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- Tasks: Build and scale AI/ML models for blockchain analytics and collaborate on innovative projects.
- Company: High-growth SaaS platform known as the Bloomberg for crypto.
- Benefits: Competitive salary, equity options, performance bonuses, and fully remote work.
- Why this job: Join a senior team shaping the future of AI in blockchain with impactful projects.
- Qualifications: 3-6+ years in data science/ML, strong Python skills, and interest in crypto/blockchain.
- Other info: Dynamic startup environment with opportunities for mentorship and career growth.
The predicted salary is between 78000 - 90000 £ per year.
Do you have 4+ years of hands-on ML experience and enjoy deploying models into production?
Have you worked with GenAI / LLMs and modern ML tooling in real systems?
Are you interested in crypto, blockchain, and applying ML to complex on-chain data?
A Series B, high-growth SaaS platform delivering institutional-grade blockchain intelligence is hiring a Senior AI/ML Engineer. Often described as the Bloomberg for crypto, the platform helps investors discover opportunities, perform due diligence, and defend portfolios using AI-driven insights.
This is a senior, hands-on role for someone strong across data science, applied ML, and cloud deployment, who wants to work in a lean team with real ownership.
Why join?
You’ll join a small, senior AI/ML team working on core product features such as:
- Large-scale wallet labelling and behavioural analysis
- NFT price estimation using ML
- Trading and risk signals built on blockchain data
The environment is fast-moving, product-led, and AI-first, with genuine room to shape how ML is applied across the platform.
Requirements
- 4-7+ years of professional experience in Data Science / ML roles
- Strong data science foundations (EDA, feature engineering, model evaluation, validation)
- Hands-on experience with ML models in production
- Cloud & MLOps exposure (GCP preferred; AWS/Azure acceptable)
- Experience with GenAI / LLMs (commercial or serious hands-on experimentation)
- Strong Python and modern ML tooling (e.g. scikit-learn, XGBoost, TensorFlow or PyTorch)
- Clear interest in crypto / blockchain (commercial experience a plus, curiosity required)
Responsibilities
- Build, train, and improve ML models on large-scale blockchain datasets
- Take models from exploration to production, working closely with engineering
- Apply GenAI / LLM approaches where they add real product value
- Maintain high DS/ML standards across experimentation, monitoring, and iteration
- Contribute to best practices in a lean, senior AI team
Key details
- Salary: up to ~£135k base + equity (4-year vest, 1-year cliff) + bonus
- Location: Fully remote (UK)
- Stack: Python, GCP, ML frameworks, LLM APIs
- Visa: Cannot sponsor
- Team: Senior, small, highly technical
If you’re a senior ML engineer with strong DS fundamentals, real deployment experience, and a genuine interest in crypto, this is a high-impact role with a lot of ownership.
Interested? Please apply below.
Senior MLE - Blockchain AI employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior MLE - Blockchain AI
✨Tip Number 1
Network like a pro! Reach out to folks in the blockchain and AI space on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get you in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML models and projects related to blockchain analytics. We want to see your hands-on experience, so make sure it’s easy for potential employers to find your work.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical skills and be ready to discuss your past projects in detail. We recommend practicing common ML and blockchain-related questions to boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Senior MLE - Blockchain AI
Some tips for your application 🫡
Show Your Passion for Blockchain: Make sure to highlight your interest in blockchain and crypto in your application. We want to see that you’re not just skilled, but also genuinely excited about the field and its potential.
Tailor Your Experience: When detailing your experience, focus on projects that align with our needs. We love seeing how you've built and deployed ML models, so be specific about your contributions and the impact they had.
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon unless it’s necessary. Make it easy for us to see why you’d be a great fit for our team.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at Harnham
✨Know Your Stuff
Make sure you brush up on your knowledge of blockchain and AI/ML models. Be ready to discuss specific projects you've worked on, especially those that involved complex datasets. This will show your potential employer that you’re not just familiar with the tech but have hands-on experience.
✨Show Your Curiosity
This company values curiosity, so don’t hesitate to ask insightful questions about their current projects or future plans. It’s a great way to demonstrate your interest in the role and the industry, plus it shows that you’re thinking critically about how you can contribute.
✨Highlight Collaboration Skills
Since the role involves working closely with engineering and product teams, be prepared to share examples of how you’ve successfully collaborated in the past. Discussing your approach to teamwork and communication can set you apart from other candidates.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions during the interview. Brush up on your Python skills and be ready to explain your end-to-end ML lifecycle experience. Practising coding challenges or discussing your thought process on model deployment can really help you shine.
