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AI Summary
Lead customer integrations by designing and implementing architectures for knowledge graphs and AI agent workflows. Balance customer-facing delivery with contributions to the core data and ML platform to create reusable product capabilities.
Forward Deployed Engineer
Department: Engineering
Employment Type: Full Time
Location: UK
Description
Location: UK (Remote)
Reports to: VP Technology
We’re hiring a Forward Deployed Engineer to work directly with customers as they integrate our knowledge graph into their own systems.
Reports to: VP Technology
We’re hiring a Forward Deployed Engineer to work directly with customers as they integrate our knowledge graph into their own systems.
Our customers are typically data science and bioinformatics teams in pharmaceuticals or biotechs. You’ll work closely with customer technical teams to get our data products into production. That might mean loading the graph into Neo4j, exposing it through APIs, flattening it into analytical warehouses, or helping customers use it in LLM and agent workflows. Sometimes the customer team will own most of the implementation; sometimes you’ll build large parts of it yourself.
In this highly impactful role; you’ll take technical ownership from trial through to a working deployment. The role is roughly 70% customer-facing delivery work and 30% contribution to our core platform.
Based in our Engineering team you’ll blend customer delivery with engineering in our core platform team; turning repeated customer problems into reusable product capabilities.
What We Do
Research and Development in the pharmaceutical industry is time-consuming, expensive, and often overlooks valuable data insights. We’re changing that with AI-powered data science solutions that uncover hidden insights to support more efficient and informed drug discovery. Using AI and Natural Language Processing (NLP), our technology extracts and structures data from millions of scientific documents, identifying causal biological relationships and organising them into knowledge graphs. This advanced data science empowers bioinformaticians and biologists with a deeper understanding of the connections between diseases, proteins, and drugs.
Our team consists of biomedical and data science experts with real-world lab and pharmaceutical experience. These specialists ensure that our solutions are scientifically grounded, delivering accurate, reliable data to our customers.
The Company
Biorelate is a venture backed Series A startup (supported by established Tech investors). If you’re eager to collaborate with exceptional scientists, software engineers, and subject matter experts from various fields in a supportive, ego-free environment, Biorelate is the place for you.
Key Responsibilities
Lead customer integrations
- You’ll lead customers through the full implementation lifecycle - supporting technical scoping during pre-sales with the Senior Solutions Consultant, through onboarding and production rollout.
- You’ll help define what’s feasible, estimate effort, identify risks early, and then own delivery once the engagement starts.
Design integrations for AI and agent workflows
- A growing number of customers want to use our graph inside agentic systems. You’ll help design and implement architectures around graph RAG, MCP, retrieval systems, and APIs for agentic use cases.
Contribute to technical scoping
- You’ll partner with the Senior Solutions Consultant during the requirements and proposal stages to validate technical feasibility and shape realistic implementation plans.
- The Senior Solutions Consultant owns qualification and the commercial process; you help determine what can actually be delivered and what it will take.
Drive productisation
- You’ll identify reusable components, recurring integration patterns, and capabilities that belong in the core product rather than custom delivery work.
Contribute to the platform
- Outside of customer delivery, you’ll work alongside the wider engineering team on the core data and ML platform.
Skills, Knowledge and Expertise
We're looking for someone with strong experience building and deploying machine-learning-based data systems, who's ready to take that expertise into a high-impact customer-facing role. You should be comfortable working with the core building blocks of these systems rather than just on top of them.
A machine learning or data science background is valuable, you'll have first-hand experience of the problems our customers are wrestling with. Candidates who have combined experience across data engineering and data science would be ideal, but while we don't expect everyone to tick every box below, deep experience in either machine learning or data engineering is essential.
Machine Learning / Data Science Focus
- Proven experience deploying NLP systems or LLM-powered features into production
- Familiarity with recommendation and retrieval systems, retrieval-augmented generation (RAG) and vector databases.
- Practical experience building or debugging LLM-driven workflows, with opinions grounded in experience of production workflows.
- Demonstrable experience designing and running model evaluation and monitoring frameworks; offline metrics, online performance tracking
- Hands-on experience fine-tuning transformer-based models for domain- or task-specific use cases, including data preparation, training, and validation.
- Proven track record of building evaluation datasets or benchmarks from scratch - task design, annotation guidelines, quality control, and interannotator agreement.
Data Engineering Focus
- Comfortable moving between different architectural approaches for storing and accessing the data we produce. Able to articulate the trade-offs involved - OLAP vs transactional systems, warehouses vs distributed compute, when to create different kinds of search indices.
- Experience building MCP servers and developing APIs with a focus on ease of LLM integration.
- Experience with graph databases - Neo4J preferred as this is commonly used by our customers, but experience with other databases is still valuable.
- Experience managing schema evolution in production - handling downstream breakages, contract changes, and backwards-compatible migrations.
- Hands-on experience building or maintaining dbt projects (or equivalent transformation tooling), including modelling, testing, documentation, and CI.
- Experience working with distributed compute systems such as Spark or Beam to process large-scale datasets in production.
- Experience running production orchestration systems such as Airflow, Dagster, or Prefect - authoring DAGs, handling failures, and managing dependencies.
- Experience with cloud data warehouses (Snowflake, BigQuery, Redshift)
Customer Facing Focus
- Ability to communicate effectively with both highly technical customer groups and internally
- High personal quality standards, bringing positive energy and able to thrive with ambiguity in a scale up environment
Benefits
This is a rare opportunity to join Biorelate at an inflection point in our growth – and to genuinely shape how the company is seen and heard in the market. You'll work in a supportive team but you will have real autonomy to make this new role your own.
We offer competitive salaries, share options, enhanced family leave and significant opportunities for professional development. You’ll work alongside some of the brightest individuals, contributing to meaningful work that has a direct impact on global healthcare.
In addition, you can expect:
- Genuine Flexibility: We are a fully remote business with colleagues across the globe: manage your schedule, save time and money on commuting, and do your best work from the comfort of your home.
- Cutting-Edge Technology: You’ll work with the latest software tools and technologies; we’ll push you to genuinely be innovative and find new ways to solve the newest challenges.
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