We are a next-generation financial operations firm led by a successful FinTech founder, building the future of investment operations powered by AI. Our mission is to revolutionize how hedge funds and asset managers run their businesses, leveraging technology, data, and deep industry expertise. We offer:

• A fast-track career path at the cutting edge of finance and AI.
• Work directly with top-tier global asset managers and founders.
• Competitive salary and bonus.
• Generous equity grant – be a part of our growth journey.
• Comprehensive benefits package including health, wellness, and career development

We're building the tools that will power the future of back and middle office finance—smart, scalable, and fully automated and we are looking for a talented and ambitious Machine Learning Engineer with at least 2 years of experience to join us on this journey.

• Design and build ML components that power agentic workflows for operational finance tasks (e.g., document extraction, anomaly detection, data classification, and more)

• Collaborate closely with product, engineering, and domain experts to understand real-world financial workflows and automate them using AI
• Fine-tune and deploy LLMs and other models to support contextual understanding, reasoning, and decision-making in financial operations
• Develop and maintain pipelines for data labeling, training, evaluation, and deployment in production
• Continuously monitor model performance, retraining and refining as needed to improve outcomes and robustness
• Explore and experiment with emerging tools in LLMs, multi-agent architectures, RLHF, and agentic systems to keep us at the bleeding edge

• 2+ years of experience in machine learning, preferably building real-world AI products
• Strong understanding of ML fundamentals, including supervised/unsupervised learning, model evaluation, data preprocessing, and feature engineering
• Hands-on experience with deep learning frameworks like PyTorch or TensorFlow
• Experience working with LLMs or NLP is a big plus (e.g., fine-tuning models, prompt engineering, embedding-based retrieval)
• Solid coding skills in Python and familiarity with ML ops workflows (model versioning, deployment, etc.)
• A growth mindset — you’re excited to learn from a team that has built and scaled successful FinTech platforms before
• Bonus if you’ve worked in or have knowledge of financial services or enterprise workflows, but not required