Senior Machine Learning Engineer

Currently, we are looking for a Senior Machine Learning Engineer who will be a part of the general-purpose data science team.

In this position, you will often communicate with a customer and consult both technical and non-technical team members regarding questions within your domain of machine learning and data-driven solutions.

Responsibilities:

Understand business needs and restrictions and offer appropriate technical solutions in the domain of data-driven applications, describe qualities and limitations of proposed approaches to non-technical people.
Work with all types of data including tabular, visual and textual data, state data collection and labelling requirements.
Design and implement machine learning solutions from data collection, labeling and preprocessing to model fine-tuning, validation and optimization to solution deployment and monitoring
Integrate machine learning solutions into existing data processing pipelines and infrastructures.

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)

Ability to develop a simple API in flask or a more complex one in FastAPI and wrap it up in a docker.
Experience deploying models on the cloud or taking them from the shelf in AWS or any other cloud.
Being comfortable with optimizing models for specific hardware, either on cloud or edge devices.
Knowledge of architectural patterns of data-driven applications scalable on-prem and on a cloud.
Familiarity with non-relational databases and, ideally, instruments similar to Apache Spark.
A few examples of machine learning pipelines being optimized for high-load or low delay.

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!

If you fit the description above, we’d love to hear from you! Email us at hrm@indatalabs.com.