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About This Role
Hadean is a deep\-tech company building cutting\-edge distributed computing technology that powers scalable, secure, and interoperable digital environments. Our platform enables real\-time simulation and training, mission rehearsal, command \& control and digital twin capabilities \- transforming the way defence, government agencies and enterprises plan, train, and make decisions.
We work at the forefront of defence innovation, collaborating with global partners to deliver next\-generation capabilities that unlock operational advantage.
The Team:
dominAI is a close\-knit team of highly technical, deeply collaborative engineers — each bringing genuine specialism to a shared mission. We move fast, hold each other to a high standard, and take real pride in what we ship. We work in sprints, building MVPs that are regularly taken out to industry events, integration hackathons, and live demonstrators. There's no waiting months to see your work matter \- the feedback loop between building and showing is tight, and it shapes what we build next. We're also a team that invests in itself: continuous learning, honest retrospectives, and a genuine culture of craft are part of how we operate.
This is a brand new product for Hadean. We're building it quickly and building it right \- and the people who join now will shape its direction.
The Role:
This is an opportunity to be part of something that doesn't exist yet — at least not in the way we're building it.
We're combining rich data from best\-in\-class defence software and hardware, presenting it through a modern command and control interface, and bringing the latest AI techniques to bear on some of the most critical analysis and planning challenges in the defence sector. The goal is to give decision\-makers information advantage at the moment it matters most.
As a Senior AI Engineer in dominAI, you will be responsible for integrating Large Language Models and broader AI capabilities into production\-grade software that real operators will depend on. You'll work as a core member of a cross\-functional team alongside Product Managers, other engineers, and domain experts \- translating ambitious product requirements into reliable, scalable, and impactful technical solutions.
This isn't a role about exploring AI in theory. It's about shipping AI that works, in conditions that matter, to users with no tolerance for failure. If that challenge excites you, you'll fit right in.
Key Responsibilities:
- Design, build, and deploy production\-grade software that integrates AI to solve complex, real\-world user problems.
- Research and evaluate emerging AI architecture patterns \- RAG, agentic workflows, evals, fine\-tuning, LLM orchestration \- and apply findings directly to the product.
- Collaborate closely with Product Managers and cross\-functional teammates to translate requirements into technical solutions that ship.
- Mentor and upskill engineers across the team on AI technologies and best practices, raising the collective capability of the group.
- Champion and embed agentic engineering tools and practices into the team's daily workflow to accelerate delivery.
- Write clean, maintainable, and efficient code in TypeScript and/or Python, holding yourself and others to a high standard.
- Ensure the reliability and scalability of AI\-powered features in a production environment \- this software operates in demanding contexts.
- Stay current with the rapidly evolving AI landscape; bring ideas back to the team and help shape our technical direction.
Skills, Knowledge and Experience:
- A degree in Computer Science, Software Engineering, or a related field
- 3\+ years professionally developing products involving AI or LLMs
- Proven experience deploying AI in a production software environment \- you've shipped things and learned from them.
- Deep understanding of AI architectures and patterns: RAG, prompt engineering, fine\-tuning, evals, and LLM orchestration.
- Strong background as a generalist software engineer with proficiency in TypeScript and/or Python.
- Ability to research complex technical topics and apply findings to deliver practical, production\-ready solutions.
- Experience mentoring or teaching others in a technical capacity.
- Strong problem\-solving instincts and a genuine appetite for hard challenges.
- Must be able to obtain and maintain Security Vetting to at least SC level.
- Willingness to attend our Shoreditch office at least once a week.
What will help you stand out
- Experience with systems programming languages like C\+\+ or Rust.
- Familiarity with the defence sector, Military Modelling and Simulation, or C2 systems.
- An interest in travelling to support customer deployments, hackathons, and industry events \- getting your work in front of real users, fast.
Job Benefits
- We make Hadean an awesome place to work with competitive benefits
- Hybrid working with 1 day per week in our fantastic office in Shoreditch, London
- Private Health Insurance
- Enhanced pension scheme
- Enhanced parental leave
- 3 extra days off at Christmas (on top of our standard 25\)
- L\&D budget
- Regularly scheduled socials
- Share options
A Place For Everyone
We believe diversity drives innovation and for that reason we strongly encourage those from all backgrounds to apply for roles at Hadean. We are an equal opportunity employer and aim to build a workforce that is truly representative of the communities in which we operate and our clients.
Locations
London
Remote status
Hybrid
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Hadean, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Hadean AI Hiring
Hadean has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in London, OH, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
Frequently Asked Questions
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