Lead Backend & API Engineer at LGND

About LGND

LGND is an early-stage startup revolutionizing geospatial AI infrastructure. We bridge the gap between large Earth observation models and specific application developers, enabling intuitive interaction with geospatial data. Our core mission is to empower decision-makers with rapid insights from vast, complex datasets. As part of our small, dynamic team, you will play a foundational role in building tools that have never existed before.

Role Summary

We're seeking an experienced and visionary Lead Backend Engineer to design, build, and scale the backend systems and APIs that are the backbone of the LGND platform. You will be responsible for architecting robust, high-performance services that handle user authentication, request management, and the orchestration of complex geospatial data processing and machine learning workflows. You will ensure our backend can handle massive scale, deliver low-latency responses, and provide a reliable foundation for our web application and SDKs.

This role is ideal for a seasoned backend engineer with a strong track record of building scalable, production-grade APIs and distributed systems. You should be comfortable with ambiguity, passionate about building robust infrastructure, and eager to collaborate across various disciplines. While experience with AI and geospatial technologies is a plus, a solid engineering foundation and a willingness to learn quickly are essential. This role offers significant technical leadership opportunities and the chance to shape the future of LGND's technology stack.

This is a hybrid role, with a preference for candidates based in New York, San Francisco Bay Area, or Copenhagen. Remote work is also a possibility.

Key Responsibilities

• API Architecture & Development: Design, implement, and maintain highly scalable, secure, and well-documented RESTful APIs using modern Python frameworks (specifically FastAPI). Ensure API contracts are clear, consistent, and meet the needs of frontend applications and external integrations.
• Backend System Design: Architect and build robust backend services for managing user requests, orchestrating complex workflows (e.g., triggering geospatial data processing, model inference tasks), managing metadata (models, datasets, tasks), and interacting with various data stores.
• Scalability & Performance Engineering: Design and implement solutions for high throughput and low latency. Utilize techniques like asynchronous processing (Celery), caching, database optimization, and efficient resource management to ensure the system scales gracefully.
• Authentication & Security: Implement and manage robust authentication and authorization mechanisms (OAuth2, JWT) to ensure enterprise-grade security for the API and data.
• Database Interaction: Design schemas and interact efficiently with various databases, including relational databases (PostgreSQL/PostGIS), and potentially NoSQL and Vector Databases (pgvector, Pinecone) for storing metadata, task status, and enabling search capabilities.
• Infrastructure & Deployment: Collaborate with DevOps (or initially handle aspects of) CI/CD pipelines, monitoring, logging, and deploying backend services on cloud platforms (primarily AWS). Ensure observability and reliability of backend systems.
• Integration & Collaboration: Work closely with front-end engineers, data scientists, ML engineers, and leadership to define requirements, integrate components, and ensure seamless end-to-end functionality. Partner with external groups as needed.
• Technical Leadership & Best Practices: Establish and champion backend engineering best practices (coding standards, testing, security, documentation). Drive technical decisions, mentor future hires, and ensure the long-term health and maintainability of the backend codebase.

Scope of Work: First 3 Months

• Enhance Core API: Take ownership of the existing FastAPI application. Improve its scalability, implement robust authentication/authorization, enhance documentation, error handling, and expand monitoring/logging capabilities.
• Orchestration Endpoints: Design and implement API endpoints to manage and orchestrate the core geospatial embedding generation workflow. This includes endpoints for initiating processing tasks based on user parameters (area, time, model), tracking task status, and retrieving results or errors.
• Asynchronous Processing: Integrate an asynchronous task queue (Celery) to handle long-running inference and data processing tasks initiated via the API, ensuring the API remains responsive.
• Database Refinement: Refine database schemas (PostgreSQL) for storing task metadata, status, and potentially pointers to results (e.g., embeddings in a vector DB, processed data in S3). Optimize queries for performance.
• Improve API Documentation: Enhance the automatically generated OpenAPI/Swagger documentation with clearer descriptions, examples, and usage guidelines.

Note: Even if you don't fulfill all the criteria, you are still encouraged to apply. We welcome applications from a diverse background.

Required Technical Skills:

• Strong Python Proficiency: Deep understanding of Python and its ecosystem for backend development.
• Backend Framework Expertise: Proven experience designing, building, and scaling APIs with modern Python web frameworks (FastAPI strongly preferred).
• API Design & Architecture: Expertise in RESTful API design principles, microservices architecture, and building distributed systems.
• Database Experience: Solid experience with relational databases (PostgreSQL) including schema design and query optimization. Experience with ORMs like SQLAlchemy.
• Scalability Techniques: Experience with asynchronous task queues (Celery), caching strategies (Redis), and designing for high availability and throughput.
• Authentication/Authorization: Experience implementing secure authentication/authorization patterns (OAuth2, JWT).
• Containerization: Proficiency with Docker for building and deploying applications.
• Cloud Platforms: Experience deploying and managing applications on cloud platforms (AWS preferred: EC2, S3, RDS).
• Version Control & CI/CD: Proficiency with Git and experience setting up/using CI/CD pipelines (GitHub Actions).
• AI use: We encourage the responsible use of AI to support your work. Our products, systems and team routinely uses different AI workflows to help develop, test, automate, CI, …

Preferred Experience:

• Experience with geospatial libraries (Rasterio, GeoPandas).
• Familiarity with Vector Databases (pgvector, Pinecone).
• Understanding of MLOps principles and tools.
• Experience with infrastructure as code (Terraform).

Soft Skills:

• Self-Led & Proactive: Ability to take ownership and drive projects forward.
• Problem Solver: Strong analytical and problem-solving skills.
• Builder Mentality: Excited to build foundational systems.
• Collaborative & Humble: Excellent communication and teamwork skills.
• Pragmatic: Focuses on delivering value effectively.

Benefits & Cultural Values

• Humility: Value collaboration and learning from others.
• Integrity: Uphold honesty and transparency.
• Effectiveness: Results-driven with a focus on impactful solutions.

Benefits

• Competitive salary based on experience.
• Equity options in an early-stage startup.
• Healthcare and 401k
• Flexible work arrangements (hybrid/remote).
• Opportunity to shape LGND's technology and culture.
• Work on cutting-edge problems in Geospatial AI.

Hiring Process:

1. Soft Filter: Initial review of applications, CV and cover letter.
2. Team Review: Shortlisted candidates reviewed by the team.
3. Initial Interview: Selected candidates invited for a Get-to-know-you interview.
4. Async Coding Test: Candidates given an asynchronous coding test.
5. Deep-dive Interviews: one to three additional interviews with team members.
6. Offer: Final offer extended to the selected candidate.