**Location working:San Mateo, CA (Peninsula) – Onsite preferred, open to Bay Area candidates willing to commute
Salary Range: $110,000 - $175,000/year**
ML System Lifecycle Ownership: Drive full lifecycle of ML systems from research to production, transforming messy geometry data into effective training signals. Model Development & Deployment: Design, train, and deploy deep learning models for CAD workflows, ensuring fast and seamless integration into CAD environments. Programming & Pipelines: Maintain robust Python training pipelines with focus on data preprocessing, experimentation, and performance optimization. Technical Expertise: E...
Responsibilities
As Machine Learning Research Engineer, you’ll own the full lifecycle of ML systems that power our AI-driven CAD product. You’ll move between deep research and hands-on engineering: scoping problems with the founders, designing and training novel models, and getting them into robust production code. A big part of the role is turning messy, real-world geometry and design data into usable training signals, then iterating quickly based on performance, reliability, and customer feedback. You’ll collaborate closely with our backend engineers on data pipelines, APIs, and infra, and you’ll play a key role in setting technical direction for how we apply ML to accelerate hardware design.
What you’ll do
- Design, train, and iterate on custom deep learning models that understand CAD workflows and predict high-quality next-step suggestions.
- Build and maintain robust Python training and evaluation pipelines, including data preprocessing, experimentation, and offline/online metrics.
- Architect model serving and backend components so that features are fast, reliable, and easy to integrate into CAD environments.
- Work closely with the founder and early customers (mechanical / hardware engineers) to understand real-world workflows and translate them into ML formulations.
- Own the full lifecycle of ML features — from research and prototyping through productionization, deployment, and monitoring.
- Collaborate with the broader engineering team on core product and infrastructure work (backend, APIs, data models, performance).
- Establish best practices for experimentation, logging, and model comparison to ensure steady improvements over time.
- Stay current on relevant ML research (e.g., sequence models, geometric deep learning, representation learning) and decide pragmatically what is worth applying.
Qualifications
We’re looking for a senior, fundamentals-strong ML engineer who is as comfortable reading papers and inventing new approaches as they are writing clean, production-ready Python. The right person has hands-on experience training models (not just calling hosted APIs), understands modern deep learning frameworks like PyTorch inside-out, and can reason about architecture, data, and evaluation trade-offs. You should be able to independently drive projects from idea to shipped feature, collaborate well with a small, high-caliber team, and thrive in an early-stage startup environment where requirements are ambiguous, ownership is high, and the bar for code quality and rigor is serious.
- Deep ML expertise – 4+ years of hands-on machine-learning experience (or equivalent research / thesis-based Master’s or PhD), with a track record of training and improving deep models, not just using pre-built APIs.
- Strong Python engineering – you write clean, well-structured, production-ready Python without handholding, including tests, documentation, and thoughtful abstractions.
- Modern deep learning frameworks – expert with PyTorch (preferred) or similar frameworks such as TensorFlow / JAX; comfortable implementing and modifying custom architectures, loss functions, and training loops.
- End-to-end ownership – experience owning ML systems from data to deployment: building training pipelines, running experiments at scale, tuning hyperparameters, and shipping models into real products.
- Applied problem-solving – proven ability to take messy, open-ended product requirements and turn them into concrete ML formulations, experiments, and shipped features.
- Collaboration & communication – able to work closely with founders, engineers, and (eventually) customers; can explain trade-offs and model behavior clearly to both technical and non-technical partners.
- Startup mindset – comfortable in a fast-moving, low-process environment; willing to wear multiple hats across research, engineering, and backend work when needed.
- Nice to have – experience with geometry / graphics / CAD, 3D representations, or robotics; familiarity with cloud ML platforms (AWS / GCP) and backend frameworks (Flask, FastAPI, Django).
Interview Process
Short survey – Initial call. – Take-home coding & ML exercise. – Onsite / virtual technical interviews. – Offer & closing
About Company
We are building AI-powered CAD to fundamentally change how hardware is designed and manufactured. Founded by repeat deep-tech entrepreneurs with experience from top YC companies and frontier robotics, We are turning CAD into an intelligent collaborator that understands design intent and autocompletes the work. Their mission is to let hardware teams move from weeks of repetitive clicking to minutes of high-leverage engineering, so they can focus on the hard physics and product decisions instead of manual drafting.