Remote AI Research Engineer

Remote · Posted April 9, 2026

Salary: Competitive salary and benefits package

AI Research Engineer Reinforcement Learning Fintech Remote NLP Machine Learning PyTorch

As a member of the AI model team, you will drive innovation in reinforcement learning approaches for advanced models.
Your work will optimize decision-making and adaptive behavior to deliver enhanced intelligence, improved performance, and domain-specific capabilities for real-world challenges.
You will work across a broad spectrum of systems, including resource-efficient models designed for limited hardware environments and complex multi-modal architectures that integrate data such as text, images, and audio.

Our team is a global talent powerhouse, working remotely from every corner of the world.
If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards.
We’ve grown fast, stayed lean, and secured our place as a leader in the industry.

As a member of the AI model team, you will drive innovation in reinforcement learning approaches for advanced models.
Your work will optimize decision-making and adaptive behavior to deliver enhanced intelligence, improved performance, and domain-specific capabilities for real-world challenges.
You will work across a broad spectrum of systems, including resource-efficient models designed for limited hardware environments and complex multi-modal architectures that integrate data such as text, images, and audio.

Develop and implement state-of-the-art reinforcement learning algorithms designed to optimize decision-making processes in both simulated and real-world settings. Establish clear performance targets such as reward maximization and policy stability.
Build, run, and monitor controlled reinforcement learning experiments. Track key performance indicators while documenting iterative results and comparing outcomes against established benchmarks.
Identify and curate high-quality simulation environments and training datasets that are tailored to specific domain challenges. Set measurable criteria to ensure that the selection and preparation of these resources significantly enhance the learning process and overall model performance.
Systematically debug and optimize the reinforcement learning pipeline by analyzing both computational efficiency and learning performance metrics. Address issues such as reward signal noise, exploration strategy, and policy divergence to improve convergence and stability.
Collaborate with cross-functional teams to integrate reinforcement learning agents into production systems. Define clear success metrics such as real-world performance improvements and robustness under varied conditions and ensure continuous monitoring and iterative refinements for sustained domain adaptation.

Excellent English communication skills
Ready to contribute to the most innovative platform on the planet
A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good publications in A* conferences).
Proven experience with large-scale reinforcement learning experiments, including online RL techniques such as Group Relative Policy Optimization (GRPO), is essential. Your contributions should have led to measurable improvements in domain-specific decision-making and overall policy performance.
Deep understanding of reinforcement learning algorithms is required, including state-of-the-art online RL methods and other gradient-based optimization approaches like policy gradients, actor-critic, and GRPO. Your expertise should emphasize enhancing policy stability, exploration, and sample efficiency in complex, dynamic environments.

Strong expertise in PyTorch and relevant reinforcement learning frameworks is a must. Practical experience in developing RL pipelines, from simulation and online training to post-training evaluation and deploying RL-based solutions in production environments is expected.
Demonstrated ability to apply empirical research to overcome reinforcement learning challenges such as sample inefficiency, exploration-exploitation tradeoffs, and training instability. You should be proficient in designing robust evaluation frameworks and iterating on algorithmic innovations to continuously push the boundaries of RL agent performance.

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Job type: Full-Time
Category: Back-End Programming
Region: Anywhere in the World
Apply before: May 2th, 2026
Salary: Competitive salary and benefits package
Location: Remote
Tags: AI, Research Engineer, Reinforcement Learning, Fintech, Remote, NLP, Machine Learning
Summary: Join 's AI model team to drive innovation in reinforcement learning approaches for advanced models, optimizing decision-making and adaptive behavior for real-world challenges.