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- Tasks: Build and improve ML models on large-scale blockchain datasets.
- Company: Join a high-growth SaaS platform revolutionising blockchain intelligence.
- Benefits: Competitive salary, equity options, and fully remote work.
- Why this job: Make a real impact in the crypto space with cutting-edge AI technology.
- Qualifications: 4+ years in Data Science/ML, strong Python skills, and cloud experience.
- Other info: Be part of a small, senior team with genuine ownership and growth opportunities.
The predicted salary is between 81000 - 162000 £ 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 in Portsmouth employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior MLE - Blockchain AI in Portsmouth
✨Tip Number 1
Network like a pro! Reach out to folks in the blockchain and AI space on LinkedIn or at meetups. A personal connection can often get your foot in the door faster than any application.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those related to blockchain or GenAI. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your ML fundamentals and cloud deployment skills. Practice coding challenges and be ready to discuss your past projects in detail—this is where we can really shine!
✨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, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace Senior MLE - Blockchain AI in Portsmouth
Some tips for your application 🫡
Show Off Your Experience: Make sure to highlight your 4+ years of hands-on ML experience in your application. We want to see how you've deployed models into production and any work you've done with GenAI or LLMs. This is your chance to shine!
Tailor Your Application: Don’t just send a generic application! Tailor your CV and cover letter to reflect your interest in crypto and blockchain. We love seeing candidates who are genuinely excited about the field and can connect their skills to our mission.
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate straightforward communication, so make sure your key achievements and experiences stand out without unnecessary fluff.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. Don’t miss out!
How to prepare for a job interview at Harnham
✨Know Your ML Stuff
Make sure you brush up on your machine learning fundamentals, especially around EDA, feature engineering, and model evaluation. Be ready to discuss specific projects where you've deployed models into production, as this will show your hands-on experience.
✨Show Your Passion for Blockchain
Since the role is heavily focused on crypto and blockchain, demonstrate your genuine interest in these areas. Share any personal projects or research you've done related to blockchain or how you've applied ML in this space.
✨Familiarise with GenAI and LLMs
Be prepared to talk about your experience with Generative AI and Large Language Models. If you've worked with them, highlight specific use cases and how they can add value to the product you're applying for.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's approach to AI and ML, especially regarding their product features. This shows that you're not just interested in the role but also in how you can contribute to their mission.
