Senior Talent Partner responsible for building and scaling the US team, recruiting technical talent across AI, ML, infrastructure, cloud, and developer tools, and partnering with founders and senior leaders to shape hiring strategy.
Responsibilities
Build the US team
Own and support hiring across strategic US roles
Develop strong relationships within the US AI and technology ecosystem
Build long-term networks of engineers, founders, operators and technical leaders
Attend and help identify relevant meetups, conferences and industry events
Requirements
Nice to have: Existing Network within AI/ML ecosystem
Nice to have: Experience in early-stage or high-growth startup
Nice to have: Experience helping European or international company establish or scale US presence
Nice to have: Exposure to US People Ops, employment or benefits topics
Nice to have: Recruiting Staff+, Principal, leadership or highly specialized technical profiles
Building and maintaining high-performance infrastructure for large-scale LLM training, reinforcement learning, and inference, including distributed runtimes and GPU cluster scheduling.
Locations (2)
New York City, New York, United States
Paris, Île-de-France, France
Responsibilities
Design, build, and operate distributed training and inference infrastructure for large language models, including Mixture-of-Experts models
Scale training with parallelism strategies (data, tensor, pipeline, and expert), memory optimization, checkpointing, and failure recovery
Build inference and rollout systems that support evaluation, experimentation, and RL without slowing down training
Build execution environments and sandboxes for online RL and agent post-training
Own continuous performance validation: regular benchmarks of rollout speed, training throughput, and learning behavior
Lead the commercial and technical strategy for CoreWeave’s inference offerings across the AMER region, driving new customer wins, shaping product roadmap, and translating customer needs into product feedback.
Forward Deployed ML Engineers work at the intersection of deep technical work and direct customer impact, partnering with leading AI companies and foundation model labs to achieve state-of-the-art performance on workloads like LLM serving, model training, and audio pipelines.
Responsibilities
Work hands-on with companies like Suno, Cognition, and Meta to architect and optimize production AI workloads on Modal
Contribute to open-source projects (e.g., SGLang) and publish technical content
Collaborate with Modal’s product and sales teams, contributing to the platform as both an engineer and a product stakeholder
Build trusted relationships with technical leaders (CTOs, VPs of Engineering, ML leads) at companies doing frontier AI work
Conduct technical demos, experiments, and proofs of concept
Requirements
Nice to have: Side projects
Nice to have: open-source contributions
Nice to have: published work in ML or systems performance
Build systems to evaluate and refine Apple's foundation models and agents, including benchmarks, evaluators, simulation environments, and optimization pipelines.
Responsibilities
Design and develop benchmarks, evaluators, simulation environments, and prompt and context optimization pipelines
Collaborate with product teams and the foundation model team to close the loop between observation and improvement
Build systems that connect failure analysis to measurable model and agent improvements
Turn evaluation findings into persona-grounded datasets and reward signals for foundation model post-training
Evaluate across offline benchmarks, device-in-the-loop simulation, and on-device observation in production
The base salary range for this role is $177,000 to $237,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).
Responsibilities
Serve as the AMER field's primary intelligence source on AI inference
Drive commercial and technical strategy in AMER for net new customer wins
Identify new business opportunities in AMER
Develop deep expertise across the AI runtime landscape
Translate customer requirements into product feedback
Requirements
Advanced degree in Computer Science, Machine Learning, or Engineering
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