Architect
Member of Technical Staff - ML Research
Palo Alto · Staff+
Sponsorship not specifiedDetected 161 days ago
Distributed SystemsAlgorithmsMachine LearningDeep LearningPyTorchLLMsSystems EngineeringResearch
About the role
- Born out of Stanford Research, our team blends AI with Silicon with a founding team from Anthropic, Google DeepMind, Meta SuperIntelligence, xAI, Apple and Intel.
- Preferably, specialization in Machine Learning, Deep Learning, or Artificial Intelligence.
- Comfortable working with large-scale distributed systems, high-performance computing, and distributed training frameworks (e.g., PyTorch, CUDA, QLoRA, ZeRO). - Engineering Rigor: Adept at analyzing and debugging model training processes.
Responsibilities
- As a Founding Member of the Technical Staff at Architect, you'll be at the forefront of training AI models for chip design, verification and exploration tasks.
- You will work at the intersection of cutting-edge research and production engineering for chip designs, implementing, scaling, and improving post-training techniques to enhance model capabilities and usability.
- Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation, ensuring that theoretical performance translates into production-ready implementations.
- This is a hands-on, 0→1 role where you'll own the end-to-end RL workflow-from reward modeling and environment design to test-time optimization and scaling.
- Collaborate with research teams to translate emerging techniques into production-ready implementations and debug complex issues in training pipelines and model behavior.
- Model Training: Strong industry or research background building end-to-end ML pipelines. Experience RL and fine-tuning LLMs and code models for reasoning, tool use, and structured coding tasks.
- Systems Engineering: Strong software engineering skills with experience building complex ML systems. Comfortable working with large-scale distributed systems, high-performance computing, and distributed training frameworks (e.g., PyTorch, CUDA, QLoRA, ZeRO).
Requirements
- Degree: PhD in Computer Science, Computer Engineering, EECS, Mathematics, or a closely related field.
Nice to have
- Foundation in Electrical/Computer Engineering, Computer Architecture, and chip-design or verification processes (not required, but a plus).
Skills
- Execution: Fast-moving builder who can prototype, benchmark, and productionize training pipelines with tight feedback loops.
- Publications in top ML (NeurIPS, ICLR, ICML) or EDA (DAC, ICCAD, DVCon) venues.
- Competitive salary and meaningful equity stake
- Fast-paced startup with autonomy and visible impact
- Cutting-edge AI-driven chip design challenges
Compensation
- Competitive salary and meaningful equity stake
Benefits
- Responsible for co-designing and implementing the Reinforcement Learning environments and algorithms, Reward Models trainings and reward signal experiments.
- RL & Post-Training Expertise: Deep expertise in reinforcement learning and post-training, with a proven track record of taking models from research to real-world deployment.
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