Top Tech Jobs & Startup Jobs in NYC, NY

Lead architecture and implementation of high-performance vector search and semantic retrieval across Azure data products. Mentor engineers, collaborate with research teams, ensure scalability, testability, performance, and production readiness of libraries and embeddings-based solutions.

Drive technical sales for Microsoft’s cloud and AI data platform through demos, proofs of concept, hackathons, and architecture workshops. Advise enterprise customers on secure, scalable database and analytics architectures, migrations, and deployments. Build relationships with platform leaders, resolve technical blockers, collaborate with engineering, and promote Azure data technologies including Fabric, Databricks, Purview, and Azure databases.

Design and implement AI compiler features and optimization passes, using LLVM/MLIR and related tools. Develop code generation for new hardware, optimize AI workloads, and build AI software in C++ and Python. Collaborate with hardware architects and ML developers, define requirements, estimate effort, and drive coding standards and adoption.

Lead hardware strategy, performance architecture, and benchmarking for large-scale Microsoft 365 and AI workloads. Investigate performance, scalability, and efficiency across compute, GPU, memory, storage, networking, and accelerators. Partner with Azure and platform teams, build AI-assisted performance engineering tools, and influence cross-organizational architecture and infrastructure decisions.

Develops and executes business strategies for the GitHub developer business, analyzes market and competitive trends, identifies growth opportunities, creates strategic planning materials, and leads cross-functional initiatives. The role aligns engineering, sales, marketing, finance, business planning, and partner stakeholders while providing data-driven recommendations to senior leadership.

Design and lead Azure infrastructure and application modernization migrations for enterprise and government customers. Architect secure, scalable, reliable solutions; lead proofs of concept and production deployments; troubleshoot technical blockers; and provide technical program management through a factory delivery model. Build customer and partner relationships, drive Azure consumption and adoption, mentor teams, develop reusable intellectual property, and provide feedback to influence Microsoft product improvements.

Leads customer R&D transformation engagements from pre-sales through delivery. Shapes scientific and technical challenges into AI architectures, product backlogs, MVPs, simulations, integrations, data pipelines, and production solutions. Partners with executives, researchers, engineers, product teams, and account teams to define outcomes, scope delivery, assess data and compute needs, and establish roadmaps. Develops reusable architectures and delivery playbooks while influencing Microsoft Discovery product direction and scaling AI-powered R&D solutions across industries.

Design and implement high-performance networking infrastructure for large-scale AI training on Azure. Lead architecture, produce secure, maintainable code, define test strategies, use AI tools for development and automation, review code, and collaborate across teams to ensure performance, scalability, reliability, and observability.

Lead complex R&D and scientific AI engagements from pre-sales through production delivery. Partner with executives, researchers, engineers, and product teams to define use cases, architectures, backlogs, MVPs, simulations, integrations, data pipelines, and success metrics. Assess technical feasibility, coordinate multidisciplinary workstreams, and develop reusable architectures and delivery playbooks. Apply AI, machine learning, cloud computing, scientific workflows, and HPC to transform research processes while influencing product roadmaps and scaling solutions across industries.

Own forecasting, calibration, and stochastic optimization for AI economics and FinOps decisions. Develop probabilistic cost, demand, latency, quality, and delivery models; apply risk measures, causal inference, conformal prediction, Monte Carlo simulation, and scenario analysis. Deploy and monitor production-quality Python models using MLOps practices, partnering with engineering teams on telemetry and data lineage. Translate uncertain model outputs into customer recommendations and reusable analytical methods.