Full-Time

Lead Machine Learning Engineer Graph ML

Posted on 11/11/2024

BenchSci

BenchSci

201-500 employees

AI-powered preclinical R&D evidence platform

No salary listed

London, UK

Remote

Category
AI & Machine Learning (3)
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Required Skills
LLM
Agile
Python
Data Science
Pytorch
SQL
Machine Learning
Pandas
Requirements
  • Minimum 5, ideally 8+ years of experience working as an ML engineer in industry
  • Technical leadership experience, including leading 5-10 ICs on complex projects in industry
  • 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 engineers using agile software development
  • Demonstrable ML proficiency with a deep understanding of how to utilise 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 successfully delivering 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
  • Strong skills related to implementing solutions leveraging Large Language Models, as well as a deep understanding of how to implement solutions using Retrieval Augmented Generation (RAG) architecture
  • Expertise in 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 data manipulation and processing, such as SQL, Cypher or Pandas
  • 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
Responsibilities
  • Analyse 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 at BenchSci
  • Participate in and sometimes lead various agile rituals and related practices

BenchSci provides a preclinical research platform called ASCEND that uses artificial intelligence and visual machine learning to map disease biology. The platform works by extracting evidence from published experiments, internal data, and vendor catalogs to help scientists generate hypotheses and identify experimental risks. Unlike traditional databases, BenchSci integrates these diverse data sources into a unified map that guides the entire research planning process across an enterprise. The company's goal is to increase the efficiency and success rate of research and development by helping scientists make better data-driven decisions.

Company Size

201-500

Company Stage

Series D

Total Funding

$161M

Headquarters

Toronto, Canada

Founded

2015

Simplify Jobs

Simplify's Take

What believers are saying

  • Merck renewed two-year ASCEND contract in 2026 for deeper AI workflow integration.
  • Multi-year Mila partnership develops AI for hypothesis generation and assay prediction.
  • Thermo Fisher collaboration enhances preclinical R&D productivity with AI tools.

What critics are saying

  • Insilico Medicine's Pharma.AI erodes market share from Merck and Sanofi in 12-24 months.
  • Recursion Pharmaceuticals maps biology 10x faster, commoditizing BEKG in 6-12 months.
  • OpenAI o1-pro obsoletes neurosymbolic AI as pharma adopts generalist LLMs in 18-24 months.

What makes BenchSci unique

  • ASCEND employs neurosymbolic AI with BEKG for evidence-based disease biology mapping.
  • Platform integrates multi-omics, publications, and internal data for customized insights.
  • Deep biopharma workflow expertise enables multi-hop scientific reasoning like scientists.

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Benefits

Remote-first culture

Equity options

15 days vacation + additional day every year

Unlimited flex time

Comprehensive health & dental benefits

Psychotherapist services

Annual Learning & Development budget

Home office set-up budget

Wellness, lifestyle & productivity spending account

Growth & Insights and Company News

Headcount

6 month growth

-3%

1 year growth

0%

2 year growth

0%
Contract Pharma
Dec 9th, 2025
BenchSci Extends ASCEND Agreement with Merck

BenchSci extends ASCEND agreement with Merck. The renewed agreement supports Merck's efforts to integrate AI more deeply into its scientific workflows. BenchSci, a leading provider of AI software for biopharma research and development, renewed its two-year contract with Merck, known as MSD outside of the United States and Canada. The renewed agreement supports Merck's efforts to integrate AI more deeply into its scientific workflows. With ongoing access to ASCEND, scientists can more easily evaluate evidence and surface insights that inform early development decisions. BenchSci's ASCEND is the first neurosymbolic AI platform built to help biopharma understand disease biology at scale. At its core is the Biological Evidence Knowledge Graph (BEKG) - an experimentally grounded foundation that unifies diverse data sources, including peer-reviewed literature, multi-omics datasets, and clinical trial evidence. By combining the BEKG with advanced foundation models, ASCEND powers AI copilots and co-scientists that deliver rapid, explainable insights and enable faster, more confident research decisions. ASCEND also harmonizes each partner's internal data to create a secure, proprietary, and customized map of disease biology, forming a living, evolving foundation for discovery. "Seeing Merck's teams use ASCEND to unravel disease biology, strengthen hypotheses, and make more evidence-driven decisions underscores the real scientific value AI can deliver. This renewed agreement gives us the opportunity to deepen that impact and continue advancing how complex biological questions are explored in early discovery," said Liran Belenzon, CEO and Co-Founder, BenchSci.

