About Us
We are a pioneering startup driven to reshape the frontiers of physics simulation using foundational AI models. Our core aim is to transform engineering design, empowering engineers with unprecedented optimization capabilities driven by AI that rivals the accuracy of traditional simulation methods. Driven by a deep belief that AI will transform how products are engineered across automotive, aerospace, energy, and beyond, we're at the forefront of this exciting revolution. Interest in this space is exploding; join us early and help build a solution that will redefine an entire industry.
We are a values-driven startup embracing fearless innovation, intelligent curiosity, collaboration, a customer-centric approach, whilst creating impact with integrity. These core values shape our culture and are fundamental to our hiring decisions. If you thrive in an environment that pushes boundaries, values learning and teamwork, and seeks to deliver transformative solutions we might be the perfect fit.
The Role
- As our ML Ops Engineer, you'll be the architect of robust processes to streamline and maintain our machine learning models in a dynamic production environment.
- You'll bridge the gap between research and deployment, ensuring our platform continues to deliver transformative results for our development partners.
- This role requires a blend of data engineering, software development, and an understanding of how to operationalize ML for physics-focused applications.
Responsibilities
- Production Pipelines: Design, implement, and optimise scalable ML pipelines. This includes model training, validation, deployment, and monitoring.
- Dataflow for Continuous Learning: Engineer efficient data infrastructure for handling simulation results and production usage data.
- Alerting & Performance Monitoring: Set up systems to track model performance in production, creating alerts to flag potential accuracy degradation related to new geometries explored by our partners.
- Automation & Tooling: Build and select tools to automate as many aspects of the ML lifecycle as possible, streamlining operations and model updates.
- Collaboration is Key: Work closely with ML research engineers to understand new model advancements and integrate them into the production environment. Partner with software engineers for seamless platform functionalities.
- Cloud Optimization: Explore cloud solutions (AWS, Azure, GCP) for scalable infrastructure and model deployment.
Essential Requirements
- Education: Bachelor's degree (Master's preferred) in Computer Science, Data Science, Engineering or a related field.
- ML in Practice: Proven experience building and maintaining operational ML systems.
- Python Proficiency: Strong Python coding skills and familiarity with relevant ML/data engineering libraries.
- DevOps Mindset: Understanding of CI/CD principles, containerisation (Docker, Kubernetes), and version control (Git).
- Problem-Solving Approach: Ability to troubleshoot issues in complex ML pipelines and proactively address potential bottlenecks.
Nice to have
- Physics Simulation Background: Experience using or working with simulation data, particularly in CFD or aerodynamics.
- Cloud Technologies Experience with cloud providers for scalable ML solutions.
- Experiment Tracking: Familiarity with ML experiment tracking tools (MLFlow, Weights & Biases, etc.)