Senior Machine Learning Engineer
Skills
Cypher Query LanguageData CollectionData ManipulationData ScienceModel SelectionNatural Language ProcessingPandas
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Minimum 3, ideally 5+ years of experience working as an ML engineer
- Some experience providing technical leadership on complex projects
- Degree, preferably PhD, in Software Engineering, Computer Science, or a similar area
- Proven track record of delivering complex ML projects working alongside high performing ML, data, and software engineers using agile software development
- Mastery of several ML frameworks and libraries, with the ability to architect complex ML systems from scratch
- Extensive experience with Python and PyTorch
- Experience with the full ML development lifecycle from architecture and technical design, through data collection and preparation, model selection, training, fine-tuning and evaluation, to deployment and maintenance
- Familiarity with implementing solutions leveraging Large Language Models, as well as a deep understanding of how to implement solutions using Retrieval Augmented Generation (RAG) architecture
- Experience with graph machine learning (i.e. graph neural networks, graph data science) and practical applications thereof
- Experience working with Knowledge Graphs, ideally biological, and a familiarity with biological ontologies
- Experience with complex problem solving and an eye for details such as scalability and performance of a potential solution
- Comprehensive knowledge of software engineering, programming fundamentals, and industry experience using Python
- Experience with data manipulation and processing, such as SQL, Cypher, or Pandas
- A proactive and assertive attitude that aligns with a culture of freedom and responsibility
- Experience working in cross-functional teams with product managers, scientists, project managers, and engineers from other disciplines (e.g. data engineering)
- Outstanding verbal and written communication skills
- Growth mindset and continuous engagement with the ML/AI community
Nice to have
- Having worked in the scientific/biological domain with scientists
- Ability to explain complex technical concepts to non-engineering stakeholders
Day to day
- Help design and implement ML-based approaches to analyse, extract, and generate knowledge from complex biomedical data
- Collaborate with team members to apply state of the art ML and graph ML/data science algorithms to enrich BenchSci’s knowledge graph
- Deliver robust, scalable, and production-ready ML models, focusing on performance and efficiency
Hiring process
- This information is not provided.