Top Tech Jobs & Startup Jobs in Boston, MA

Develop and iterate post-training methods for large language models: drive data collection, build evaluations, design experiments, and apply reward modeling and RL techniques to improve model capabilities and deployment.

Design, curate, and evaluate high-quality multimodal datasets for foundation models. Build scalable ingestion, preprocessing, filtering, annotation pipelines; run data ablations and analyses; create auditing, visualization, and versioning tools; collaborate with Safety, Ethics, and research teams to ensure data quality, privacy, and responsible AI alignment.

Design and develop multimodal foundation-model algorithms and architectures, run large-scale experiments, improve training and deployment efficiency, build datasets and pipelines, perform rigorous evaluation and ablation studies, and collaborate with infrastructure, data, and product teams to responsibly advance AI systems.

Drive product strategy and KPIs for backend networking of ultra-large AI supercomputers. Identify failure points, lead remediations and roadmap changes, and coordinate cross-functional teams including customers, datacenter operators, vendors, and product groups to maximize uptime and AI workload throughput.

Design and implement next-generation networking infrastructure for large-scale AI training on Azure. Lead architecture, write secure high-performance code, create test strategies, perform and coach code reviews, integrate AI tools in the SDLC, and collaborate cross-team to ensure scalability, reliability, observability, and security for distributed HPC/AI systems.

Design, implement, and optimize low-level software for Azure HPC/AI VMs. Diagnose HW/SW integration and performance issues, drive cross-team solutions, maintain service stability, and develop operational playbooks.

Design, build, and optimize high-performance, low-latency networking infrastructure for large-scale AI training on Azure. Benchmark and improve scalability, reliability, and observability; debug complex network issues; author design documents; act as DRI and participate in on-call operations to restore and maintain service availability.

Design and build monitoring, telemetry, and data pipelines to operate flagship supercomputers at scale. Diagnose and troubleshoot GPU hardware, networking, datacenter and software stack issues, implement systemic mitigations, improve observability, and drive reliability and performance improvements through incident reviews and tooling.

Design, curate, and evaluate large-scale text datasets for LLM pretraining. Build scalable ingestion, preprocessing, filtering, annotation pipelines; run data ablations and analyses; create auditing, visualization, and versioning tools; and collaborate with Safety, Ethics, and Governance to ensure dataset quality, privacy, and responsible AI alignment.

Lead infrastructure analytics for compute, storage, and networking: design scalable data pipelines, define core metrics, build dashboards and APIs, ensure data quality and governance, drive instrumentation fixes, and advise executives on capacity, utilization, and readiness.