OUR HIRING PROCESS:
We will review your application against our job requirements. We do not employ machine learning technologies during this phase as we believe every human deserves attention from another human. We do not think machines can evaluate your application quite like our seasoned recruiting professionals—every person is unique. We promise to give your candidacy a fair and detailed assessment.
We may then invite you to submit a video interview for the review of the hiring manager. This video interview is often followed by a test or short project that allows us to determine whether you will be a good fit for the team.
At this point, we will invite you to interview with our hiring manager and/or the interview team. Please note: We do not conduct interviews via text message, Telegram, etc. and we never hire anyone into our organization without having met you face-to-face (or via Zoom). You will be invited to come to a live meeting or Zoom, where you will meet our INFUSEmedia team.
From there on, it’s decision time! If you are still excited to join INFUSE and we like you as much, we will have a conversation about your offer. We do not make offers without giving you the opportunity to speak with us live. After all, we consider our team members our family, and we want you to feel comfortable and welcomed.
We are looking for an experienced Machine Learning Engineer who specializes in the development of neural networks and algorithms for the B2B market.
Skills:
Good knowledge of Python (standard and ML libraries, ability to apply OOP);
Strong knowledge of database systems, both relational (e.g. MySQL, PostgreSQL) and NoSQL (e.g. MongoDB, Redis);
Experience with cloud platforms (AWS, GCP);
Experience with docker;
Strong knowledge of GPTChat;
Strong knowledge of probability theory and mathematical statistics;
MLOps/DevOps experience (preferable)
Requirements:
At least 5+ years of experience as a Machine Learning Engineer;
Ability to work both in a teams and independently;
Experience working with large volumes of data and databases;
Understanding the principles of building machine learning models;
Knowledge of CV models development;
Ability to work with GitHub;
Systems thinking, desire to understand the subject area and business processes;
Technical higher education (BSc in Computer Science, Mathematics or similar field; Master’s degree is a plus);
Responsibilities:
Design, build, and maintain ML infrastructure that is efficient, scalable, and reliable;
Set technical direction and priorities for the ML-infra;
Monitor, troubleshoot, and optimize the performance of machine learning models in production;
Professionally mentor data engineers;
Work with data scientists and software engineers to deploy and maintain machine learning model;
Collaborate with other teams to ensure alignment and successful delivery of projects. Communicate technical solutions and trade-offs to non-technical stakeholders;
Stay up-to-date with the latest trends and technologies in the ML field;
Use data visualization tools to create visualizations that help data scientists and stakeholders understand the insights from the data;
Research existing solutions to find the best ones that cover business requirements;
Researching and experimenting to validate different hypotheses of the solution;