Software Engineer (L1) As a Software Engineer, you’ll join a team responsible for designing, building, and operating production-ready software. This is an opportunity to gain hands-on experience across the full software development lifecycle — from designing solutions and writing high-quality code, to deploying, monitoring, and maintaining applications in production. You’ll be surrounded by experienced engineers who will support your learning and development, while also expecting you to contribute meaningfully to the team’s work. You’ll be building features across the stack — from back-end services and APIs to customer-facing front-ends — while gaining a deep understanding of how our systems work together to deliver value. You’ll learn to make thoughtful trade-offs between performance, maintainability, and speed of delivery. You’ll be encouraged to question assumptions, propose improvements, and take ownership of the quality and stability of your work. As part of our engineering culture, you are expected to adopt and actively use AI-based tools and coding assistants to increase your development efficiency and quality. You’ll be supported in learning these tools and expected to incorporate them into your day-to-day workflow as part of how we deliver software quickly and responsibly. You are also expected to take full responsibility for validating the changes you make — including unit tests, integration tests, end-to-end flows, and any required manual validation. You’ll learn how to build observability into your services, respond to production issues, and participate in CI/CD and automation workflows. While you may not yet lead conversations, you’ll begin developing the communication skills and business awareness required to collaborate across disciplines. You’ll work with product managers and designers to clarify scope and expected behavior, and over time, you’ll develop an understanding of how your work supports customer value and business priorities. What you’ll do: Build and maintain features and services across the stack (front-end, back-end, and infrastructure). Use AI-powered development tools to assist with coding, testing, and documentation. Collaborate with senior engineers and cross-functional partners (Product, Design, QA) to deliver customer value. Participate in code reviews, ask questions, and continually improve your technical skills. Write clean, testable code and contribute to automated test coverage. Take ownership for testing your work end to end — including unit, integration, and manual validation. Debug, troubleshoot, and resolve production issues — with support from your team. Learn and adopt DevOps practices including CI/CD, observability, and incident response. Participate in team stand-ups, planning sessions, and retrospectives. Requirements: Familiarity with at least one modern programming language (e.g., TypeScript, Java, Python, Kotlin, PHP). Basic understanding of web architecture (client/server), APIs, and databases. Exposure to version control systems and automated testing practices. Curiosity about AI tools and openness to integrating them into your development process. Desire to grow through feedback, collaboration, and real-world experience.

Senior DevOps Engineer (L3) As a Senior DevOps Engineer at Spring, you are a strategic leader and technical expert responsible for designing, building, and scaling our infrastructure platforms and delivery systems. You help set the standard for reliability, observability, cost-efficiency, and velocity across engineering — and play a critical role in enabling Spring to scale securely and sustainably. You own cross-functional infrastructure initiatives that support new products, integrations, or compliance requirements. You contribute to the long-term evolution of Spring’s platform architecture and collaborate closely with engineering, product, and business leadership to ensure infrastructure decisions support strategic objectives. You mentor other engineers, lead technical design efforts, and serve as a go-to partner for high-risk or business-critical systems. At this level, you’re expected to deeply understand how Spring’s technical architecture supports our business. You know how key services interact across domains — from payment reconciliation to identity verification to customer communications. You anticipate and mitigate systemic risks, lead readiness reviews, and help shape platform roadmaps that balance scale, reliability, and speed. You also collaborate with security, IT, sysadmins, and network teams on strategic concerns like multi-region availability, compliance automation, VPN routing, SOC2 readiness, data loss prevention, and endpoint security. What you’ll do: Lead the design and implementation of scalable, secure, and cost-efficient cloud infrastructure. Own architecture and execution of high-impact platform initiatives (e.g., CD migration, zero-downtime deploys, logging architecture). Collaborate with Security, IT, and Infrastructure teams to define and implement org-wide access, audit, and reliability standards. Proactively identify technical debt, scalability concerns, and risk across multiple systems and services. Guide platform investments and architecture strategy in partnership with engineering and product leadership. Mentor engineers and set best practices in observability, incident response, and infrastructure evolution. Requirements: Deep expertise in a major cloud provider, such as AWS, and Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or CDK. Extensive, hands-on experience with containerization and orchestration technologies, such as Docker, Kubernetes (and its variants like EKS), and Helm. Strong proficiency in a programming language like Python or Go for creating robust automation and tooling. Deep expertise in designing and managing CI/CD pipelines, with experience in modern practices like GitOps using tools such as ArgoCD or Flux. Expert-level knowledge of Linux/UNIX systems administration, version control (Git), and internals. Advanced understanding of networking (VPC design, DNS, routing, service mesh) and cloud security (IAM, threat modeling, compliance automation). Deep understanding of observability, monitoring, and alerting using tools such as the Prometheus stack (Prometheus, Grafana, Loki) and/or commercial platforms like Datadog. Proven experience leading infrastructure strategy and operating high-availability production systems. Strong leadership, cross-functional influence, and a business-first mindset when making technical trade-offs.

