SmartRecruiting is looking on behalf of its client for a hardworking and creative Machine Learning Engineer FTE.
DataTech company operating in the financial and professional services sector. We are creating a privacy preserving, sector agnostic, plug and play, data access and data collaboration platform at a scale that makes it critical infrastructure.
- As an Machine Learning Engineer, you will apply your expertise to research new Machine Learning models that meet client demands and allow to further scale its state-of-the-art Federated Learning platform.
- You will design, research, architect and develop the ML models in close collaboration with the other Machine Learning engineers, developers and business colleagues.
- Demonstrated experience in software engineering, e.g. development and code review best practices, algorithms, data structures, object-oriented design, design patterns and microservices.
- Proficiency in Python and advanced knowledge of some of the following Python libraries:
- ML (e.g. NumPy, Pandas, SciPy, matplotlib, scikit-learn, statsmodels, Numba, PySpark etc.).
- Deep Learning (e.g. TensorFlow, PyTorch, Keras, MXNet etc.). o Distributed/parallel processing (e.g. Spark or Ray).
- Experience or familiarity with MLOps tools such as MLFlow, Kubeflow, DVC or Sacred.
- Applied knowledge of statistical methods, including quantitative analysis (e.g. time-series analysis/forecasting, clustering, dimensionality reduction, transfer learning).
- Familiarity with SQL and NoSQL data modelling.
- Practical experience in core data engineering techniques, e.g. data acquisition, pre-processing (cleansing, enrichment), normalisation, derivation, validation, and publishing.
- Good communication skills in order to collaborate on productionising ML/NLP projects with the other data scientists and/or interact with companie’s clients in order to pitch/present on your ML implementation.
- Passionate about learning new techniques and discussing creative ML solutions with the team.
Nice to have:
- MSc or PhD in Computer Science, Mathematics, or relevant data technology field and/or professional work experience in software development, data engineering, data science, or machine learning.
- Knowledge of NLP techniques (e.g. NED/NER, data point extraction, document classification etc.) and Python NLP libraries (e.g. NLTK, spaCy, TextBlob, Stanford CoreNLP, Gensim, polyglot etc.).
- Demonstrated software engineering knowledge, e.g. development, UML, mocking and code review best practices, algorithms, data structures, object-oriented design, architectural patterns, design patterns and microservices.
- Experience designing software architecture and building large-scale machine learning pipelines using primarily Python-based industry-standard toolkits to solve real-world problems.
- Demonstrable experience with containerisation and orchestration.
- Knowledge of privacy technologies.
- Knowledge of federated learning.
- Knowledge of distributed computing and security
- Understanding of distributed ledger technologies e.g. Hyperledger Fabric.
- Knowledge of data programming/weak supervision (e.g. Snorkel).
- Experience with human-in-the-loop pipelines (e.g. crowdsourcing).
- Familiarity with reinforcement learning.
- You will operate on a “remote work first” basis, offering competitive packages.