Machine Learning Engineer

Job Description

Work Schedule: Day Shift
Employment Type: Full-Time

Qualifications:
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
Ability to write clean, maintainable, production-ready Python, including tests, documentation, and well-designed abstractions
Expertise in PyTorch (preferred) or similar frameworks such as TensorFlow or JAX; comfortable implementing custom architectures, loss functions, and training loops
Experience owning ML systems from data ingestion to deployment, including training pipelines, large-scale experimentation, hyperparameter tuning, and production rollout
Demonstrated ability to translate ambiguous, real-world product requirements into concrete ML formulations and shipped features
Strong ability to work with founders, engineers, and stakeholders; able to explain model behavior and trade-offs to both technical and non-technical audiences
Comfortable operating in a fast-moving, low-process environment with high ownership and evolving requirements

Key Responsibilities:
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