
hyperexponential · London
ABOUT HYPEREXPONENTIAL (HX) At hyperexponential, we're building the AI-powered platform that enables the world's most critical decisions in a £7 trillion indus...
At hyperexponential, we're building the AI-powered platform that enables the world's most critical decisions in a £7 trillion
industry, which risks to take, and how to price them. These are the decisions that shape real-world outcomes: whether rockets
successfully launch into space, autonomous vehicles make it to market, or communities recover after major storms.
Until now, insurance has been making billion-pound decisions using outdated tools. We're changing that. Our platform brings
together data, AI, and human expertise to give insurers the fastest path from submission to decision - helping them move faster,
act smarter, and take on more risk with confidence.
Backed by a16z, Highland Europe, and Battery Ventures, we're scaling globally - already trusted by nearly 50 of the world's
largest insurers, with zero churn and billions in premiums flowing through hx.
What began as a single product in one market has rapidly evolved into a multi-product, multi-territory platform powering every
stage of pricing and underwriting. AI is at the core of what we do - from building the world's first domain-specific AI peer
programmer for insurance (think GitHub Copilot with a PhD in actuarial science) to shaping agentic workflows that reinvent how
this industry operates.
What makes hx different is the people who build it. Here, impact isn't tied to title or tenure; it's defined by the challenges you
take on and the discipline you bring. Surrounded by peers who stretch you, you'll do the best, hardest work of your life in a
company engineered to endure.
If that sounds like you, join us in building what comes next.
Platform at hx builds the foundation that enables engineering teams to ship secure, scalable applications at velocity. We're
evolving towards an AI-first platform where self-service infrastructure, intelligent automation, and reusable components compound
developer productivity and operational excellence. This isn't about maintaining the status quo; we're reimagining how modern
software delivery works.
As the Staff Platform Engineer, you will own the technical direction of this evolution. You will define the architecture and
engineering standards that others build to, drive coherence across teams, and ensure the platform strategy is aligned to long-term
business outcomes. You will shape how AI-first, self-service infrastructure is done at scale, not just by delivering capabilities
yourself, but by creating the conditions for engineering teams across the organisation to move faster, more safely, and more
autonomously.
Check out our Engineering Candidate Hub for a behind the scenes look at the team you would be joining!
alignment across engineering leadership. The roadmap is understood, trusted, and actively referenced by stakeholders.
well-designed, well-documented, and genuinely reduce friction. Measurable reduction in Platform as a bottleneck for common
provisioning and deployment tasks.
Other engineers can build AI-native capabilities without requiring Platform's direct involvement in every initiative.
enforced through gatekeeping, but adopted because they are the path of least resistance. Measurable reduction in
misconfiguration, security exceptions, and operational incidents across the estate.
trade-offs, and platform thinking. You are sought out as a technical authority.
calls on foundational trade-offs such as build vs buy, depth vs breadth, short-term pragmatism vs long-term coherence and
brings the organisation along on those decisions.
holding a position under pressure, updating it when presented with better evidence, and ensuring decisions stick once made.
enablement approaches that multiply the effectiveness of the engineers around them, rather than being the primary hands-on
contributor to every initiative.
establishing patterns that make AI-augmented workflows the default for engineering teams across the organisation.
roadmap based on feedback, usage data, and a clear-eyed view of where friction exists and ensures the platform earns its
adoption rather than mandating it.
organisation thinks about reliability, security posture, and cost efficiency not just within Platform's direct remit.
leadership, translate engineering constraints into business language, and use commercial context to sharpen technical
priorities.
Recognised as someone who raises the capability of the team, not just the quality of the code. Models the engineering culture
the organisation is trying to build.
and iteration to solve
customers whose adoption and satisfaction drive success
practices based on data and feedback
best work. We're building something that asks for commitment and conviction, and we want you to feel excited by the opportunity
to grow with us.
At hx, we're committed to salary transparency. You'll always have clarity on pay early in the process - our Talent Partner will
share details with you during initial conversations - and we're working towards publishing salary information for all roles
globally.
Because we're building at the intersection of technology/SaaS and insurance, our roles don't always map neatly onto traditional
benchmarks. Our approach is to design compensation that's competitive in the market, fair across teams, and aligned with the
impact our people make.
Equity: We offer equity across all roles at hx, making it a significant component of total compensation. Your Talent Partner will
be able to share more details about this.
