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Senior Machine Learning Engineer - Graph ML

We are looking for a Senior Machine Learning Engineer to join our Knowledge Enrichment team at BenchSci. 


You will help design and implement ML-based approaches to analyze, extract and generate knowledge from complex biomedical data such as experimental protocols and from results from several heterogeneous sources, including both publicly available data and proprietary internal data, represented in unstructured text and knowledge graphs. You will work alongside some of the brightest minds in tech, leveraging state of the art approaches to deliver on BenSci’s mission to expedite drug discovery. Knowledge Enrichment is at the core of this challenge as it ensures we can reason over and gain insights from an extensive, accurate, and high quality representation of biomedical data.


The data will be leveraged in order to enrich BenchSci’s knowledge graph through classification, discovery of high value implicit relationships, predicting novel insights/hypotheses, and other ML techniques. You will collaborate with your team members in applying state of the art ML and graph ML/data science algorithms to this data. 


You are comfortable working in a team that pushes the boundaries of what is possible with cutting edge ML/AI, challenges the status quo, is laser focused on value delivery in a fail-fast environment.


You Will:
  • Analyze and manipulate a large, highly-connected biological knowledge graph constructed of data from multiple heterogeneous sources, in order 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 optimising 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, as well as 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


You Have:
  • 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 optimising 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, 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
  • This is complimented by your 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 can-do proactive and assertive attitude - your manager believes in freedom and responsibility and helping you own what you do; you will excel best if this environment suits you
  • You have experience working in cross-functional teams with product managers, scientists, project managers, 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, complimented by actively engaging with the ML/AI community


About BenchSci:

BenchSci's mission is to exponentially increase the speed and quality of life-saving research and development. We empower scientists to run more successful experiments with the world's most advanced, biomedical artificial intelligence software platform. 

Backed by Generation Investment Management, TCV, Inovia, F-Prime, Golden Ventures, and Google's AI fund, Gradient Ventures, we provide an indispensable tool for scientists that accelerates research at 16 top 20 pharmaceutical companies and over 4,300 leading academic centers. We're a certified Great Place to Work®, and top-ranked company on Glassdoor.


Our Culture:

BenchSci relentlessly builds on its strong foundation of culture. We put team members first, knowing that they're the organization's beating heart. We invest as much in our people as our products. Our culture fosters transparency, collaboration, and continuous learning. 

We value each other's differences and always look for opportunities to embed equity into the fabric of our work. We foster diversity, autonomy, and personal growth, and provide resources to support motivated self-leaders in continuous improvement. 

You will work with high-impact, highly skilled, and intelligent experts motivated to drive impact and fulfill a meaningful mission. We empower you to unleash your full potential, do your best work, and thrive. Here you will be challenged to stretch yourself to achieve the seemingly impossible.  Learn more about our culture.


Diversity, Equity and Inclusion: We're committed to creating an inclusive environment where people from all backgrounds can thrive. We believe that improving diversity, equity and inclusion is our collective responsibility, and this belief guides our DEI journey.  Learn more about our DEI initiatives.


Accessibility Accommodations: Should you require any accommodation, we will work with you to meet your needs. Please reach out to talent@benchsci.com.


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What You Should Know About Senior Machine Learning Engineer - Graph ML, BenchSci

At BenchSci, we’re on the lookout for a talented Senior Machine Learning Engineer specializing in Graph ML to join our Knowledge Enrichment team in London. This isn’t just any job; it's an opportunity to dive deep into the fascinating world of biomedical data. You will play a critical role in designing and implementing innovative ML-based methods that analyze, extract, and generate vital knowledge, transforming complex datasets from various sources, both public and proprietary, into actionable insights. Step into a creative environment where you’ll collaborate with brilliant minds who share your passion for technology. You will work on enriching BenchSci’s extensive knowledge graph, applying advanced ML techniques to uncover hidden relationships and predict valuable insights. Your day-to-day will be dynamic, as you utilize cutting-edge algorithms to enhance the quality of biomedical data representation. We believe in a collaborative culture where everyone is valued. If you thrive in environments that celebrate creativity and rigorously challenge the status quo, you’ll fit right in. Your leadership skills will shine as you apply your expertise to ensure our ML solutions are robust, scalable, and production-ready. Plus, at BenchSci, we prioritize continuous growth and development, ensuring you stay ahead of the curve in this ever-evolving field. Join us to make a real difference in expediting drug discovery and revolutionizing research efficiency. So, if you're ready to push boundaries and drive innovation with us, we can’t wait to meet you!

Frequently Asked Questions (FAQs) for Senior Machine Learning Engineer - Graph ML Role at BenchSci
What are the primary responsibilities of a Senior Machine Learning Engineer at BenchSci?

