As a Machine Learning Engineer, you will be responsible for designing, developing, and deploying machine learning (ML) models and systems. You will work closely with data scientists, software engineers, and other stakeholders to implement scalable and efficient ML production solutions. Your responsibilities include:

Develop and Implement Machine Learning Models

• Use regression and classification algorithms for predictive modeling. 

• Apply clustering algorithms for data segmentation. 

• Finetune deep learning models such as Transformers. 

• Build NER and NLP pipelines. 

• Implement reinforcement learning strategies. 

• Evaluate models using appropriate metrics to ensure data truthfulness.  

Data Preprocessing and Feature Engineering

• Handle missing data and perform feature scaling. 

• Apply feature selection techniques and dimensionality reduction methods. 

• Encode categorical data effectively. 

• Co-own data quality management 

System Design and Integration

• Design data ingestion and preprocessing pipelines in GCP. 

• Implement ETL/ELT processes for data management. 

• Handle big data using tools like Pyspark and Polars. 

• Utilize GCP Pub/Sub, Dataproc and Dataflow for data streaming. 

• Work with SQL and noSQL databases. 

Model Deployment and Monitoring

• Deploy models in batch and real-time environments. 

• Monitor and maintain models in production. 

• Implement model retraining strategies and workflow orchestration (Vertex AI). 

• Track experiments and manage model registry and versioning using tools like Vertex AI or MLflow. 

 Coding and Implementation

• Develop ML solutions using Python. 

• Write efficient and maintainable code. 

• Work with ML libraries such as scikit-learn, PyTorch, and TensorFlow. 

• Develop scripts for deployment and maintaining the models on the cloud.

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field highly preferred.

• Proven experience (1-3 years) productionizing machine learning and data science models. Internship excluded.

• Proficiency in Python, NLP and ML libraries such as NLTK, Spacy, SparkNLP, PyTorch, TensorFlow.

• Strong understanding of ML algorithms and deep learning architectures.

• Experience with big data tools (Pyspark/Polars) and data streaming (Dataflow).

• Knowledge of ETL/ELT processes and data preprocessing techniques.

• Deep familiarity with model deployment and monitoring in production environments.

• Strong problem-solving skills and ability to work in a collaborative environment.

• Experience with workflow orchestration tools (GCP Workflows, Airflow).

• Familiarity with experiment tracking and model versioning tools (MLflow).

• Understanding of SQL and noSQL databases.

• Experience in developing recommender systems, fraud detection systems, or similar ML applications.

• Experience with GCP and deployment and maintaining models on GCP.

Studies show that women and people from underrepresented groups often hesitate to apply unless they meet all the criteria. If you’re excited about this role but don’t meet every single requirement, we encourage you to apply anyway. You might be just the person we need!

• 30 mins chat with Julie & Valentina

• 30 mins chat with the hiring manager, Harsha

• 30 mins chat with our CDSO, Martin

• A case study on a mission related to the role

• Final call with our CEO, Maurice