General Engineer

We are looking to work with engineers who are interested in building reliable systems for deploying and serving machine learning models in production. A large part of the work involves building infrastructure and services that support ML models in real-world applications.

• Work directly on production systems powering state-of-the-art speech and avatar models
• Small team where every engineer has outsized impact on the product
• Rapid iteration cycles — your code ships to real users fast
• Deep exposure to cutting-edge ML research and infrastructure challenges

• Experience working with modern ML tooling (e.g. Hugging Face, PyTorch, model serving frameworks)
• Building production systems around ML models, including data pipelines and inference services
• Familiarity with infrastructure and Dev Ops tooling such as Docker, Terraform, and CI/CD systems (e.g. Jenkins or similar)

• Developing backend services for model inference (for example using FastAPI or similar frameworks)
• Working with containerized environments and infrastructure automation
• Building web interfaces or tools (e.g. React-based dashboards or internal tooling) to interact with deployed models

• Assess your fit from a skill perspective
• Assess your fit from a culture perspective
• Give you a chance to ask questions and find out more

You will be asked to complete an engineering exercise related to your specialties and skillsets. This could be something like building a data pipeline or optimising a model.

Normally it will last about 3 hours:

• 20 minutes of planning
• 10 minutes of discussion
• 2 hours 15 minutes of independent coding and implementation

You will spend 2–5 days in person with the engineering team. Depending on the situation you would either work through a problem the team has already solved, build something new with the team, or another activity that seems productive with two aims:

• Helping the engineering team get a sense of working with you
• Helping you get a sense of working with the engineering team