As a Senior Machine Learning Engineer at BenchSci, your main responsibilities will include analyzing and manipulating complex biomedical data, developing ML solutions for data enrichment, and collaborating closely with various team members to implement state-of-the-art ML techniques. You’ll focus on delivering robust and production-ready ML models and collaborate on cross-functional projects that drive innovation in drug discovery.

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What qualifications are required for the Senior Machine Learning Engineer position at BenchSci?

Candidates applying for the Senior Machine Learning Engineer role at BenchSci should have a minimum of 3-5 years of experience in the field, preferably with a PhD in Software Engineering or Computer Science. A deep familiarity with ML frameworks, Python, and practical applications of graph machine learning is essential. Moreover, standout candidates will show demonstrated success in leading complex ML projects and a knack for working in agile environments.

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What kind of projects will a Senior Machine Learning Engineer work on at BenchSci?

In the Senior Machine Learning Engineer role at BenchSci, you’ll work on exciting projects centered around analyzing and enriching vast biomedical knowledge graphs. You will tackle challenges such as data classification, discovering implicit relationships, and applying graph neural networks to draw meaningful insights from diverse data sources, all contributing towards expediting drug discovery.

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What is the team culture like for Senior Machine Learning Engineers at BenchSci?

The team culture at BenchSci is built on fostering transparency, collaboration, and continuous learning. As a Senior Machine Learning Engineer, you’ll become part of an inclusive environment where diversity is celebrated, and individual growth is prioritized. Your ideas will be welcomed, and you will have the freedom to explore creative solutions while contributing to impactful projects that challenge traditional norms.

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How does BenchSci support the professional development of its Senior Machine Learning Engineers?

BenchSci is deeply committed to the professional growth of its team members. As a Senior Machine Learning Engineer, you will have access to resources and opportunities for continuous learning and improvement. The company encourages engagement with the ML/AI community and provides avenues for you to stay updated with the latest advances in machine learning technologies.

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Common Interview Questions for Senior Machine Learning Engineer - Graph ML
Can you explain your experience with graph machine learning and how it applies to this role?

When answering this question, highlight specific projects where you utilized graph machine learning techniques. Explain the algorithms you used, the challenges you faced, and the outcomes of your contributions. Be sure to relate your explanations to how these experiences can benefit BenchSci's Knowledge Enrichment goals.

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What ML frameworks and libraries are you proficient in, and how have you applied them in your past projects?

Share your experience with different ML frameworks like TensorFlow and PyTorch, emphasizing any instances where you’ve implemented complex ML systems. Discuss how your knowledge of these tools will help you in delivering production-ready models at BenchSci, focusing on performance and efficiency.

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How do you ensure the robustness and scalability of the ML models you develop?

Discuss your approach to model testing and validation, including the metrics you use to gauge performance. Highlight your experience in utilizing version control and documentation to maintain consistency and encourage scalability. Emphasize how these practices can be integrated into BenchSci’s ML workflows.

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Describe a time when you had to collaborate with non-technical stakeholders. How did you ensure effective communication?

Provide an example illustrating how you translated complex technical concepts into understandable terms for non-technical team members. Emphasize the importance of fostering collaboration and gaining buy-in while working on cross-functional projects at BenchSci.

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What strategies would you use to identify data enrichment opportunities within a knowledge graph?

Speak about the analytical techniques and methodologies you’ve employed to explore knowledge graphs in your previous roles. Discuss how these efforts align with BenchSci’s goal of enriching biomedical data and achieving clarity in complex datasets.

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Can you talk about your experience with the full ML development lifecycle?

Provide an overview of your familiarity with the entire ML lifecycle, from data collection and preprocessing to model training and deployment. Sharing specific experiences can demonstrate how well you understand the complexities of the process as a Senior Machine Learning Engineer at BenchSci.

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How do you stay updated with advancements in ML/AI technology?

Detail the resources and communities you engage with to keep your skills sharp, such as attending conferences, online courses, or working on personal projects. Explain how this commitment to growth can benefit your role at BenchSci.

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What are your thoughts on working in a fail-fast environment?

Discuss the value of iterative processes and the benefits of learning from failures. Highlight how a fail-fast approach can lead to innovation and improved project outcomes, especially in a cutting-edge field like machine learning at BenchSci.

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How do you approach problem-solving when faced with a complex ML challenge?

Outline your multi-step approach to problem-solving, including how you analyze the problem, research solutions, and implement strategies effectively. Relate your approach back to potential scenarios that might arise in the Senior Machine Learning Engineer role at BenchSci.

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Are you comfortable providing technical leadership on projects?

Express your experience in guiding teams and leading projects, emphasizing your ability to mentor junior engineers and facilitate collaboration. Share examples that showcase your leadership style and its relevance in driving successful outcomes at BenchSci.

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At BenchSci, our mission is to exponentially increase the speed and quality of life-saving research. We do so by empowering scientists with the world’s most advanced biomedical artificial intelligence so they can run more successful experiments.

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DATE POSTED
December 3, 2024

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