1. Initial call with our Talent team (30 minutes).
2. Manager Interview (60 minutes).
3. Technical Assessment (system design whiteboard) (120 minutes).
4. Values Interview with Tech Leadership (60 minutes).
5. We offer!
hxers are at the centre of everything we build. We know that progress depends on diverse perspectives, and we are committed to
creating an environment where everyone can thrive, grow, and make an impact. We recognise there is always more to do, and we take
responsibility for shaping a workplace that is not only diverse but genuinely inclusive.
Diversity is not just the right thing to do, it is key to solving the complex challenges we choose to take on. By welcoming people
from all backgrounds and experiences, we strengthen our ability to question assumptions, push boundaries, and design solutions
that endure.
If you're energised by complexity and motivated to grow, we encourage you to apply and join our global team.
If this opportunity resonates with you, we encourage you to apply or share it with your connections! Our dedicated talent team
reviews all applications, and we promise to provide feedback regardless of the outcome.
For more information about applying and to view other opportunities, you can visit our careers page.
Please note that background checks will be conducted as part of the hiring process to ensure compliance with our governance
policies. We handle all background checks sensitively and in full compliance with relevant regulations. All applicant data will be
processed in accordance with data protection regulations and our privacy policy.
The impact you will have: As Staff MLOps Engineer, you will define and build Elliptic's Enterprise MLOps platform. Elliptic has growing ML capability across several teams, an established model registry, and a maturing model risk management practice. What is missing is the unified platform layer that ties training, deployment, monitoring, and governance together into a coherent, scalable discipline. You will be responsible for creating that layer. Your platform will serve four distinct internal consumers, each with different needs: * Product Engineering teams building customer-facing models and customer data analytical models, who need reproducible training pipelines, CI/CD for model deployment, and low-latency serving infrastructure * Intelligence Research building frontier intelligence collection, predictive pre-screening models, and behavioural pattern detection, who need rapid experimentation, GPU orchestration, and dataset versioning * InfoSec who own the model registry and model risk management framework today, and need the platform to close execution gaps in audit trails, drift monitoring, and compliance reporting * Operations who own BI, usage prediction, and revenue opportunity signalling, and need scheduled batch inference, BI integration, and pipeline reliability The platform you build must enforce governance with enough rigour to satisfy a regulated financial crime context, while remaining flexible enough to avoid slowing down research teams who need to iterate quickly. This is a role for someone who has built ML infrastructure from the ground up before, who understands that a platform succeeds only when it is adopted, and who is comfortable making build-vs-buy decisions that others will adopt and use for years. What you will do: * Define the target-state MLOps architecture for Elliptic, covering model training pipelines, serving infrastructure, monitoring, feature management, and governance, and produce the architecture decision records that inform investment decisions * Make and document build-vs-buy-vs-stop recommendations with clear cost modelling and trade-off analysis, evaluating vendors, open-source tools, and managed services against Elliptic's constraints (AWS-primary, Databricks ecosystem) * Work with InfoSec to improve the existing model registry and model risk management framework, closing identified gaps in metadata, lineage, approval workflows, and drift/bias detection * Build model training pipelines, CI/CD for ML, and serving infrastructure, working directly with a small group of infrastructure engineers to ship production-grade platform capabilities * Instrument observability across the ML lifecycle: training metrics, serving latency and throughput, data quality, and prediction drift, integrating with Elliptic's existing observability stack * Work directly with data scientists and ML engineers across all four consumer groups to onboard them onto the platform, writing documentation, runbooks, and reference architectures that lower the barrier to self-service You will be a great fit here if you: * Have built MLOps platforms or ML infrastructure from the ground up, and can speak to what worked, what didn't, and why * Have operated in a regulated industry (e.g. compliance, financial) and have hands on experience building ML infrastructure to meet those regulatory demands * Think about ML infrastructure the way the best platform engineers think about data infrastructure: as a set of foundations with internal customers whose needs must be understood and balanced * Are comfortable operating in ambiguity, making decisions with incomplete information, and creating structure where none exists, while remaining open to changing course when better