Research Engineer, Applied AI

Aleph (Headquarters: Remote)

AI Machine Learning Research Python GCP FP&A Finance Data Science Remote Applied AI TensorFlow PyTorch Scikit-learn NLP AI Engineer Data Science

Company: Aleph is redefining Financial Planning & Analysis (FP&A) with an AI-native platform, backed by top VCs and serving clients like Notion, Zapier, and Turo. We are a remote-first company focused on centralizing financial data and empowering finance teams through AI. Join our mission-driven, low-ego team to reshape the future of finance. Enjoy benefits like remote work, equity, health insurance, 401k matching (US), unlimited PTO, and regular offsite retreats.

Role: As a Research Engineer focused on Applied AI, you will be at forefront of integrating cutting-edge artificial intelligence and machine learning techniques into Aleph's FP&A platform. You will research, prototype, and implement AI-driven features that automate complex financial workflows, extract insights from data, and enhance decision-making for our users. This role involves bridging the gap between theoretical research and practical application in a fast-paced startup environment.

Responsibilities:
- Research state-of-the-art AI/ML techniques relevant to FP&A challenges (e.g., forecasting, anomaly detection, data analysis, NLP).
- Design, develop, and evaluate AI models and algorithms.
- Implement and deploy AI solutions into our production systems (Tech stack includes Python, GCP).
- Collaborate with product managers and engineers to define and deliver AI features.
- Stay abreast of the latest advancements in AI/ML and identify opportunities for innovation.
- Analyze data to understand user behavior and improve model performance.

Ideal Candidate:
- Strong background in Computer Science, AI, Machine Learning, or a related field (MS or PhD preferred, or equivalent experience).
- Proven experience in applying AI/ML techniques to real-world problems.
- Proficiency in Python and relevant AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Experience with data processing, feature engineering, and model evaluation.
- Familiarity with cloud platforms (GCP preferred) and MLOps practices.
- Strong analytical and problem-solving skills.
- Ability to communicate complex technical concepts clearly.
- Interest in finance or FP&A is a plus.