Machine Learning Engineer (f/m/d)

A Machine Learning Engineer at adjoe plays a pivotal role in developing algorithms and models that optimize ad bidding and enhance user experience. This involves deep learning, recommendation systems, and hands-on experimentation with data-driven decision-making.

To apply for the Machine Learning Engineer position at adjoe, candidates should have at least 2 years of experience in Data Science, proficiency in programming languages like Python or R, and familiarity with deep learning frameworks such as TensorFlow or PyTorch.

At adjoe, we prioritize professional development through our Education Package, which offers training courses directly aligned with your career goals, ensuring that our Machine Learning Engineers stay competitive and knowledgeable in their field.

The WAVE Data Science team at adjoe is a collaborative and innovative group dedicated to creating cutting-edge advertising solutions. Team members share their expertise and work closely together to shape the future of adjoe's technology and algorithms.

Yes! Machine Learning Engineers at adjoe benefit from a flexible work-life package that includes 2 remote days a week and the option to spend 3 weeks per year working remotely, promoting a comfortable work environment.

Candidates applying for the Machine Learning Engineer position at adjoe should have strong knowledge in Python, R, Scala, or Julia, as these are the typical languages used in Data Science projects within the company.

As a Machine Learning Engineer at adjoe, you'll work on a variety of projects focused on developing and deploying algorithms for bidding processes, creating recommendation systems, and analyzing user data to enhance monetization strategies.

Supervised learning involves training a model on labeled data, meaning each input is paired with an output. In contrast, unsupervised learning deals with unlabeled data, where the model tries to find patterns and relationships without explicit instructions. It’s important to highlight your understanding of both when discussing topics during the interview.

When discussing your experience deploying machine learning models to production, focus on specific projects where you've successfully transitioned models from development to production, including any challenges encountered and how you monitored performance after deployment.

To address overfitting, consider strategies such as cross-validation, regularization techniques, and reducing feature complexity. Provide specific examples from your experience where you successfully mitigated overfitting in a model.

Discuss a data problem where you faced difficulties in data preparation, model selection, or optimization. Explain the methodologies you used to overcome these challenges, demonstrating your problem-solving skills and analytical thinking.

Discuss using techniques like Recursive Feature Elimination (RFE), correlation matrices, or regularization methods such as LASSO to select the most relevant features for your model. Emphasize the importance of feature selection in improving model accuracy and performance.

Data cleaning is crucial as it ensures the data fed into the model is accurate, complete, and relevant. Talk about your experience in identifying and rectifying inconsistencies in datasets, which ultimately leads to higher prediction accuracy.

Mention your commitment to continuous learning through attending workshops, following thought leaders, reading research papers, and participating in online courses. Share specific examples of how these resources have influenced your work.

Provide a clear explanation of basic neural networks, including input, hidden, and output layers. Discuss different architectures like CNNs, RNNs, and their applications, showcasing your knowledge of deep learning as a Machine Learning Engineer.

Talk about projects where you utilized libraries like TensorFlow or PyTorch for building and training models. Highlight any unique implementations, challenges faced, and how these tools enhanced your ability to develop effective machine learning solutions.

A recommendation system identifies and suggests items to users based on their preferences and behavior. Discuss collaborative filtering, content-based filtering, and hybrid approaches, showcasing how this knowledge will contribute to your success as a Machine Learning Engineer at adjoe.