information arrives * Influence through clarity, evidence, and the quality of your work rather than positional authority. You earn adoption by making the platform genuinely better than the alternative * Care about production engineering quality: you write production-grade code, your systems are tested, observable, documented, and designed for others to operate Our ideal candidate has: * Deep hands-on experience building MLOps platforms, including model registries, feature stores, and ML pipeline orchestration * Working knowledge of model serving patterns: real-time inference, batch prediction, A/B deployment, and deployment strategies * AWS infrastructure experience (ECS/EKS, S3, IAM, networking) and comfort operating in a Databricks ecosystem or equivalent lakehouse architecture * Experience with model monitoring: model evaluation, data drift detection, prediction drift, and performance degradation alerting * A track record of building something from zero and bringing it to a state where others could operate and extend it * Experience in a regulated industry (fintech, financial services, healthcare) where model governance is a compliance requirement * See AI as a core part of how modern engineering gets done, not a passing trend. You actively use it to think faster, prototype faster, and pressure-test your own designs, and you're excited that the bar keeps rising. * Prior experience running formal build-vs-buy evaluations with written decision records Bonus Points for: * Familiarity with model risk management frameworks and the ability to connect governance practices to regulatory expectations * Experience working simultaneously with research-oriented ML teams and production-oriented engineering teams, and understanding how their needs diverge * Infrastructure-as-code fluency (Terraform) * Experience with ClickHouse or similar OLAP engines for low-latency ML feature serving * Blockchain or crypto domain knowledge * Experience working in fraud detection and modelling * Contributions to open-source MLOps tooling JOB BENEFITS > How we work: * Hybrid working and the option to work from almost anywhere for up to 90 days per year * £500 Remote working budget to set up your home office space > Learning & Development: * $1,000 Learning & Development budget to use on anything (agreed with your manager) that contributes to your growth and development > Vacation/ Leave: * Holidays: 25 days of annual leave + bank holidays * An extra day for your birthday * Enhanced parental leave: we provide eligible employees, regardless of gender or whether they become a parent by birth or adoption, 16 weeks fully-paid leave and leave. > Benefits: * Private Health Insurance - we use Vitality! * Full access to Spill Mental Health Support * Life Assurance: we hope you will never need this - but our cover is for 4 times your salary to your beneficiaries * Cycle to Work Scheme
The impact you will have: As Staff AI Engineer, you will be one of the most impactful early hires in Elliptic's next stage of AI expansion. You will join at a moment when Elliptic is actively forming its approach to AI foundations: tooling decisions are being made, agentic patterns are being established, and the kernel of a centralised AI platform is being laid out. Your role is to govern the quality and coherence of those decisions before they crystallise. You will initially work across our AgentForce and Investigations & AI teams, holding the architectural bar on tooling evaluations, keeping the stack decision open and well-reasoned, and ensuring that the internal agentic patterns being developed today are genuinely inheritable by the customer-facing AI products of tomorrow. You will act as a strong advocate for AI adoption, AI technical best practices, and AI enablement across product, engineering, and development. This is a role for someone who is comfortable with ambiguity, energised by the challenge of making decisions that others will build on for years, and confident enough to hold a strong technical position without needing a team beneath them to do it. What you will do: * Serve as the architectural conscience for Elliptic's early AI decisions, evaluating our current tooling explorations (including the LangSmith ecosystem and Databricks) against the requirements of production-scale, customer-facing AI products, and producing a clear, evidence-based recommendation * Work consultatively with the Investigations & AI technical lead and AgentForce engineering to ensure that internal agentic patterns, prompt architectures, and evaluation frameworks are being designed with customer-facing scale and regulatory auditability in mind * Hold the AI stack decision open responsibly: document trade-offs, establish evaluation criteria, and prevent pragmatic local choices from defaulting the answer before the right person is in place to make it * Define and uphold engineering standards for AI systems across the organisation: model observability and tracing, prompt versioning and registry, cost governance, evaluation harnesses, and agent reliability patterns * Produce the technical foundation documents that will be a coherent architectural position, a clear view of decisions made and decisions deferred, and an honest assessment of what the architecture can accomplish You will be a great fit