Architectural Services, Artificial Intelligence (AI), Benchmarking, CUDA (Compute Unified Device Architecture), Computer Science, Debugging Skills, Distributed Computing, Engineering, Large-Scale Systems, Mathematics, Process Capability, Process Modeling, Prototyping, RTL Design, Reinforcement Learning, Research Skills, Semiconductor Manufacturing, Software Engineering, Staff Training, Startup, Systems Engineering, Test Design, Training/Teaching, Usability Engineering
What You'll Do
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.
- Responsible for co-designing and implementing the Reinforcement Learning environments and algorithms, Reward Models trainings and reward signal experiments.
- 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, 01 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.
What We'd Like to See
Qualifications & 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.
- 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.
- 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).
- 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.
Bonus:
- Worked on the post-training team at frontier labs like OpenAI, Anthropic, DeepMind, Mistral, MSL, Cohere, etc.
- Foundation in Electrical/Computer Engineering and chip-design or verification processes (not required, but a plus).
- Publications in top ML (NeurIPS, ICLR, ICML) or EDA (DAC, ICCAD, DVCon) venues.
- Experience as a Founding ML Engineer/Researcher or early hire at an AI deeptech startup.
What We Offer
- Competitive salary and meaningful equity stake
- Fast-paced startup with autonomy and visible impact
- Cutting-edge AI-driven chip design challenges