Bridgewise is a pioneering technological research company that leverages proprietary AI-based analysis and language models to provide comprehensive insights into global stocks in multiple languages. Our mission is to bridge the knowledge gap in the investment world and empower investors to become "super-investors." With our generative AI technology integrated into brokerage platforms and financial institutions' infrastructures, we offer instant fundamental analyses of global stocks, enabling informed investment decisions for millions of investors worldwide.

• Master's degree (2nd degree) in Computer Science - with a strong algorithmic focus.
• 5+ years of hands-on software development experience, with at least 3 years of professional Python development.
• Deep expertise in algorithms and data structures: complexity analysis, graph algorithms, dynamic programming, sorting, searching, and optimization techniques.
• Strong mathematical foundations: probability, statistics, linear algebra, combinatorics, and numerical methods.
• Experience using AI-assisted development tools (e.g., Claude Code, GitHub Copilot) to accelerate development and improve code quality.
• Experience designing and building microservices and RESTful APIs at scale.
• Proven ability to independently take a task from inception to production - self-directed and accountable.
• Strong collaboration and communication skills - a team player who shares knowledge and elevates those around them.
• Familiarity with data science and ML libraries: NumPy, pandas, scikit-learn, SciPy, PyTorch, HuggingFace Transformers, and LangChain.
• Understanding and hands-on experience with time series analysis, factor models, and risk metrics.

As an Algorithm Developer at Bridgewise, you will be at the core of our AI-powered financial research engine. You will design, implement, and optimize complex algorithms that power our analytical products. We are looking for someone who can take full ownership of a feature - from research and design through to production deployment - and thrives in a collaborative, fast-moving environment.

1. Algorithm Design & Development:

• Design, implement, and optimize algorithms for financial data analysis, scoring, and ranking.
• Analyze algorithmic complexity and ensure solutions scale to large volumes of real-time market data.
• Translate business and research requirements into efficient, production-grade algorithmic solutions.

2. Data Processing & Analysis:

• Build robust Python pipelines to ingest, clean, transform, and analyze large financial datasets.
• Apply statistical methods, time-series analysis, and quantitative techniques to derive actionable insights.

3. Performance Optimization:

• Profile and benchmark algorithms for latency and throughput.
• Identify bottlenecks and apply optimization techniques (vectorization, caching, parallelism).

4. Research & Innovation:

• Stay current with academic and industry developments in algorithms, ML, and quantitative Methods.
• Prototype and evaluate new approaches; deliver rigorous empirical comparisons before productizing.

5. Microservices & Integration:

• Develop and maintain algorithm services as part of our microservices architecture.
• Design and expose clean REST APIs consumed by downstream services and frontends.

6. Code Quality & Collaboration:

• Write clean, well-tested, maintainable code and actively participate in code reviews.
• Work closely with data scientists, backend engineers, and product managers to deliver end-to-end solutions.
• Contribute to architecture and design decisions across the team.

• Hands-on experience with Apache Kafka for event-driven or streaming data architectures.
• Kubernetes (k8s) and Docker for containerized deployments.
• AWS cloud services (EC2, Lambda, S3, ECS/EKS).
• Experience working with large language models (LLMs) such as GPT, Gemini, or Claude (API integration, prompt engineering, evaluation).
• Domain knowledge in financial markets, fintech, or quantitative analysis (stocks, indices, fundamentals, valuation models).
• Experience with NoSQL databases (e.g., Couchbase, Redis, Elasticsearch).