Forward Deployed Engineer, Biorelate
Salary not provided
Airflow
Redshift
Spark
Snowflake
BigQuery
dbt
Mid and Senior level
Remote from UK
AI biomedicine analysis and trend platform
Open for applications
AI biomedicine analysis and trend platform
21-100 employees
HealthcareB2BArtificial IntelligenceAnalyticsSaaSMedTech
Open for applications
Salary not provided
Airflow
Redshift
Spark
Snowflake
BigQuery
dbt
Mid and Senior level
Remote from UK
21-100 employees
HealthcareB2BArtificial IntelligenceAnalyticsSaaSMedTech
Company mission
To curate knowledge to advance the world's most promising biomedical innovations.
Role
Who you are
- 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
- 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
- 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)
- 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
What the job involves
- 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
- 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
- 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
- Reports to: VP Technology
Application process
- 1. Applied
- 2. Review
- 3. Interview
- 4. Hired
Our take
With an increasing number of data analysis and compilation tools and methods available across the sciences, a tangible channel for actionable insights based upon accurate analysis is vital. With so many discoveries, it’s easy for information to outpace digestion. Biorelate’s Galactic AI cloud computing software is a solution to this, providing a comprehensive and ever-refreshing catalogue of biomedical research for scientists.
The Galactic AI platform provides actionable and relevant data by “auto-ingesting” patents, clinical trials, journal articles, and more. It works with a range of computer technologies including TensorFlow and Google Cloud to remain scaleable and accessible, all with actions in place to keep data secure and its throughline transparent.
BioRelate was founded in 2014 and continues to secure funding into the present. Its funding is being invested toward boosting its product capabilities, sales, and marketing. BioRelate is also planning an expansion into the US.
Freddie
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 3 rounds)
Nov 2022
$8.8m
SERIES A
Sep 2018
$1m
SEED
Total funding: $9.9m
Company HQ
Chorlton-on-Medlock, Manchester, UK
Leadership
Daniel Jamieson
(CEO)Former member of the Early Career Advisory Panel for the Biochemical Society, PhD student at the University of Manchester, and was a Bioinformatician at both Pfizer and Merck.
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