Top Tech Jobs & Startup Jobs in Boston, MA

Design and implement LLM-first APIs and documentation for CUDA-X math libraries. Develop tools, metrics, and processes to improve AI-agent code generation and transform library APIs into LLM-friendly formats. Collaborate with stakeholders on LLM-enhanced releases while prototyping solutions for code-generation platforms. Requires expertise in LLMs, fine-tuning, reinforcement learning, RAG, MCP, agent tooling, composable API design, and technical documentation.

Design and develop next-generation scale-up networking fabrics for AI data centers. Lead collaborative development across hardware, software, and applications to improve scalability, reliability, and robustness for large-model training. Create innovative concepts through production implementation, collaborate with cross-functional teams, and lead complex projects delivering large-scale networking and AI solutions.

Lead relationships with major research universities, advising institutional leaders, research computing teams, and principal investigators on NVIDIA technologies. Drive business growth, revenue forecasting, go-to-market strategy, platform adoption, and ecosystem enablement. Build partnerships across scientific disciplines, coordinate internal technical and product teams, communicate technology roadmaps, and develop expertise in emerging computational methods. Travel approximately 25% or more and support institutions in the Northeast or Mid-Atlantic regions.

Manage strategic state and local government accounts for NVIDIA’s AI and accelerated computing solutions. Own the full technology sales cycle, develop territory and account plans, build executive and technical relationships, collaborate with solution architects and channel partners, navigate public-sector procurement, forecast revenue, and achieve sales quotas. The role requires consultative selling to government organizations and travel of at least 25%.

Lead an engineering team responsible for reliability processes and platforms, including incident management, maintenance, on-call, issue management, and production readiness. Own the roadmap, technical direction, delivery, adoption, and continuous improvement of operational systems. Partner across infrastructure, product, and security teams; lead major incident response; hire and develop engineers; and apply automation, AI, and operational insights to improve service reliability and reduce manual work.

Develop scalable distributed storage management systems for NVIDIA’s large-scale engineering infrastructure. Collaborate with hardware and software teams to define features, optimize workflows, improve software design and testing, and deliver reliable storage solutions. The role requires Linux, storage systems, networked distributed systems, Perl, and Python expertise, with responsibility for architectural decisions balancing cost, performance, reliability, and scalability.

Develop and support NVIDIA CUDA Math Libraries across emerging and specialized platforms. Responsibilities include platform integration, functional qualification, defect triage, bug fixing, performance benchmarking, regression resolution, and building CI/CD qualification pipelines. The role requires high-performance numerical software development using C++ and Python, parallel computing, numerical methods, and software architecture, with preferred experience in CUDA, GPU computing, math libraries, cross-platform development, and embedded or safety-critical systems.

Lead NVIDIA’s Federal and SLED General Accounts sales team, managing Account Managers across government, higher education, and research customers. Develop go-to-market strategy, pipeline, demand generation, forecasting, revenue performance, and regional growth. Build executive relationships and coordinate technical, product, marketing, developer, and partner resources. The role requires extensive public-sector technology sales experience, leadership capability, knowledge of government procurement and buying cycles, and the ability to sell advanced AI, accelerated computing, and infrastructure solutions.

Develop and productize machine learning models for autonomous vehicle perception, including LiDAR and camera perception, sensor fusion, detection, classification, segmentation, and tracking. Build scalable data, training, metrics, and performance workflows, and implement efficient C++ code for the NVIDIA DRIVE AV platform. Collaborate across engineering teams, track advances in machine learning, and optimize models for real-time embedded inference.

Design and guide security architecture for NVIDIA networking firmware and embedded subsystems. Responsibilities include threat modeling, analyzing firmware and hardware interactions, defining lifecycle security features, translating threats into requirements, partnering with hardware architects, and driving secure firmware development practices such as memory-safe C, static analysis, and fuzzing. The role requires expertise in firmware security, authorization models, cryptography, secure boot, attestation, and embedded systems.