here if you: * Are energised by the challenge of bringing rigour to early-stage technical decisions, and understand that preventing a bad architectural choice is often more valuable than shipping a feature * Can hold a strong, well-reasoned technical position without needing formal authority to make it stick. You influence through clarity, evidence, and the quality of your thinking * Think about AI infrastructure the way the best platform engineers think about data infrastructure: as a set of foundations with internal customers whose needs must be understood and balanced * Are comfortable operating in ambiguity and working across teams without a fixed mandate, and know how to make yourself useful in a way that doesn't create dependency or territorial friction * Care about the trustworthiness of AI systems, not just their capability. Understand why explainability, auditability, and reliability matter especially in a regulated compliance context Our ideal candidate has: * Made production AI architectural decisions, including evaluation framework selection, LLM integration patterns, prompt management and versioning at scale, and model observability. You can speak to what went well, what they would do differently, and why * Worked across the boundary between internal tooling and customer-facing AI products, and understands how requirements differ across those contexts, particularly in relation to reliability, auditability, and cost * Built or significantly shaped an AI evaluation or observability framework in a production environment, and has strong opinions on what good looks like * Operated effectively without a team beneath them. As a Staff IC whose impact comes from technical leadership and cross-team influence rather than people management and team workstream prioritisation Bonus Points for: * Experience building agentic systems in a production context, including orchestration patterns, tool use, memory management, and agent reliability at scale * Familiarity with one of the major AI ecosystems, such as LangSmith, MLflow, or Databricks ML * Having navigated a transition from a scrappy, point-to-point AI integration to a well-engineered, reusable AI platform. An understanding of the organisational as well as technical challenges that transition involves * An interest in the crypto ecosystem and the mission of making digital assets safer and more accessible JOB BENEFITS > How we work: * Hybrid working and the option to work from almost anywhere for up to 90 days per year * £500 Remote working budget to set up your home office space > Learning & Development: * $1,000 Learning & Development budget to use on anything (agreed with your manager) that contributes to your growth and development > Vacation/ Leave: * Holidays: 25 days of annual leave + bank holidays * An extra day for your birthday * Enhanced parental leave: we provide eligible employees, regardless of gender or whether they become a parent by birth or adoption, 16 weeks fully-paid leave and leave. > Benefits: * Private Health Insurance - we use Vitality! * Full access to Spill Mental Health Support * Life Assurance: we hope you will never need this - but our cover is for 4 times your salary to your beneficiaries * Cycle to Work Scheme
OUR MISSION At Omnea, we’re reinventing how enterprise businesses operate, starting with the most painful parts: procurement – where a single purchase can drag on for months, trigger 50+ emails, and pull in Finance, Legal, Security, and IT just to get something approved. We’ve raised $75M from Khosla Ventures, Insight Partners, and Accel to change that. Our AI-native platform connects every person, step, and system so buying is fast, safe, and efficient – one place to request, automated approvals and renewals, real-time supplier risk, and complete spend visibility. The opportunity is massive. Every enterprise on the planet has this problem and nobody has solved it. We’ve 10x’d ARR to double-digit millions in 18 months and are trusted by global enterprises like Spotify, MongoDB, Monzo, and Albertsons. We’re now the 4th fastest growing startup in Europe & the Sunday Times' #1 Best Medium Sized Tech Company To Work for. Our team previously scaled Tessian (cybersecurity tech, backed by Sequoia, Balderton, Accel, acquired post-Series C), and our team includes ex-founders operators who’ve grown unicorns, shipped world-class products, and executed at the highest levels. You’ll work alongside leaders like Ben, Abs, Sabrina, and Rebe. FIND OUT MORE ABOUT THE TEAM AND LIFE AT OMNEA HERE. WHY WE NEED A SOFTWARE ENGINEER (PLATFORM) NOW Omnea deploys multiple times a day, serves enterprise customers who expect rock-solid reliability, and is scaling 10x. Our platform team owns the infrastructure, tooling, and developer experience that underpins all of this. We're adding a hands-on Platform Engineer to the team to improve our CI/CD, scale our serverless infrastructure, strengthen observability, and make every product engineer more effective. WHAT WE'RE LOOKING FOR We're hiring at both Level 3 (Senior) and Level 4 (Staff). For calibration, candidates typically bring 5+ years of platform engineering experience in high-growth, cloud-native SaaS environments, but we care more about impact than years. You'll join an experienced platform team and contribute across