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!
We'll help you harness AI and ML technologies to create innovative software solutions
We'll help you harness AI and ML technologies to create innovative software solutions
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