2+ years as Team Leader, working with Agile methodologies
5+ years of industry and research experience in developing machine learning products
Excellent written and verbal communication skills, with prior experience explaining assumptions, conclusions, and methodology to both internal and external customers
Ability to frame business requirements into Machine Learning problems that you enjoy solving
A strong predilection for good software and the processes that make it
Mathematical foundation including: linear algebra, vector calculus, probability, and statistics. Experience implementing this math effectively in software (e.g. Python, numpy)
Strong foundation in machine learning & deep learning concepts including: supervised and unsupervised learning, transfer learning, ensembling, classification, regression, clustering, bias & variance, regularization, overfitting & underfitting, Logistic & Linear regressions, Decision Trees, MLP, RNNs
Strong foundation in natural language processing concepts including: bag-of-words & TF-IDF, n-grams, word & text embedding, NER, transformers, text classification & similarity
Proficiency in Python and PyData stack (numpy, scipy, pandas, scikit-learn)
Fluency with popular NLP libraries (spaCy, NLTK, transformers)
Hands-on experience with Deep Learning frameworks such as PyTorch
Experience working in a Linux environment
Basic Git knowledge: creating and merging branches, cherry-picking commits, examining the diff between two hashes. More advanced Git usage is a plus, particularly: development on feature-specific branches, squashing and rebasing commits, and breaking large changes into small, easily-digestible diffs
Experience with SQL
Understanding of algorithm complexity and performance implications
Knowledge of classical data structures and algorithms

Буде плюсом

Work closely with Product Managers, Data Scientists, and fellow ML Engineers to frame Machine Learning problems within the business context
Provide mentorship to other ML engineers and Data Scientists in the teamBe hands-on and involved with every stage of the ML product development cycle
Design, extend and review ML experiments and solutions
Evaluate, justify and communicate ML models’ performance to various stakeholders
Write clean and tested code that can be maintained and extended by other fellow ML engineers
Influence the Machine Learning development culture to be innovative
Establish and extend standards and best practice for ML engineering
Contribution to research activities in ML domain inside the company