Blazetalent
Senior Sourcer (AI / ML / Infra) - Contract
Remote, USA · Senior
Sponsorship not specified$50k-$90kDetected 175 days ago
C++Distributed SystemsGitMachine LearningPyTorchRecruitingPower ElectronicsResearch
About the role
- Identify "pockets of talent" within hardware-accelerated compute and distributed systems.
- Strategic Outreach: Craft highly personalized, technical outreach that resonates with senior engineers who are inundated with recruiter spam.
- You must speak the language of AI infra.
Responsibilities
- Deep-Technical Sourcing: Build robust pipelines for highly specialized roles including GPU/TPU Kernel Engineers, Distributed Systems Architects, InfiniBand/RDMA Networking Specialists, and AI Platform Engineers.
- High-Velocity Delivery: Maintain a high volume of qualified "first-screens" while ensuring the candidate experience is white-glove from the very first touchpoint.
- ASAP About the Role Blaze Talent is seeking an elite Technical Sourcer to support a high-growth AI Hyperscaler client.
- This isn't a generalist role; you will be the primary engine driving top-of-funnel talent for the teams building the massive-scale compute, networking, and power systems that make modern AI possible.
- You will be embedded within a world-class engineering organization, hunting for the 1% of talent capable of building and scaling AI infrastructure at a global level.
- Build robust pipelines for highly specialized roles including GPU/TPU Kernel Engineers, Distributed Systems Architects, InfiniBand/RDMA Networking Specialists, and AI Platform Engineers.
- Maintain a high volume of qualified "first-screens" while ensuring the candidate experience is white-glove from the very first touchpoint.
Requirements
- AI Infrastructure Expertise: Must have prior experience sourcing for AI Infrastructure, Data Centers, or Large-Scale Cloud Systems.
- 5+ Years Sourcing Experience: A proven track record of finding "purple squirrels" in the deep-tech space, preferably with experience at a major Cloud Service Provider (CSP) or a high-growth AI startup.
- Technical Fluency: Ability to screen candidates for proficiency in low-level programming (C++/CUDA/Rust), distributed frameworks (PyTorch/Ray), and hardware/software co-design.
- You enjoy the challenge of finding talent that isn't looking to be found.
Nice to have
- Mastery of LinkedIn Recruiter, GitHub, and technical sourcing extensions.
- Experience with Ashby (ATS) is a significant plus.
- Must be able to operate during US business hours (PST preferred).
- This is a chance to work at the absolute center of the AI boom.
- Our client is an industry leader providing the backbone for the next generation of LLMs.
- If you are a sourcer who loves the "deep tech" side of the house and wants to work on a high-stakes, high-impact project, this is for you.
- Availability: Must be able to operate during US business hours (PST preferred).
Compensation
- $50-$90/hr Duration: 6 months (Potential for conversion) Start Date: ASAP About the Role Blaze Talent is seeking an elite Technical Sourcer to support a high-growth AI Hyperscaler client.
- This isn't a generalist role; you will be the primary engine driving top-of-funnel talent for the teams building the massive-scale compute, networking, and power systems that make modern AI possible.
- You will be embedded within a world-class engineering organization, hunting for the 1% of talent capable of building and scaling AI infrastructure at a global level.
- Market Mapping & Intelligence: Map the talent landscape across competing hyperscalers, chipmakers, and specialized AI labs.
- Identify "pockets of talent" within hardware-accelerated compute and distributed systems.
- Deep-Technical Sourcing: Build robust pipelines for highly specialized roles including GPU/TPU Kernel Engineers, Distributed Systems Architects, InfiniBand/RDMA Networking Specialists, and AI Platform Engineers.
Apply directly at Blazetalent →Create a free account for alerts like thisView Blazetalent immigration profile
This listing is sourced directly from Blazetalent's careers page and normalized into a canonical job model.