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About This Role
Our Mission
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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.
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The Role
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AI is changing what it means to deploy enterprise software. Configuring a customer's procurement workflows used to mean weeks of manual setup, integration by integration, edge case by edge case. Increasingly, that first\-draft build work can be directed rather than done by hand: AI agents can generate workflow configurations, draft integration mappings, and produce the initial pass on a customer's setup.
The job shifts with it. Instead of building every workflow yourself, you specify exactly what needs to exist, direct AI to build it, and apply the judgment AI can't: is this the right configuration for this customer's approval policy, will Legal and Finance actually sign off on it, does it hold up once real spend starts flowing through it. In practice, that might mean catching that the approval chain AI just drafted works perfectly for HQ, but misses a regional delegation\-of\-authority rule that would fail an audit in the customer's APAC entity.
As Omnea's AI Solutions Lead, you own deployment of the Omnea platform end to end \- typically running 3\-5 enterprise deployments concurrently \- for some of Europe's most recognisable companies. You advise customers on the best\-fit configuration, direct AI\-assisted build work to get there fast, and validate every output against what the customer actually needs. You'll also deal with the politics that come with every enterprise rollout: the Legal team wanting one more sign\-off, the Finance stakeholder who doesn't trust the new approval flow, the exec sponsor who's gone quiet. You become the internal voice of the customer, and you're judged on outcomes you can point to: time to launch, post\-go\-live CSAT, and whether that customer becomes a case study or reference.
This is an early hire on Omnea's customer team, working directly with our sales, customer, product, and engineering leadership. The future shape of the role is yours to define!
What You'll Do
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### Discover and design
- Run discovery with customer stakeholders across Procurement, Finance, Legal, and IT, and translate their process into an Omnea configuration
- Design the target workflow: approval chains, routing logic, integrations, and the edge cases that make procurement hard
- On a deployment with a dozen\-plus integrations, you own the sequencing \- what has to land first, what blocks what \- and the harder calls underneath it: which system is the source of truth for a given entity, and how records reconcile when two systems key that entity differently
- Define success criteria and a realistic path to go\-live with the customer's project sponsor
### Build with AI, not instead of it
- You're not just directing AI to draft your own deliverables faster \- you're configuring the specialized agents Omnea's customers rely on every day: an intake agent that knows how to ask a procurement analyst the right follow\-up question, a risk agent that flags what actually matters to this customer's Legal team
- The real leverage comes from chaining agents together \- an intake agent that hands clean, structured requests to a risk agent, which in turn routes into approvals with the right context already attached. You design how those agents work as a system, not just individually
- Writing a spec for one agent is a skill. Writing one that holds up across a chain of agents \- where one agent's output becomes another's input \- is the actual bar here
- Validate every AI\-generated output against the customer's actual process, policy, and edge cases before anything ships
- Handle what AI can't: judgment calls on ambiguous requirements, custom logic, and anything that needs a human read on internal politics
### Deploy and prove value
- Present configured workflows to stakeholders, from procurement analysts to CFOs, working through the pushback and internal politics that come with any enterprise rollout, and win their buy\-in
- Run multi\-day onsite workshops with customers, coordinating dozens of stakeholders across multiple workstreams at once \- a different muscle than a single stakeholder meeting
- Project\-manage the deployment: timelines, blockers, stakeholder alignment, and a clear path to launch, acting as an extension of the customer's team
- Track adoption after go\-live and keep the configuration working as the customer's process evolves
- Work with Product and Engineering during the design phase, not just after something breaks in the field \- you're often the earliest signal on whether a new module will hold up for an enterprise customer, and that shapes what gets built next
What's Different About This Role
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- You'll spend more time specifying and validating than building from scratch. AI produces the first draft; you make sure it's right for this customer
- Writing instructions AI can act on precisely is a skill you'll build fast here. Vague requests produce vague configurations
- Judgment matters more, not less. AI speeds up the build, but you decide whether the output is right for this contract, this Board, this customer
- You'll keep experimenting as our AI tooling evolves, and share what works with the rest of the team
About You
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We don't expect you to know how to do all of this on day one. TL;DR: you're ambitious and hard\-working, as comfortable directing an AI agent through a tricky configuration as you are presenting a project plan to an exec. You make up for any gap in experience with hunger and a genuine growth mindset. You derive energy from building relationships, shipping projects, and making customers succeed.
- 4\-8 years in a fast\-paced, high\-bar environment: a startup, scale\-up, consulting, banking, or something you built yourself
+ Less experience? Check out our Solutions Associate role
- Comfortable directing AI tools to produce first\-draft work, and confident enough in your own judgment to catch what's wrong. Prior use of tools like ChatGPT, Claude, or Cursor is a strong plus. What matters most is your ability to write clear, specific instructions and evaluate what comes back
- Client\-facing experience, or the natural gravitas to be excellent at it. You can point to a time you went well beyond what a client expected
- A track record of doing something exceptionally well, whether that's work, academia, sport, or anything else you've put your mind to
- Comfortable reasoning about system architecture \- how data models, primary keys, and integration sequencing interact across a dozen enterprise systems. You don't need to write the integration code, but you need to know why it breaks and be able to reason with the people who do, and you'll need to become an Omnea product expert
- You build real relationships with people at any level, from a junior procurement analyst to a CFO, and know how to handle pushback and stakeholder politics, not just build rapport
- Comfortable running rooms of stakeholders across a genuinely cross\-functional tool \- on a single project you'll interface with the CPO, Legal, Finance, AP, InfoSec, and Compliance, plus each of their technical resources, often all with competing priorities on the same decision
- Comfortable moving between the detail and the big picture: configuring a workflow in the morning, presenting a project plan to an exec in the afternoon
- Entrepreneurial, have high ownership, and want to be part of building something. We've signed up to the Future Founder Promise
- An excellent communicator: clear and concise, verbal and written, even when the subject is complicated
- Highly organised: comfortable running 3\-5 enterprise deployments concurrently, each at a different stage, without dropping any of them
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:
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- 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!
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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.
Compensation Range: $120K \- $190K
Salary Context
This $120K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Omnea, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($155K) sits 28% below the category median. Disclosed range: $120K to $190K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Omnea AI Hiring
Omnea has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in NY, US. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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