. Oversee Windows engineering, endpoint management, security, automation, modernization, patching, compliance, migrations, VDI, and troubleshooting across thousands of devices. Drive testing, proof-of-concept initiatives, vendor evaluations, and endpoint security improvements while providing technical leadership and managing enterprise platforms including SCCM/MECM, Intune, PowerShell, Python, and virtual desktop technologies.

Designs and operates SAP security, governance, and access-control frameworks across complex SAP landscapes. Responsibilities include SAP role design, user provisioning, segregation-of-duties analysis, GRC Access Control operation, audit support, remediation, and securing Fiori, BTP, and cloud SAP applications. The role also supports identity governance integrations, Process Control, regulatory compliance, continuous controls monitoring, and SAP cloud security operating models.

Design, build, and operate SAP BTP extensions, side-by-side applications, and integrations for SAP S/4HANA and other SAP applications. Develop clean-core solutions using CAP with Node.js or Java, SAP Fiori/UI5, OData, BTP runtimes, authentication services, and CI/CD tooling. Work across application development, integration, data, analytics, and AI while applying SAP extension patterns and clean-core principles.

Develop and support PeopleSoft HCM, FSCM, and Campus Solutions customizations, integrations, workflows, and enhancements. Build Application Engine programs, Component Interfaces, Integration Broker services, SQR, BI Publisher reports, and AWE workflows. Optimize SQL, support upgrades and patches, troubleshoot production issues, collaborate with functional teams, document solutions, and mentor junior developers.

Design, operate, and optimize large-scale AI infrastructure for GPU training and inference. Responsibilities include distributed scheduling, high-performance storage and networking, observability, fault tolerance, security, automation, cost optimization, and developer tooling. The engineer partners with ML research teams on capacity planning and supports hybrid on-premises and cloud environments.

Design, optimize, and deploy machine learning models for resource-constrained edge devices, including mobile and embedded platforms. Responsibilities include model compression, quantization, pruning, hardware-aware optimization, inference runtime development, accelerator tuning, device updates, hybrid edge-cloud architectures, privacy-preserving telemetry, secure execution, benchmarking, and responsible AI evaluation. The role also requires production deployment experience, cross-functional collaboration, and comprehensive technical documentation.