Top Tech Jobs & Startup Jobs

Conduct research on speculative decoding for large language models, including developing drafter architectures, verification schemes, distillation methods, and adaptive training approaches. Train and fine-tune models on distributed GPU clusters, evaluate algorithms under realistic serving workloads, optimize implementations for inference engines, communicate findings, and contribute successful research to production.

Research intern developing speculative decoding algorithms and draft model architectures for faster large-language-model generation. Responsibilities include training, fine-tuning, and distilling models on distributed GPU clusters; designing rigorous experiments; optimizing serving implementations; evaluating realistic workloads; communicating results; and contributing research to production systems through technical writing and collaboration.

Source, engage, and nurture passive candidates for business-side roles across go-to-market, infrastructure strategy, and G&A. Partner with recruiters and hiring managers to define candidate profiles, craft personalized outreach, build talent pipelines, track sourcing metrics, and develop scalable sourcing systems. The role requires managing senior, hard-to-fill searches in a startup or hyper-growth environment while adapting across functions and using recruiting tools such as GEM, LinkedIn Recruiter, Noon AI, and Greenhouse.

Architect and build Together AI’s globally distributed GPU cloud platform, including virtualization, Kubernetes-based IaaS, GPU scheduling, networking, storage, fault-tolerant control planes, monitoring, and automated remediation. Set technical direction, lead architecture and design reviews, drive cross-team alignment, mentor engineers, and establish engineering standards for reliable, high-performance AI infrastructure at massive scale.

Sources and evaluates elite engineering candidates through proactive outreach, GitHub review, and technically deep screening calls. Builds talent pipelines, runs outreach campaigns, assesses candidates’ technical depth and ownership, and partners with hiring managers and recruiters to calibrate hiring standards. The role requires an engineering background, strong technical fluency, persistence, and compelling written communication. Recruiting experience is optional and can be learned on the job.

Design and implement security solutions for AI infrastructure across hardware, firmware, operating systems, and cloud services. Assess platform security, automate telemetry and configuration-drift detection, build AI-assisted security applications, support secure device lifecycle management, and enforce identity and privileged-access controls. Research confidential computing and hardware roots of trust while documenting technical designs, tests, and security findings.

Supports hardware and platform security for AI infrastructure across firmware, operating systems, and cloud services. Builds Python or Bash automation for telemetry, configuration drift detection, and incident investigations; prototypes AI-assisted security tools; researches confidential computing, remote attestation, and hardware roots of trust; and delivers a mentored security project with documented code and a final presentation.

Own investor relationships, equity fundraising, investor communications, and the company’s investment narrative. Lead board reporting, long-range planning, valuation and fundraise models, KPI development, investor targeting, diligence, and executive preparation. Translate AI infrastructure economics, financial performance, market trends, and investor sentiment into clear strategic insights for leadership, board members, and investors.

Operate and scale multi-petabyte distributed storage for AI and HPC workloads. Design high-performance parallel filesystems, object stores, caching architectures, and Kubernetes-native storage operators. Optimize data paths, throughput, multi-tenancy, cost, and reliability across thousands of GPU nodes. Integrate technologies such as Vast, Weka, Ceph, and Lustre; develop automation and tooling in Go and Python; and contribute to storage infrastructure, observability, benchmarking, and open-source projects.

Leads Together AI’s partner marketing function by defining strategy, messaging, and co-marketing execution with NVIDIA and cloud partners. Collaborates with GTM, product marketing, demand generation, and field marketing teams to deliver campaigns, establish partner operating rhythms, track performance metrics, and improve ROI. The role requires building the function from the ground up and scaling partner go-to-market programs for a B2B AI or software platform business.