Position : Machine Learning Engineer
Specialization : NLP and Approximate Reasoning
Level : Senior
Workload : Part-time
sci.AI is a platform for researchers to uncover hypotheses and find new applications of existing biomedical discoveries. Its technology applies natural language processing to read scientific papers and reveals the relationships among points of interest with the help of AI reasoning methods.
We are looking for the expert in mathematical foundations of probability, fuzzy sets, graph theory, Markov models, semantics and machine learning applied to natural language processing. Areas of particular interest include application of neural networks for semantic parsing, clustering of the structured and unstructured textual data, approximate reasoning and zero/one-shot learning.
You will support sci.AI R&D team in architecture design and algorithms development. Tasks will include but not limited to developing/validating of math apparatus of the algorithms, designing architecture of the NN, preparing implementation requirements, coding advanced parts of the applications, evaluating accuracy and performance of the algorithms.
Active interest in biomedical progress is a must to share common motivation with the whole team.
- MS/PhD in Computer Science, Math, Physics, Engineering, Statistics or other technical field;
Experience and Skills in:
- Python and R;
- Associated libraries, including TensorFlow and Theano;
- Core NLP techniques and tasks: NER, WSD, Semantic Parsing, Syntax Parsing, Co-occurence and Dependences Parsing, Paraphrase Identification etc.;
- RDF, OWL, SPARQL, SHACL and ontologies building;
- NoSQL and Graph databases. Specifically, Mongo;
- Large scale computing;
- Experience using compiled programming languages (e.g., C/C++, Go, Java) for high-performance statistical computing is highly appreciated;
- Development on Unix platforms;
- Experience with the Elasticsearch is highly desired;
- Worked on 5 machine learning projects with team of 2 persons at least;
- Intermediate English language communication and writing skills at least;
- Interest/ Knowledge in the biomedicine is a must as long as you’ll be involved in analysis of the machine-generated results together with the biologists;
- Math Nerd;
- Academic background is highly desired to be familiar with the processes of research conduction and dissemination;
- Strong analytical, synthesizing, critical thinking and reasoning skills;
- Enjoying teaching and mentorship roles;
- Delivering. Problem solver and result oriented;
- Like writing. Project documentation, specification design, communication via email and messengers is an integral part of daily work;
- Punctual, responsible and reliable;
- Quality obsessed;
- Constant learner;
- Interested in biomedical sciences;