
Monolith is trusted by the world’s top engineering teams to build self-learning models that empower your engineers to do less testing, more learning, and develop better quality products in half the time. Our end-to-end cloud platform enables any engineer to use their test data and expertise to solve intractable physics problems. It's designed by engineers, for engineers. Quickly design AI pipelines and train models without advanced programming or data science experience. Understand how your design works and which parameters are most influential on performance. Use AI self-learning models to make predictions of how your design performs under different conditions. Find optimal values for key design parameters to meet performance targets and regulatory requirements. Monolith was founded in 2016 by Dr. Richard Ahlfeld, born from his PhD at Imperial College London and NASA. Boosted by joining the FoundersFactory 6-month Accelerator Programme in January 2018, he built a diverse team of engineers, data scientists, and software developers to achieve his company vision. Monolith was named a Gartner Cool Vendor for AI in Automotive.

Monolith is trusted by the world’s top engineering teams to build self-learning models that empower your engineers to do less testing, more learning, and develop better quality products in half the time. Our end-to-end cloud platform enables any engineer to use their test data and expertise to solve intractable physics problems. It's designed by engineers, for engineers. Quickly design AI pipelines and train models without advanced programming or data science experience. Understand how your design works and which parameters are most influential on performance. Use AI self-learning models to make predictions of how your design performs under different conditions. Find optimal values for key design parameters to meet performance targets and regulatory requirements. Monolith was founded in 2016 by Dr. Richard Ahlfeld, born from his PhD at Imperial College London and NASA. Boosted by joining the FoundersFactory 6-month Accelerator Programme in January 2018, he built a diverse team of engineers, data scientists, and software developers to achieve his company vision. Monolith was named a Gartner Cool Vendor for AI in Automotive.
What they do: AI SaaS platform that uses test and simulation data to accelerate engineering product development
Headquarters: London, UK
Founded: 2016
Founder / CEO: Dr. Richard Ahlfeld
Notable customers: BMW Group, Siemens, Vertical Aerospace, Webasto
Series A announced Aug 23, 2021 (reported £8.5M; total reported £10.6M); lead investor Insight Partners
| Company |
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Engineering product development — reducing test-programme time and cost, improving test data quality, and predicting performance from simulations/tests.
2016
Software / AI for Engineering
£8.5M (reported)
Round reported to bring total raised to £10.6M; other participants named include Pentech, TouchStone Capital Group, Alejandro Agag, Apex Black, and Stanford Angels of the UK.
“Backers include Insight Partners (lead in Series A), Pentech, TouchStone Capital Group, IP Group, and other angel/investor participants”
Position Overview Monolith AI is seeking a talented Software Developer for an intensive 3-month migration project
transitioning existing client environments to our next-generation platform. This role requires a
developer who excels at rapidly understanding undocumented systems, extracting clarity from
complex database structures, and building practical migration tooling in Python. You'll work closely
with our 15-person technical team (currently doubling), collaborating extensively with Customer
Success and Product to ensure migrations meet defined business requirements while maintaining
high velocity.
Primary Responsibilities
discussions, and reverse engineering
structure
investigation
acceptance criteria
migration outcomes
standards
workflows
knowledge transfer
format
data integrity)
blockers
product decisions
Key Performance Indicators
migration criteria
documentation
Experience Required Qualifications
Technical Skills
Strong Python proficiency including handling pickle files, data processing,
and object serialization
relationships, and schema interpretation
data
behavior
Preferred Qualifications
Necessary Soft Skills Communication Excellence
discussions
Product)
Rapid Learning & System Investigation
experimentation
Velocity & Pragmatism
Collaboration & Stakeholder Management
Adaptability & Ownership
Results-Oriented Mindset
Key Challenges in This Role
object structures with limited documentation, requiring strong investigative skills
and ability to learn through code inspection and team discussions
build vs. what to defer, while ensuring migrations meet Customer Success
requirements—demands constant communication with tech lead about velocity and
trade-offs
acceptance criteria, and engineering to understand technical constraints—all while
maintaining project momentum and driving timely decisions on edge cases