Senior Machine learning Engineer (GraphRAG) at BenchSci

London, England, United Kingdom

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, AIIndustries

Requirements

Minimum 3, ideally 5+ years of experience as an ML engineer or data scientist with a proven track record of delivering complex ML projects
Strong platform-building mindset: Experience building and maintaining ML-powered tools, APIs, or platforms for other developers or internal teams
Deep, hands-on experience with Graph Data Science (GNNs, link prediction) and Knowledge Graphs (e.g., Neo4j, Neptune) using query languages like Cypher
Strong, demonstrable experience with LLMs and RAG pipelines, with a specific focus or passion for query generation and retrieval optimization
Mastery of the Python data science and ML ecosystem (Pandas, PyTorch, scikit-learn) and experience with the full ML development lifecycle. Experience deploying services (e.g., FastAPI, Docker) is a plus
Degree (preferably PhD or Masters) in Computer Science, Data Science, or a related field
Excellent communication and collaboration skills, with a customer-centric mindset for understanding and supporting internal "consumer teams"

Responsibilities

Design, build, and maintain scalable retrieval tools and services (APIs, libraries) for internal consumer teams, focusing on our Knowledge Graph
Develop and optimize automated querying systems, including Text-to-Query models (e.g., Text-to-Cypher), to translate natural language questions into efficient graph queries, developing Cypher templates and query stitching
Partner with consumer teams to understand their needs, provide high-quality and well-documented tools, and drive the adoption of your retrieval platform
Own the end-to-end lifecycle of the ML models that power these tools, from data preparation and training to deployment, monitoring, and iteration
Collaborate cross-functionally with data engineers, ML engineers, and product managers to build a cohesive and powerful retrieval ecosystem

Skills

Key technologies and capabilities for this role

Machine LearningGraphRAGKnowledge GraphRetrieval-Augmented GenerationCypherText-to-CypherAPIsGraph TraversalML ModelsData PreparationModel TrainingModel DeploymentMonitoring

Questions & Answers

Common questions about this position

Is this position remote?

Yes, this is a full-time remote position.

What experience level is required for this role?

Minimum 3 years, ideally 5+ years of experience as an ML engineer or data scientist with a proven track record of delivering complex ML projects.

What key technical skills are needed for this Senior Machine Learning Engineer role?

Required skills include deep hands-on experience with Graph Data Science (GNNs, link prediction) and Knowledge Graphs (e.g., Neo4j, Neptune) using Cypher, strong experience with LLMs and RAG pipelines focusing on query generation and retrieval optimization, and mastery of Python data science tools like Pandas, PyTorch, and scikit-learn.

What is the team culture like at BenchSci?

The team pushes the boundaries of cutting-edge ML/AI, challenges the status quo, and is laser-focused on value delivery in a fail-fast environment, working alongside some of the brightest minds in tech.

What makes a strong candidate for this position?

A strong candidate has a platform-building mindset with experience building ML-powered tools and APIs for internal teams, a degree preferably PhD or Masters in Computer Science or related field, and excellent communication and collaboration skills with a customer-centric approach.

BenchSci

AI-driven platform for preclinical research

About BenchSci

BenchSci operates in the biotechnology sector, specializing in preclinical research and development. The company uses artificial intelligence and machine learning to create a detailed map of disease biology, which helps scientists understand existing research and improve their R&D efficiency. Its main product, ASCEND, is a platform that extracts evidence from various data sources to assist scientists in hypothesis generation and risk identification. BenchSci aims to enhance research capabilities and reduce risks in preclinical studies.

Toronto, CanadaHeadquarters

2015Year Founded

$156.6MTotal Funding

SERIES_DCompany Stage

AI & Machine Learning, BiotechnologyIndustries

201-500Employees

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

Risks

17% workforce reduction may impact morale and innovation at BenchSci.

Heavy reliance on external funding poses financial risks if future rounds falter.

Rapid team expansion could lead to integration challenges and inefficiencies.

Differentiation

BenchSci's ASCEND platform uses AI to map disease biology for drug discovery.

ASCEND helps scientists identify risks and generate hypotheses in preclinical R&D.

BenchSci's AI Reagent Selector improves reagent selection efficiency in drug development.

Upsides

$95M Series D funding boosts BenchSci's AI platform development and market reach.

Generative AI integration enhances ASCEND's predictive capabilities for hypothesis generation.

Recognition as a Best Workplace for Inclusion attracts top talent and fosters innovation.

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