— 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 team
— Be 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

Чем Вы должны обладать

Обязательно

— 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