Member of Technical Staff - ML Research

Kindredventures; Palo Alto (CA); On-site; USD 150,000 - 200,000; Full time

Benefits offered by this job

Competitive salary and equity

Autonomy in a fast-paced environment

Cutting-edge AI challenges

Job summary

Kindredventures in Palo Alto is looking for a Founding Member of the Technical Staff to lead AI model training for chip design. You will work on cutting-edge Reinforcement Learning environments and build end-to-end ML pipelines.

The successful candidate will hold a PhD in a relevant discipline and have strong experience in ML and deep learning applications. This fast-paced startup offers competitive salary and equity, focusing on impactful AI-driven chip design challenges.

Qualifications

• PhD or BS/MS in relevant fields with a strong research-oriented background.
• Deep expertise in reinforcement learning and model post-training.
• Experience building end-to-end ML pipelines.

Responsibilities

• Co-design and implement Reinforcement Learning environments and algorithms.
• Design and run efficient pipelines for model fine-tuning.
• Collaborate with research teams to translate techniques into production.

Skills

Reinforcement Learning

Machine Learning

Deep Learning

Python

Software Engineering

Education

PhD in Computer Science, Computer Engineering, EECS, or Mathematics

BS/MS with a strong research engineering background

Tools

PyTorch

CUDA

Job description

About Architect

Architect is a frontier AI lab for chip design. We build AI models and tools for on-demand custom ASICs at scale. Our goal is to co‑design custom ASICs alongside evolving ML workloads, and enable a new era of domain‑specific chips that unlock capabilities impossible with current hardware paradigms. 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.

What You'll Do

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 be doing fundamental research and applying that to industry‑grade chips going into production at leading foundry technologies like TSMC.

• 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, 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.

What We'd Like to See

Qualifications & Skills:

• Degree: PhD in Computer Science, Computer Engineering, 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, Computer Architecture, 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