Top Tech Jobs & Startup Jobs in NYC, NY

Implement, optimize, and validate high-performance machine learning and linear algebra kernels for the Cerebras Wafer-Scale Engine. Develop low-level kernel routines using the Cerebras Software Language and C++, apply parallel programming techniques, profile and tune performance, debug correctness and hardware utilization issues, build tests and validation, and collaborate with compiler, performance, and hardware engineers.

Early-career SDET on Release Integration Testing for Cerebras AI Inference Core. Write automation and tests across the AI stack, triage cross-stack failures, maintain branch stability, collect qualification evidence, and collaborate with feature and infra teams to prepare features for production release.

Design and validate electrical infrastructure for high-density AI data center deployments: develop BoD, one-line diagrams, load and protection studies, review vendor submittals, coordinate multidisciplinary teams, support commissioning, and ensure compliance with NEC, NFPA, IEEE and regional codes.

Implement and scale LLM training, fine-tuning, and post-training techniques (RL-based). Build evaluation and data pipelines, debug ML stack issues, optimize training/inference workflows, and ship maintainable ML infrastructure code.

Design and develop software to automate bare-metal configuration, orchestration, scheduling, and job placement for large wafer-scale clusters. Build upgrade/patch workflows, monitoring, HA failure handling, visualization and alerting, and user/admin tools. Ensure on-premise and cloud deployment support and develop tests and debugging tools for distributed systems.

Drive end-to-end ML model inference performance: build kernel- and system-level performance models, optimize kernel microcode and compiler algorithms, debug runtime performance on system and cluster, and develop tooling to visualize and analyze performance data from the Wafer Scale Engine and compute cluster.

Own quality and reliability of the Cerebras Inference Platform by building test infrastructure and automation, validating Kubernetes and hardware deployments, debugging distributed systems and networking, developing testbeds for performance and scalability, and partnering with platform engineers to ensure production-ready releases.

Build, productionize, and optimize a GPU-based inference stack combining GPU prefill with Cerebras decode. Implement and operate model-serving APIs, vLLM/PyTorch/ROCm runtimes, deployment and reliability practices, performance profiling and optimization, cross-layer debugging, numerical validation, and benchmarking/infrastructure for production inference at scale.