BetaKit
Nov 12th, 2025
BenchSci inks multi-year partnership with Mila to develop AI for drug discovery

BenchSci inks multi-year partnership with Mila to develop AI for drug discovery. Toronto-based BenchSci has teamed up with Montréal's AI research centre Mila to build artificial intelligence (AI) systems that can automatically create new scientific hypotheses and forecast the outcome of drug-related tests before they are conducted. BenchSci and Mila say this work could mark "a major step" on the path to autonomous drug discovery. Together, the two organizations hope to develop AI models capable of biological inference, or the ability to generate hypotheses and predictions for the outcomes of experiments during the drug discovery process. They are also targeting experimental assay prediction - which they say would mark "a major step" on the path to autonomous drug discovery. Experimental assay prediction forecasts the outcome of drug-related tests before they are conducted. BenchSci, which sells AI-powered software for biopharmaceutical research and development (R&D), says it has struck a multi-year partnership with Mila to "push the boundaries of predictive and generative modeling in drug discovery." In a statement, BenchSci co-founder and CEO Liran Belenzon argued that this could lay "the groundwork for autonomous labs that accelerate innovation, uncover insights beyond human reach, and bring life-saving medicines to patients faster." BenchSci will gain access to Government of Canada-backed Mila's network of AI experts, which will work alongside the company's team of machine learning scientists. Their combined research will help BenchSci evolve its generative AI R&D platform with new inference models that build on its existing map of disease biology. "By joining forces with BenchSci, we're applying world-class AI research to one of the most complex and impactful challenges of our time - understanding biology at a level that can transform how life-saving medicines are discovered and developed," Mila executive vice-president Stéphane Létourneau said in a statement. Founded in 2015, BenchSci aims to use AI to better understand disease biology for drug discovery. Its software acts as an AI R&D assistant for preclinical organizations. BenchSci says it caters to 16 of the top 20 pharma firms - from AbbVie to Gilead Sciences, Merck, Novartis, Novo Nordisk, and Sanofi - and more than 4,500 research centres globally. BenchSci has raised $218 million to fuel its efforts from a group of investors that includes Al Gore's Generation Investment Management, Inovia Capital, TCV, F-Prime, Gradient Ventures (Google's AI fund), and Golden Ventures. This Mila collaboration follows recent partnerships BenchSci secured with pharma giants Sanofi and Thermo Fisher Scientific. Generative AI has also impacted BenchSci's internal operations. As Belenzon wrote in a blog post this July, BenchSci shifted to an "AI-first" strategy in 2025. Similar to Canadian tech peers like Shopify and Klue, Belenzon wrote that BenchSci is now asking whether or not AI can do a job before it hires new employees. Since BenchSci's $95-million Series D in mid-2023, the company has cut down the size of its team, shedding 70 employees (then 17 percent of staff) in early 2024. It cut another 83 employees (23 percent of its overall workforce at the time) in 2025 in layoffs that were first reported by The Globe and Mail, since confirmed by BetaKit, as the company has adopted AI to slash costs. LinkedIn Insights indicates that BenchSci's headcount has fallen 35 percent over the past two years, to 292 today. This year, BenchSci has also made some changes to its leadership team. The company announced John Jackson as CTO and Peter Grandsard as fractional senior vice-president of strategy, while COO Eran Ben-Ari and director of product Nim Fox have left.

SynBioBeta
Oct 2nd, 2025
BenchSci Partners with Thermo Fisher to Enhance R&D Productivity with AI Tools

BenchSci and Thermo Fisher Scientific have joined forces to develop AI-driven research tools aimed at improving the efficiency of scientific research and drug discovery.

BiopharmaTrend
Sep 30th, 2025
BenchSci and Thermo Fisher Collaborate to Develop AI Tools for Preclinical R&D

BenchSci and thermo fisher collaborate to develop AI tools for preclinical R&D.

Samfiru Tumarkin LLP
Jan 12th, 2024
BenchSci: Severance Packages

January 2024: BenchSci has eliminated 17 per cent of its workforce, or 70 jobs, as it furthers its investment into generative AI.

INACTIVE