the full stack, from infrastructure-as-code and deployment pipelines to developer tooling and occasionally product features. WHAT YOU'LL DO * Scale our infrastructure. Design and evolve our AWS serverless architecture (including Lambda, DynamoDB, Aurora, EventBridge) for 10x growth, multi-region HA, and data residency requirements. * Own CI/CD and deployment. Build and improve our deployment pipelines (GitHub Actions), infrastructure-as-code (Pulumi), and release tooling so product teams can ship faster and safer. * Own observability. Build out monitoring, alerting, and debugging across the stack using Datadog. When something breaks, we should know before our customers do. * Improve developer experience. Build internal tooling, automate toil, and remove friction so engineers spend more time shipping product and less time fighting infrastructure. * Code and build. Contribute production-ready TypeScript across infrastructure and product features; raise the bar through solid patterns, libraries, and reviews. WHAT CAN YOU EXPECT? * A strong team. You'll join a platform team of senior and staff engineers who set a high bar and will push you to do your best work. * Modern, cloud-native stack. Everything serverless and IaC-driven (AWS, Pulumi, TypeScript end-to-end). * Continuous delivery. We deploy multiple times a day. Your work determines how fast and safely the whole engineering org ships. * Scalability challenges. As we 10x revenue, you'll evolve infrastructure for multi-region HA, data residency, and performance at scale. * Customer impact. The reliability and speed you build is what lets us win and keep enterprise accounts. * Massive ownership. You own problems end-to-end, from brainstorming through to production. * Collaboration & autonomy. Plenty of heads-down technical work, but also close partnership with product engineers and engineering leadership. ABOUT YOU * Platform engineer & builder. You design reliable infrastructure and write clean production code (TypeScript or similar). You've built and maintained CI/CD pipelines, IaC, and developer tooling at scale. * Cloud-native. Deep knowledge of AWS (Lambda, DynamoDB, EventBridge, IAM, networking) or equivalent cloud-native experience. You understand serverless and its trade-offs. * Developer-first. You think about engineers as your customers. You automate toil, improve feedback loops, and care deeply about making other people productive. * Commercially minded. You understand that infrastructure exists to serve the product and the business. You make trade-offs with that in mind. * Bias for action. You iterate quickly, ship pragmatically, and automate everything. * Culture carrier. You coach teammates, share knowledge freely, and keep calm when things break. Nice-to-haves * Experience with Pulumi or similar IaC frameworks. * Hands-on Datadog experience. * Experience building internal developer platforms or tools adopted org-wide. * Open-source contributions to infrastructure or developer tooling projects. You can learn more about Engineering at Omnea and our hiring process via our R&D Candidate Hub here. At Omnea, we embrace diversity. To build a product that's loved by everyone, we're best served by a team with all sorts of backgrounds, experiences, and perspectives. We encourage you to apply even if your experience doesn't quite match the full job spec! And regardless of your race, religion, colour, gender, or anything else! If you think you could be a good fit for Omnea, please reach out. A FEW THINGS TO NOTE: * We offer competitive geo-localised benefits, and you can check out our UK Benefits Package here and our US Benefits Package here. * We work Tuesdays, Wednesdays & Thursdays in-person at our offices. At this early stage of our company life-cycle it's important to us that we get this together-time, and you can read more about why we believe this is a winning move here * We're commercial, ambitious and we don't pretend otherwise! We're actively seeking folks looking to make the most of a career-defining opportunity, with the hunger to be part of building something really impressive. You can see our values here and our Omnea Future Founder's fund here! * We sometimes use AI note-takers to help us transcribe interview notes, so we can be more present in your interview. If you'd like to opt out of us using automatic transcribers, please note this in the free text field in your application, otherwise we'll take your application as confirmation that you're happy for us to use notetakers (whether added to video calls or in the background). We are proud to be recognised for both our culture and product, and we are just getting started. Join us as we grow! LEGAL NOTE: IF YOU ARE VIEWING THIS POSTING OUTSIDE OF THE OMNEA CAREERS' PAGE, THIS MAY BE AN AUTO-GENERATED ADVERTISEMENT AND MAY LACK THE FULL RANGE OF ADVERTISED INFORMATION - PLEASE CLICK THROUGH TO THE POSTING AT HTTPS://JOBS.ASHBYHQ.COM/OMNEA TO VIEW ADDITIONAL ADVERTISED INFORMATION ON THIS POSTING. ADDITIONALLY, WHERE ROLES HAVE HARD-SPECIFIED REQUIREMENTS (E.G. [X] DAYS IN OFFICE, UNABLE TO PROVIDE VISAS, ETC), IF IN YOUR APPLICATION YOU PROVIDE DETERMINISTIC CHECK-BOX CONFIRMATION THAT YOU DO NOT MEET THE HARD-SPECIFIED REQUIREMENTS, DETERMINISTIC (NOT AI OR SUBJECTIVE) AUTOMATIC REJECTION CRITERIA ARE IN PLACE.