Member of Technical Staff — Agent Post-Training
Embedding VC20 days ago
Responsibilities
- Build RL and post-training pipelines for multimodal, vision-language, and code-generating agents.
- Develop infrastructure for supervised fine-tuning, preference optimization, reward modeling, and reinforcement learning.
- Create systems for collecting, filtering, replaying, and learning from agent trajectories.
- Design rewards, verifiers, and evaluations for long-horizon agent tasks.
- Improve agents’ planning, code generation and execution, tool use, error recovery, and complex workflow completion.
- Scale distributed training and high-throughput rollout generation across multi-GPU environments.
- Improve training reliability, reproducibility, observability, and cost efficiency.
- Help define long-term strategy for agent, robotics, and embodied-model training.
Requirements
- Real-world experience training large language, vision-language, multimodal, or code models.
- Strong experience in reinforcement learning, post-training, or large-scale fine-tuning.
- Experience building distributed training or high-throughput inference systems.
- Familiarity with supervised fine-tuning, preference optimization, reward modeling, and agentic RL.
- Experience with code-generation agents, long-horizon evaluation, or tool-using systems.
- Strong Python skills and experience with PyTorch, JAX, or similar frameworks.
- Ability to work across data, models, environments, rewards, evaluation, and infrastructure.
- Strong research judgment and a bias toward building reliable systems.
- Preferred: experience at a frontier AI lab or organization operating large-scale training systems.
- Preferred: experience with code-model post-training, autonomous coding agents, multimodal models, robotics, simulation, embodied AI, verifiable rewards, outcome-based training systems, or large GPU clusters.
Benefits
- On-site, in-person work in San Francisco.
Categories
AI ResearchML Engineering
About Embedding VC
Embedding VC is an early-stage venture fund that backs generative AI startups, led by current and former builders and operators. The partnership invests capital and hands-on expertise to help AI founders scale, with a focus on progress toward AGI. Founded in 2023 and headquartered in Menlo Park, California, it operates as a private partnership concentrating on AI-native applications and infrastructure.