Private markets are one of the largest, most complex, and most underserved corners of global finance. Our mission at Juniper Square is to unlock their full potential. We’re the Operations Partner trusted by 2,300+ GPs, unifying technology, data, and fund administration services into a single platform that helps GPs move faster, make better decisions, and scale with precision. With $300B+ under administration and 700,000+ LPs on platform, we’ve built the scale to match our ambition. And with JunieAI, our purpose-built AI platform, we’re reimagining how private markets operate, embedding intelligence across every workflow. Founder-led since 2014, backed by $350M+ in funding, and now 1,000+ employees strong, we’re building a company designed to shape the future of private markets for decades to come.
Our culture is built for people who want to do ambitious, meaningful work alongside exceptionally talented teammates. We think like owners, move with urgency, and take pride in solving hard problems that truly matter to our customers and the future of private markets. We believe the best ideas come from open debate, deep collaboration, and diverse perspectives, which is why we believe transparency is the default and feedback makes us stronger. If you’re energized by high standards, rapid growth, and the opportunity to help define a category at a pivotal moment, come join us!
Juniper Square offers employees a variety of ways to work, ranging from a fully remote experience to working full-time in one of our physical offices. We invest heavily in digital-first operations, allowing our teams to collaborate effectively across 27 U.S. states, 2 Canadian Provinces, India, Luxembourg, and England. We also have physical offices in San Francisco, New York City, Mumbai and Bangalore for employees who prefer to work in an office some or all of the time.
As a Staff Software Engineer at Juniper Square, you will set the technical direction for our AI-powered document intelligence platform and drive execution across a cross-functional pod of engineering, product, and design. This is a technical leadership role - you will own the architecture, define the roadmap, and be the person the pod looks to for engineering judgment and decisions.
You will lead the technical strategy for structured document extraction - transforming unstructured financial documents (K-1s, capital call notices, subscription agreements, fund reports) into structured, queryable data at scale - and own the architecture and ongoing evolution of our in-house RAG pipeline, enabling intelligent retrieval and generation over private-markets documents. Beyond the technical work itself, you will identify and drive improvements to the team's engineering efficiency, stability, and consistency, and collaborate closely with your product and design counterparts to translate complex workflows into simple, intuitive user experiences.
This is a high-impact role at the intersection of applied AI, backend systems, and data engineering, where your work directly powers core product experiences for our customers.
AI-Native Culture & Velocity: Champion and embed AI-native development practices and tools (e.g., Cursor, Augment) to achieve significant productivity gains, fostering a "startup-mode" culture of rapid iteration, high velocity, and quality, including guiding the effective use of AI code generation
Technical Ownership & Architecture: Take ownership over the team's architecture, actively participating in design reviews and driving the long-term technical vision.
Individual Coding Contribution: Take ownership of team code, and actively participate in coding, testing and delivering roadmap projects. Write high-quality code that is well-tested, secure, and maintainable
Workflow Automation & AI Integration: Lead the team in designing and implementing document processing pipelines — including extraction, classification, chunking, embedding, and retrieval — and identify opportunities to leverage LLMs and modern AI tooling to improve accuracy, coverage, and scalability.
Document AI Pipeline Ownership: Own the end-to-end design and reliability of document extraction and RAG pipelines — from ingestion and preprocessing through model inference, post-processing, and structured output delivery. Define quality benchmarks, evaluation frameworks, and feedback loops to continuously improve extraction accuracy.
Execution & Project Management: Effectively manage the team's short-term roadmap (spanning the next two quarters), actively identifying risks and creating clear mitigation strategies to ensure successful project delivery
Team Leadership & Mentorship: Provide backend and AI/ML-focused technical leadership, leveling up existing team members and helping build high-performance teams. Mentor engineers, fostering their technical and professional growth
Cross-Functional Collaboration: Collaborate with cross-functional partners (Product, UX, QA, Customer Support) to ensure the team meets project timelines and solutions align with business strategy. Handle most cross-team conflicts and decisions autonomously
Operational Excellence: Own monitoring, diagnosing, and resolving production issues within the team's services
Code Reviews & Quality: Contribute directly to efforts through building features and frameworks, conducting code reviews, participating in architecture and system design discussions
Best Practices Implementation: Implement and ensure best practices across the teams to maximize developer productivity
Platform & Developer Experience: Actively seek opportunities to improve our platform and developer experience and own those initiatives through execution
Hiring & Team Building: Partner with recruiting to build and grow the team
Domain Expertise: Grow into a subject matter expert (SME) in AI document extraction and RAG systems, with a deep understanding of how structured data extraction creates value across the private markets workflow.
