At a Glance
- Tasks: Build and enhance AI features for financial crime intelligence using cutting-edge technologies.
- Company: Join Arva AI, a startup revolutionising compliance with innovative AI solutions.
- Benefits: Competitive salary, equity package, remote work options, and bi-annual reviews.
- Why this job: Make a real impact in the fight against financial crime with advanced AI technology.
- Qualifications: 3+ years in AI research, experience with prompt engineering and model fine-tuning.
- Other info: Collaborative culture focused on transparency and customer-first values.
The predicted salary is between 36000 - 60000 ÂŁ per year.
Location: In person, Central London, 4-5 days in office
Type: Full-Time
NB: We are able to sponsor visas
Arva AI is revolutionising financial crime intelligence with our cutting-edge AI Agents. By automating manual human review tasks, we enhance operational efficiency and help financial institutions handle AML reviews, while cutting operational costs by 80%.
As an AI Research Engineer, you’ll play a pivotal role in building our in-house models—training, fine-tuning, and rigorously evaluating LLMs and agentic systems that power our AI compliance platform, from document fraud detection to web-scale due diligence. You’ll also develop the automation and infrastructure that continuously tests and improves performance (evals, data/label pipelines, training loops, and iterative fine-tuning/distillation).
About the Role
As an AI Researcher, you will:
Build and iterate on in-house LLM and agentic capabilities, integrating them into product features with rigorous evaluation, benchmarking, and backtesting across real compliance workflows.
Design continuous-improvement loops for our models, leveraging human-in-the-loop feedback to drive dataset curation, fine-tuning/distillation, and optimisation of model behaviour.
Stay on the frontier and translate research into impact, exploring and applying new AI methods to improve model quality, explainability, latency, and reliability in production.
What You’ll Do
- Evals: Build benchmarks, evaluate backtests and analyse results to quantify our performance and prevent regressions.
- Model Fine-Tuning: Adapt and fine-tune large language models and vision models for specific use cases.
- Custom Model Training: Train and deploy bespoke AI models tailored to solve unique compliance challenges.
- AI features: Use the latest in LLMs, agents and computer vision to build systems that are faster, more thorough and more accurate than humans.
- MLOps: Ensure that we maintain good practices around collecting and managing datasets, backtests and benchmarks to maximise development velocity.
- Prompt Engineering: Develop and test prompts to optimise model performance and deliver high-quality outputs.
- Collaboration: Work closely with the engineering and product teams to translate customer needs into actionable AI solutions.
- Iterate: Continuously iterate on our AI systems to improve performance, reduce errors, and deliver value to customers.
Our Culture
Deliver Value Fast
Speed starts with clarity. We first understand what value actually means, for the customer, the business, or the system, and then take the shortest credible path to delivering it. We move quickly without cutting corners on quality, security, or trust.
Outcome Obsessed
We obsess over details and take full ownership of wider outcomes, not just tasks. We think like the user, execute with rigor, and validate that every detail works in real conditions. If something falls short, we fix it properly and prevent recurrence, always raising the bar.
Relentless Urgency
We move with urgency because time matters. We prioritise what truly moves the outcome, make decisions with imperfect information, and act decisively. Urgency means focus, momentum, and driving meaningful progress without unnecessary delay.
What We’re Looking For
- Experience: 3+ years in an AI research or engineering role, with experience building and testing agentic systems.
- Technical Expertise: Hands‑on experience with prompt engineering, fine‑tuning pre‑trained models, and training custom models, including vision models. Experience using TypeScript a plus.
- Product Mindset: Strong understanding of how AI can solve real‑world problems, with a focus on customer needs.
- Interests: Ability to stay updated with the latest advancements in AI and apply them effectively.
- Collaboration: Excellent communication skills to work across teams and explain complex AI concepts to non‑technical stakeholders.
- Ownership: A proactive, problem‑solving mindset with the ability to take full responsibility for projects and outcomes.
- Growth‑Oriented: Excited to learn new skills, tackle challenges, and adapt as the company scales.
Why Join Us?
Be part of an early‑stage startup with significant ownership and influence over the AI strategy.
Work on cutting‑edge AI technologies that transform how businesses operate.
Collaborate with a passionate, mission‑driven team dedicated to innovation.
Work from anywhere in the world for 4 weeks a year, in addition to regular team off‑sites.
Competitive salary and equity package, with bi‑annual salary review and yearly performance‑based equity refresh.
Ready to Join the Fight Against Financial Crime?
If you’re excited to shape the future of compliance with state‑of‑the‑art AI solutions, we’d love to hear from you. Apply now to become an Arvanaut as an AI Researcher.
Arva AI is trusted by fast‑growing fintechs like Keep to reduce friction, improve compliance outcomes, and unlock scale
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AI Research Engineer employer: Arva AI Inc.
Contact Detail:
Arva AI Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Research Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with Arva AI employees on LinkedIn. Building relationships can open doors that applications alone can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI projects, especially those related to LLMs and agentic systems. This will give you an edge and demonstrate your hands-on experience.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss how you've tackled challenges in AI research and engineering roles.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you're genuinely interested in joining our mission at Arva AI.
We think you need these skills to ace AI Research Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in AI research and engineering. We want to see how your skills align with the role, so don’t be shy about showcasing your achievements in building and testing agentic systems!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about AI and how you can contribute to our mission at Arva AI. Keep it concise but impactful – we love a good story!
Showcase Your Technical Skills: Don’t forget to mention your hands-on experience with prompt engineering and model fine-tuning. We’re looking for someone who can hit the ground running, so highlight any projects or tools you’ve used that relate to the job.
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 the role. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Arva AI Inc.
✨Know Your AI Stuff
Make sure you brush up on the latest advancements in AI, especially around LLMs and agentic systems. Be ready to discuss your hands-on experience with prompt engineering and model fine-tuning, as these are key aspects of the role.
✨Show Your Problem-Solving Skills
Prepare examples that showcase your proactive approach to tackling challenges. Think about specific projects where you took ownership and how you iterated on solutions to improve outcomes. This aligns perfectly with their culture of urgency and ownership.
✨Communicate Clearly
Since you'll be working with both technical and non-technical teams, practice explaining complex AI concepts in simple terms. This will demonstrate your collaboration skills and ensure everyone is on the same page during discussions.
✨Emphasise Customer Focus
Be ready to talk about how you've used AI to solve real-world problems, particularly in a customer-centric way. Highlight any experiences where you prioritised customer needs and how that influenced your work, as this is a core value for the company.