We’re looking for a machine learning engineer to join the team and contribute to automate and bring to production our deep learning solutions.

You will:

1. Build data and machine learning model pipelines and manage infrastructure

2. Research and implement best practices to improve the existing machine learning infrastructure

3. Research and implement appropriate tools to accelerate our use case production

4. Create metrics to monitor our services and ensure their proper functioning

5. You will help to improve the processes of the team and automate some parts of the experimentation, development, and evaluation

Adevinta is an equal opportunity employer and values diversity in our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status or disability status. 

Flexibility: We believe that work is what you do, not where you do it. So we provide flexible hours and remote working options that give you the autonomy and scope to set your own agenda and working patterns. We empower our teams to decide which key moments they want to spend face to face, instead of mandating a fixed number of office days.

Covid-19 

The health of our employees is very important to us. Due to Covid-19 all of our employees currently have the option to work from home full-time or, depending on the local situation and government regulations, they may go into the office following all safety protocols. This means that our interviews are currently taking place virtually. We are reviewing the situation on a regular basis and want to ensure that we follow all governmental guidelines and put the health of our people first.

If any of the above ticks your boxes, then why not Apply now to find out more.

Who you are:

1. Experience with delivering machine learning models from data exploration to production and maintenance.

2. Experience with software engineering in Python and Java, applying design principles for machine learning. 

3. Strong knowledge of AWS Sagemaker (plus general AWS environment and tooling)

4. Experience with serving machine learning models (i.e. TorchServe, ONNX, TFServe)

5. Knowledge of Kubeflow (in particular Pipelines) and Docker

6. Experience with CI/CD and gitops (i.e Git, Travis, Spinnaker)

7. Strong analytical and problem solving skills

8. Good verbal and written communication skills