Required
Bachelor's degree in Computer Science, Mathematics, AI/ML, or a related technical field.
7+ years of backend and/or ML engineering experience, with a trajectory of increasing technical leadership, architectural responsibility, and mentorship.
Deep expertise in Python, with strong proficiency in building production-grade backend services and data pipelines; experience with other server-side languages (Node/TS, Java) a plus.
Solid understanding of Python web frameworks (like Django or FastAPI)
Hands-on experience designing and operating document processing pipelines, including parsing, extraction, classification, and structured output generation from unstructured documents (PDFs, scanned files, financial forms, etc.)
Production experience building and operating RAG systems, including chunking strategies, embedding models, vector stores (e.g., pgvector, Pinecone, Weaviate), retrieval, and reranking
Experience evaluating and improving LLM-based extraction quality — including designing eval frameworks, handling edge cases, and building human-in-the-loop feedback mechanisms
Familiarity with model serving, inference optimization, and managing LLM API costs at scale
Experience with LLM application patterns beyond basic RAG — including tool-calling agents, planning/execution loops, and multi-step reasoning systems — and how these apply to document intelligence workflows
Experience with Relational Databases like Postgres or MySQL
Experience with Cloud technologies (AWS preferred) and Container technologies (Docker and k8s)
Deep understanding of service-oriented architecture, modern software development practices, and developing scalable, reliable systems
Ability to identify and evaluate opportunities to integrate AI capabilities into products and workflows
Demonstrable product focus and a keen understanding of how technology can solve customer problems and drive business outcomes
Highly self-driven, with a proactive approach to leadership, technical problem-solving, and initiative execution
Experience working in agile development environments and familiarity with practices that promote rapid iteration and velocity
Excellent communication and collaboration skills, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders and align them on product goals
Proven ability to lead projects end-to-end with a player-coach mindset — hands-on in code and architecture while working autonomously with product partners
Ability to manage multiple priorities and lead teams effectively in a fast-paced environment, with the flexibility to adapt as needs shift
Demonstrated track record of mentoring engineers and elevating team technical capability
Hands-on experience with AI-native development tools (e.g., Cursor, Augment, Loveable); demonstrated ability to embed AI-driven practices to accelerate team velocity and code quality
Ability to critically evaluate AI-generated code and outputs, including identifying failure modes, regressions, and edge cases introduced by AI-assisted development
Experience building and shipping production-grade software using AI-assisted workflows across the full SDLC
Nice to Have
Experience with OCR technologies and document understanding models (e.g., AWS Textract, Azure Document Intelligence, LayoutLM, Donut)
Background in financial document processing or fintech data pipelines
Experience with MLOps tooling (experiment tracking, model registries, deployment pipelines)
Familiarity with evaluation frameworks for LLM/extraction quality (e.g., RAGAS, custom evals, human review pipelines)
Experience with multi-modal models or vision-language models for document understanding
Knowledge of data privacy and compliance considerations in document processing pipelines (PII handling, encryption, access controls)
Compensation for this position includes a base salary, equity and a variety of benefits. The U.S. base salary range is $210,000 - $260,000 USD. Actual base salaries will be based on candidate-specific factors, including experience, skillset, and location, and local minimum pay requirements as applicable.
Benefits include:
Health, dental, and vision care for you and your family
Life insurance
Mental wellness coverage
Fertility and growing family support
Flex Time Off in addition to company-paid holidays
Paid family leave, medical leave, and bereavement leave policies
Retirement saving plans
Allowance to customize your work and technology setup at home
Annual professional development stipend
Your recruiter can provide additional details about compensation and benefits.
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