Clera
AI/ML Engineer
About this role
An early-stage AI startup is seeking an AI/ML Engineer to develop machine learning models that revolutionize computer-aided engineering and simulation workflows. You'll research and deploy AI solutions that accelerate design iteration, improve accuracy, and enable new engineering capabilities, working alongside applied AI and mechanical engineering experts.
What you'll do
- Design and develop AI/ML algorithms for simulation, design automation, and engineering challenges
- Build scalable AI solutions and integrate models into CAE workflows and infrastructure
- Lead performance optimization across model accuracy and system-level bottlenecks
- Guide AI/ML projects from research conception through production deployment
- Establish MLOps practices and build robust end-to-end AI/ML systems
- Collaborate with cross-functional teams to solve applied engineering problems
What they're looking for
- Python and modern ML frameworks (PyTorch, JAX, TensorFlow)
- AI/ML algorithms for sequential, spatial, or physics-informed data
- MLOps and end-to-end AI system development
- Engineering principles in simulation, modeling, and optimization
- Technical leadership and project management
- CAE/CAD tools experience (Ansys, Abaqus, COMSOL, etc.)
- Machine learning model deployment
- Time series and mesh data analysis
Benefits
- Salary: $150,000–$250,000 USD annually
- Visa sponsorship available
- Early-stage startup equity opportunity
- On-site work in San Francisco
- Opportunity to make foundational impact on engineering automation
- Collaboration with expert teams in AI and mechanical engineering
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Clera
Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.
View all jobs at CleraLikely interview questions
- Walk us through a project where you deployed an ML model to production in an engineering or scientific domain. What were the key challenges and how did you measure success?
- Describe your experience with physics-informed or domain-specific ML. How have you incorporated engineering principles into model design or validation?