layiq
/la·yiq/adjective
worthy; deserving; fitting; suitable.
A role, opportunity, or path that merits attention, time, and pursuit.
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Job opportunity
AI/ML Engineer
Clera
San Francisco, California, United States
Source: Clera careers · View original posting
Employer job description
From Clera's posting. “We” and “our” refer to the employer.
About the Role
Join an early-stage, venture-backed AI startup reimagining computer-aided engineering (CAE) for the AI era. As an AI/ML Engineer, you'll shape the future of engineering automation by building AI that thinks like an engineer — designing, training, and deploying models that accelerate simulation, improve accuracy, and unlock entirely new ways of exploring the design space. From adaptive solvers and reduced-order modeling to generative design and real-time validation, your work will push the boundaries of what CAE can achieve.
You'll collaborate closely with experts in applied AI and mechanical engineering, integrating machine learning directly into high-fidelity simulation pipelines to help customers iterate faster, diagnose failures earlier, and bring better products to market. This is a rare opportunity to join early and make a foundational impact.
What You'll Do
- Research, design, and develop AI/ML algorithms to tackle complex challenges in simulation, design automation, and computer-aided engineering.
- Build scalable AI solutions and collaborate with cross-functional teams to integrate models seamlessly into existing CAE workflows and infrastructure.
- Lead performance improvements — from optimizing model accuracy to analyzing outputs and addressing system-level bottlenecks.
- Guide AI/ML projects from research through to production deployment.
- Contribute to MLOps practices and help build robust, end-to-end AI/ML systems.
What We're Looking For
- Required: 4+ years of experience developing and deploying AI/ML models with a proven track record of delivering impact in applied engineering or scientific domains.
- 2+ years of technical leadership guiding AI/ML projects from research to production.
- Proficiency in Python and modern AI/ML frameworks (e.g., PyTorch, JAX, TensorFlow).
- Experience with AI/ML algorithms for sequential, spatial, or physics-informed data (e.g., time series, mesh data, or simulation outputs).
- Familiarity with MLOps practices and building AI/ML systems end-to-end, from prototyping to scalable deployment.
- Strong understanding of engineering principles in simulation, modeling, and optimization.