Senior Software Engineer

Daloopa is transforming how investment professionals work. Our mission is to eliminate the slow, error-prone parts of fundamental research—without sacrificing accuracy or auditability.

Founded by a former equity research analyst and top engineers, we built an AI-powered platform that converts complex financial filings, transcripts, and KPIs into clean, hyperlinked, customizable data. This means analysts spend less time on manual tasks and more time generating insights that drive performance.

Today, Daloopa powers the workflows of leading investment teams worldwide—helping them move faster, think deeper, and make every decision with confidence. If you’re passionate about building technology that solves real-world problems and want to make an impact, join us and help redefine the future of investment research.

• Competitive pay + performance incentives
• Equity in a fast-growing FinTech
• Career growth and mentorship opportunities
• A vibrant, collaborative culture
• Full benefits package

About the role

Location: New York City (Hybrid – this role requires 3 days per week in office)

Compensation: $173,000-193,000 base + equity + benefits (This range is reflective of level of contribution. Candidates who fully match what we’re looking for in this role will be at the top of the range.)

Daloopa's mission is to become the market leader in high-quality, actionable data for the world's top investment professionals. We're judged on three things: coverage, speed, and accuracy. Whether a hedge fund analyst can find the right number, get it fast, and trust it absolutely.

As a Senior Engineer, you’ll build and harden the systems that make that data usable in the real world, and you'll play an active role in how we develop, evaluate, and improve AI-powered features. That means getting close to LLMs: building pipelines, writing evals, and iterating on prompting strategies and model outputs. The work sits at the intersection of rigorous engineering and applied AI in a domain where the quality of your output actually moves capital.

If you're the kind of engineer who raises the bar around them, challenging ticket scope when something doesn't feel right, pushing code quality through thoughtful reviews, and actively helping teammates grow, this is a role for you. We are looking for someone who cares about correctness, challenges vague requirements, and raises the standard for how production AI in finance should behave.

What You’ll Do

• Design, develop, and maintain secure, scalable backend APIs and services using Python, Django, and FastAPI for enterprise customers.
• Work closely with LLMs, building pipelines, writing evaluations, and iterating on prompting strategies and model outputs to improve quality and reliability.
• Implement LLM observability, monitoring, and evaluation systems to track model performance, latency, and cost.
• Design efficient and scalable database schemas, optimize SQL queries, and maintain data integrity at scale.
• Collaborate with cross-functional teams, product, frontend, and data/AI, to translate requirements into well-architected technical solutions.
• Participate in code reviews and contribute to high coding standards and best practices.

Who You Are

• 5+ years of professional engineering experience.
• Strong proficiency in Python; experience with Django is a strong foundation.
• Hands‑on experience working with LLMs in production, building pipelines, writing evals, and improving model outputs.
• Experience with distributed systems, caching, and asynchronous task queues (Celery or equivalent).
• Solid understanding of RESTful and/or GraphQL API design and development.
• Ability to break down and drive complex technical problems to completion.
• Clear, direct communication and a strong sense of ownership over the outcomes of your work.
• Genuine interest in the problem domain. Financial data, supervised learning systems. You should be the kind of person who reads product launches from competitive companies and has opinions.
• Experience at high-growth, product-focused technology companies where you've owned systems in production and worked across teams.

Bonus Points

• Background in fintech, financial data, or other domains where data quality is mission-critical and audit trails matter.
• Familiarity with the financial fundamentals landscape: 10-Ks, 10-Qs, transcripts, segment reporting, non‑GAAP reconciliations.

Success Looks Like

• You build systems and AI‑powered features that are deeply trusted by customers because they are reliable, accurate, and improve meaningfully over time.
• You consistently raise the standard for quality and clarity, challenging assumptions, tightening requirements, and turning ambiguous problems into durable solutions.
• Teammates look to you as a thought partner and reviewer, and the bar for engineering and applied AI work rises because of your presence.