Trust & Safety ML Engineer safeguarding Hapiko’s generative - AI sticker printer for children. Owning moderation pipelines, safety evaluations, classifiers, red teaming, and compliance systems.
About the role
- Senior Machine Learning Engineer working on ML-based approaches for analyzing biomedical data at BenchSci. Collaborating with experts to expedite drug discovery through knowledge enrichment.
Responsibilities
- Analize and manipulate a large, highly connected biological knowledge graph constructed of data from multiple heterogeneous sources, to identify data enrichment opportunities and strategies.
- Work with data and knowledge engineering experts to design and develop knowledge enrichment approaches/strategies that can exploit data within our knowledge graph.
- Provide solutions related to classification, clustering, more-like-this-type querying, discovery of high value implicit relationships, and making inferences across the data that can reveal novel insights.
- Deliver robust, scalable and production-ready ML models, with a focus on optimizing performance and efficiency.
- Architect and design ML solutions, from data collection and preparation, model selection, training, fine-tuning and evaluation, to deployment and monitoring.
- Collaborate with your teammates from other functions such as product management, project management and science, and other engineering disciplines.
- Sometimes provide technical leadership on Knowledge Enrichment projects that seek to use ML to enrich the data in BenchSci’s Knowledge Graph.
- Work closely with other ML engineers to ensure alignment on technical solutioning and approaches.
- Liaise closely with stakeholders from other functions including product and science.
- Help ensure adoption of ML best practices and state of the art ML approaches within your team(s).
- Participate in various agile rituals and related practices.
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.
- A proven track record of delivering complex ML projects working alongside high performing ML, data and software engineers using agile software development.
- Demonstrable ML proficiency with a deep understanding of how to utilize state of the art NLP and ML techniques.
- Mastery of several ML frameworks and libraries, with the ability to architect complex ML systems from scratch. Extensive experience with Python and PyTorch.
- Track record of contributing to the successful delivery of robust, scalable and production-ready ML models, with a focus on optimizing performance and efficiency.
- 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, and a deep understanding of how to implement solutions using Retrieval Augmented Generation (RAG) architectures, including both Graph RAG and Vector RAG.
- Experience with graph machine learning (i.e. graph neural networks, graph data science) and practical applications thereof. Your experience working with Knowledge Graphs, ideally biological, and a familiarity with biological ontologies complement this.
- 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, like SQL, Cypher or Pandas.
- A can-do, proactive and assertive attitude - your manager believes in freedom and responsibility and helping you own what you do. You will excel if this environment suits you.
- You have experience working in cross-functional teams with product managers, scientists, project managers, and engineers from other disciplines (e.g. data engineering).
- Ideally, you have worked in the scientific/biological domain with scientists on your team.
- Outstanding verbal and written communication skills. Can clearly explain complex technical concepts/systems to engineering peers and non-engineering stakeholders.
- A growth mindset continuously seeking to stay up-to-date with cutting-edge advances in ML/AI, complemented by actively engaging with the ML/AI community.
Benefits
- A great compensation package that includes BenchSci equity options
- A robust vacation policy plus an additional vacation day every year
- Company closures for 14 more days throughout the year
- Flex time for sick days, personal days, and religious holidays
- Comprehensive health and dental benefits
- Annual learning & development budget
- A one-time home office set-up budget to use upon joining BenchSci
- An annual lifestyle spending account allowance
- Generous parental leave benefits with a top-up plan or paid time off options
- The ability to save for your retirement coupled with a company match!
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