Top Tech Jobs & Startup Jobs

Own data projects supporting AI researchers, from understanding requirements and sourcing vendors to reviewing quality and delivering data. Build vendor partnerships, investigate new data domains, create scalable processes and tooling, automate repetitive work, and coordinate with legal and technical teams. The role requires strong judgment, communication, initiative, and comfort managing ambiguity and changing research priorities.

Owns the company’s GRC certification roadmap, including SOC 2, ISO 27001, FedRAMP, and related audits from scoping through closure. Manages control testing, evidence collection, risk registers, policy attestations, regulatory requirements, and audit responses. Partners with engineering, security, legal, safety, and product teams to translate technical and regulatory requirements into actionable controls. Builds compliance tooling, documentation, and a multi-quarter roadmap to scale the GRC function.

Owns end-to-end reliability for Tinker, including CI/CD, observability, service-level objectives, incident response, multi-tenant isolation, resource scheduling, and vulnerability remediation. The role designs monitoring for distributed training systems, improves recovery and checkpointing, and operates Kubernetes clusters supporting heterogeneous GPU workloads. It collaborates closely with platform, security, engineering, and research teams to improve production resilience and utilization.

Owns reliability of large-scale GPU network fabrics supporting AI training and inference. Responsibilities include validating fabric designs, debugging RDMA/RoCEv2, NCCL collectives, PFC/ECN, NVLink/NVSwitch, and host-level networking; building instrumentation, dashboards, and alerts; troubleshooting across NICs, drivers, kernels, switches, and workloads; and driving end-to-end escalations with cloud networking providers.

Build and scale infrastructure supporting frontier AI model training, research, and product development. Responsibilities may include operating Kubernetes clusters with GPU workloads, developing data pipelines with Spark, improving developer productivity tooling, debugging across application, operating system, and network layers, and delivering reliable distributed systems end to end.

Build and ship AI-powered products from prototype to production. Develop full-stack web features, including backend services, APIs, and polished frontend interfaces. Collaborate with researchers, product managers, and designers to translate model capabilities into usable experiences. Own technical architecture, scalability, reliability, and maintainability while making pragmatic tradeoffs in ambiguous environments. Help establish engineering practices and standards for a growing product engineering team.

Design, optimize, and scale infrastructure for high-performance AI model inference. The role develops distributed serving systems, improves latency, throughput, reliability, observability, and hardware utilization, and collaborates with research teams to deploy novel architectures. Responsibilities include extending Kubernetes, Ray, and SLURM orchestration, optimizing GPU-based systems, supporting evaluations and production deployments, and sharing infrastructure advancements through documentation, open-source work, or technical reports.

Advance agentic AI capabilities by building synthetic data frameworks, developing task environments for tool use and long-horizon workflows, designing training data and recipes, conducting rigorous ablation experiments, and improving model usability through evaluations and feedback. The role requires strong software engineering, deep learning, distributed training, and scientific experimentation skills, with substantial autonomy across the research and development lifecycle.

Design, implement, and optimize distributed training infrastructure for large-scale AI models across thousands of GPUs and nodes. Build high-performance frameworks and libraries that improve throughput, reliability, reproducibility, and scalability. Collaborate with researchers and engineers, establish system standards, debug complex codebases, and share advances through documentation, open-source projects, or technical reports.

Provides onsite IT support for the New York office, managing employee onboarding and offboarding, identity and access systems, SaaS platforms, macOS devices, office networks, security systems, conference room technology, vendors, and infrastructure projects. Collaborates with the San Francisco IT and Security team on standards, documentation, incident response, automation, and company-wide technology improvements.