Machine Learning Engineer

Job Overview

ML System Lifecycle Ownership: Own the full lifecycle of machine learning systems—from research and prototyping to production—transforming complex, real-world geometry data into effective training signals.

Model Development & Deployment: Design, train, and deploy deep learning models for CAD workflows, ensuring fast, reliable, and seamless integration into CAD environments.

Programming & Pipelines: Build and maintain robust Python-based training pipelines with a strong focus on data preprocessing, experimentation, evaluation, and performance optimization.

Technical Leadership: Contribute to technical direction and best practices for applying machine learning to accelerate hardware and mechanical design workflows.

Responsibilities

As a Machine Learning Research Engineer, you will own the end-to-end lifecycle of ML systems that power an AI-driven CAD product. This role blends deep research with hands-on engineering—working closely with founders and engineers to scope problems, design and train novel models, and deploy them into production-quality systems.

A core aspect of the role is transforming messy, real-world geometry and design data into usable training signals, then iterating quickly based on performance, reliability, and user feedback. You will collaborate closely with backend engineers on data pipelines, APIs, and infrastructure, and play a key role in shaping how machine learning is applied to modern CAD workflows.

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 Python-based training and evaluation pipelines, including data preprocessing, experimentation frameworks, and offline/online metrics
• Architect model serving and backend components to ensure ML-powered features are fast, reliable, and easy to integrate into CAD environments
• Work closely with founders and early users (mechanical and hardware engineers) to understand real-world workflows and translate them into ML formulations
• Own the full ML feature lifecycle—from research and prototyping to productionization, deployment, and monitoring
• Collaborate with the broader engineering team on core product and infrastructure components (backend services, APIs, data models, performance optimization)
• Establish best practices for experimentation, logging, model comparison, and evaluation to drive continuous improvement
• Stay current on relevant ML research (e.g., sequence models, geometric deep learning, representation learning) and apply it pragmatically to real product needs

Qualifications

We are looking for a senior, fundamentals-strong machine learning engineer who is equally comfortable reading research papers and writing clean, production-ready code. The ideal candidate has hands-on experience training models (not just calling hosted APIs), deep familiarity with modern ML frameworks, and the ability to independently drive projects from concept to shipped feature.

• Deep ML expertise: 4+ years of hands-on machine learning experience (or equivalent research experience via Master’s or PhD), with a proven track record of training and improving deep learning models
• Strong Python engineering: Ability to write clean, maintainable, production-ready Python, including tests, documentation, and well-designed abstractions
• Modern deep learning frameworks: Expertise in PyTorch (preferred) or similar frameworks such as TensorFlow or JAX; comfortable implementing custom architectures, loss functions, and training loops
• End-to-end ownership: Experience owning ML systems from data ingestion to deployment, including training pipelines, large-scale experimentation, hyperparameter tuning, and production rollout
• Applied problem-solving: Demonstrated ability to translate ambiguous, real-world product requirements into concrete ML formulations and shipped features
• Collaboration & communication: Strong ability to work with founders, engineers, and stakeholders; able to explain model behavior and trade-offs to both technical and non-technical audiences
• Startup mindset: Comfortable operating in a fast-moving, low-process environment with high ownership and evolving requirements

Nice to Have

• Experience with geometry, graphics, CAD systems, 3D representations, or robotics
• Familiarity with cloud ML platforms (AWS or GCP)
• Experience with backend frameworks such as Flask, FastAPI, or Django

Ideal Candidate

A fundamentals-driven ML builder who cares deeply about both theory and production. You enjoy reading research papers, but you’re most excited when those ideas are running in production and delivering real user impact. You write clean Python, know how to debug and evaluate models rigorously, and can own features from concept to deployed service. You’re motivated by tackling hard, long-term problems and building world-class AI-powered engineering tools.