Architect
Founding Member of Technical Staff - Post Training
Palo Alto, CA · Staff+
Sponsorship not specifiedDetected 352 days ago
Distributed SystemsAlgorithmsMachine LearningDeep LearningPyTorchLLMsSystems EngineeringResearch
About the role
- Born out of Stanford, our team blends researchers and engineers from Anthropic, Google DeepMind, NVIDIA, Meta SuperIntelligence Labs, Apple, and Intel.
- Responsible for co-designing and implementing the Reinforcement Learning environments and algorithms, Reward Models trainings and reward signal experiments.
Responsibilities
- As a Founding Member of the Technical Staff (RL) at Architect, you'll be at the forefront of post-training the AI models for chip design tasks like RTL code generation, verification, and architectural exploration.
- 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.
- Systems Engineering: Strong software engineering skills with experience building complex ML systems.
- About Architect Architect is a frontier AI research and product lab for chip design.
- We build AI models and systems that can explore, design, optimize, and verify new hardware.
- Our goal is to reimagine chip design using AI, cut down ASIC design time and cost, and enable a new era of ultra-efficient, domain-specific chips powering the future of computation.
Requirements
- What We'd Like to See Qualifications & Skills: Degree: PhD in Computer Science, EECS, Mathematics, or a closely related field.
Nice to have
- Foundation in Electrical/Computer Engineering and chip-design or verification processes (not required, but a plus).
Skills
- Degree: PhD in Computer Science, EECS, Mathematics, or a closely related field.
- Preferably, specialization in Machine Learning, Deep Learning, or Artificial Intelligence.
- Or BS/MS with a strong research engineering background.
- Experience RL and fine-tuning LLMs and code models for reasoning, tool use, and structured coding tasks.
- Engineering Rigor: Adept at analyzing and debugging model training processes.
- Capable of balancing research exploration with engineering rigor and operational reliability.
- 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.
Benefits
- Competitive salary and meaningful equity stake Fast-paced startup with autonomy and visible impact Cutting-edge AI-driven chip design challenges
- 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.
- Bonus: Worked on the post-training team at frontier labs like OpenAI, Anthropic, DeepMind, Mistral, MSL, Cohere, etc.
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