We’re currently seeking a Senior Machine Learning Engineer to join our versatile data science team. This role involves frequent interaction with clients, providing expert consultation on machine learning and data-driven solutions to both technical and non-technical stakeholders.

Responsibilities:

• Translate business requirements and constraints into suitable technical solutions for data-driven applications, clearly explaining the strengths and limitations of proposed approaches to non-technical audiences.
• Work proficiently with diverse data types, including tabular, visual, and textual data, and define data collection and labeling requirements.
• Own the full machine learning lifecycle, from data collection, labeling, and preprocessing, through model fine-tuning, validation, and optimization, to solution deployment and ongoing monitoring.
• Seamlessly integrate machine learning solutions into existing data processing pipelines and infrastructure.

Requirements:

Fundamental (must have 80% of these)

• Knowledge of linear algebra and calculus sufficient for explaining concepts such as backpropagation and regularization.
• Knowledge of fundamental machine learning theory such as soft optimization and statistical learning.
• Knowledge of classical machine learning algorithms such as ensembles, clustering and dimensionality reduction.
• Knowledge of basic deep learning architectures such as MLPs, CNNs, RNNs, and, maybe, a little bit of Attention.
• A brief touch to theoretical computer science and its algorithms (we don’t ask inversion of binary trees).
• Experience with basic software engineering instruments such as python, bash, git and some relational databases.
• Understanding of Data Science Lifecycle from data collection to model training, deployment and maintenance.

Domain

(having 50% of these is already great)

• Proficiency in developing and containerizing APIs, from simple Flask implementations to more complex FastAPI applications, using Docker.
• Experience deploying machine learning models in cloud environments (e.g., AWS) or leveraging pre-trained models from cloud platforms.
• Comfort optimizing models for specific hardware, whether deployed in the cloud or on edge devices.
• Knowledge of architectural patterns for scalable data-driven applications, suitable for both on-premise and cloud deployments.
• Familiarity with NoSQL databases and ideally, experience with distributed computing frameworks like Apache Spark.
• Demonstrable experience optimizing machine learning pipelines for high-load or low-latency requirements.

Scaling up

(a few ways to stand out)

• Ability to read, estimate and implement solutions from scientific papers without code provided.
• Outsmarting everyone in fine-tuning and squeezing everything out of the machine learning model. (Kaggle)
• Building solutions with a personal mixture of engineering and modeling, from data to deployment.
• Fluent knowledge of English or any additional language across four major skills. (ideally C1+)

You will work with smart people who love to solve hard problems, and who not only expect but also foster high performance!