DevOps Engineer (L1) As a DevOps Engineer at Spring, you’ll help support the delivery and operation of scalable, secure, and reliable infrastructure. This is an ideal role for someone early in their DevOps career who is eager to grow their understanding of cloud systems, CI/CD pipelines, automation, and production support in a modern, fast-moving environment. You’ll work closely with more senior DevOps engineers and cross-functional teams to build internal tools, reduce operational toil, and maintain environments that power our core products. You’ll gain experience across AWS, infrastructure-as-code, monitoring, deployment pipelines, and security-conscious workflows — all while learning how infrastructure connects to real customer and business outcomes. You’ll also begin contributing to Spring’s internal platform tooling, learning how to support the systems our product and engineering teams rely on to ship and scale. At this level, you’re expected to begin developing an understanding of how Spring’s infrastructure supports our core business workflows — from customer onboarding and payment processing to internal reporting and operational efficiency. You’ll also start collaborating with internal IT, network administrators, sysadmins, and security teams to support secure developer environments, manage access, and ensure basic compliance with infrastructure hygiene and audit requirements. What you’ll do: Support CI/CD pipelines, build automation, and environment management for development, staging, and production. Write scripts and contribute to infrastructure-as-code used for provisioning and managing cloud resources. Participate in basic monitoring, logging, and incident response practices. Collaborate with engineers to identify and eliminate manual deployment or operational tasks. Work with internal IT and sysadmin teams to support secure developer access, device provisioning, and environment stability. Assist with patching, service configuration, and system documentation. Requirements: Basic experience with a major cloud provider, such as AWS. Familiarity with containerization technologies, such as Docker. Familiarity with version control systems such as Git. Proficiency in shell scripting and a basic knowledge of a programming language, such as Python, for automation. Basic experience with CI/CD principles and tools, with exposure to solutions like GitHub Actions considered a plus. Familiarity with Linux/UNIX operating systems and command-line interface. Basic understanding of networking concepts (e.g., TCP/IP, DNS, VPCs). Familiarity with infrastructure-as-code tools (e.g., Terraform, CloudFormation) is a plus. Eagerness to learn about security, cloud architecture, and developer tooling. Strong collaboration and communication skills; willingness to support internal partners and escalate when needed.

Machine Learning Engineer (L1) Experience Required: 2-4 years As a Machine Learning Engineer at Spring, you’ll help bring data-driven intelligence into our products and operations. You’ll support the development and deployment of models and pipelines that power smarter decisions, more personalized experiences, and scalable automation. This is an opportunity to build hands-on experience in real-world ML and AI systems while collaborating with experienced engineers and data scientists. You’ll work on data processing, model training, and integration tasks — gaining exposure to the entire ML lifecycle, from experimentation to production deployment. You’ll learn how to balance model performance with system requirements, and how to structure your code for reliability, observability, and maintainability. You’ll use modern ML/AI tools such as scikit-learn, HuggingFace, and LLM APIs — and be encouraged to explore AI techniques that improve our workflows or unlock new product value. You’ll also be expected to help build and support automated data pipelines, inference services, and validation tools as part of your contributions. You’ll work closely with engineering, product, and business stakeholders to understand how models drive value. Over time, you’ll build the skills and judgment needed to identify impactful use cases, communicate technical trade-offs, and contribute to the broader evolution of ML at Spring. What You’ll Do Support model development and deployment across structured and unstructured data and AI use cases. Build and maintain automated pipelines for data processing, training, and inference. Use ML and AI tools (e.g., scikit-learn, LLM APIs) in day-to-day development. Collaborate with engineers, data scientists, and product teams to scope and deliver features. Participate in code reviews, testing, and monitoring practices. Integrate ML systems into customer-facing applications and internal tools. Identify differences in data distribution that could affect model performance in real-world applications. Stay up to date with developments in the machine learning industry. Tech Expectations Core Skills Curiosity, attention to detail, strong debugging skills, and eagerness to learn through feedback Solid foundation in statistics and data interpretation Strong understanding of data structures, algorithms, and software development best practices Exposure to data pipelines, model training and evaluation, or training workflows Languages Must Have: Python, SQL ML Algorithms Must Have: Traditional modeling techniques (e.g., tree models, Naive Bayes, logistic regression) Ensemble methods (e.g., XGBoost, Random Forest, CatBoost, LightGBM) ML Libraries / Frameworks Must Have: scikit-learn, Hugging Face, Statsmodels, Optuna Good to Have: SHAP, Pytest Data Processing / Manipulation Must Have: pandas, NumPy Data Visualization Must Have: Plotly, Matplotlib Version Control Must Have: Git Others – Good to Have AWS (e.g., EC2, SageMaker, Lambda) Docker Airflow MLflow Github Actions

DevOps Engineer II (L2) Expereince Required: 4-6 years As an Intermediate DevOps Engineer at Spring, you take ownership of infrastructure, automation, and internal tooling that enables faster, safer, and more reliable product delivery. You work closely with engineers, product managers, security, and platform teams to reduce friction in our development pipeline, improve observability, and maintain resilient cloud systems. At this level, you’re expected to proactively identify gaps in reliability, speed, and automation, and then lead the design and implementation of improvements. You play a critical role in supporting Spring’s shift toward continuous delivery, service ownership, and modern security practices — helping product teams move quickly without compromising operational integrity. You’re expected to have a strong grasp of how Spring’s platform architecture supports key products (e.g., loan decisioning, Bloom, Hive) and core business functions like customer onboarding, compliance, and data warehousing. You’ll also work closely with Security, IT, and Sys Admin teams on access control, secure developer workflows, VPN and VPC configuration, patch management, and audit readiness. You may also participate in vendor integration or operational compliance initiatives. What you’ll do: Own and evolve infrastructure-as-code used for provisioning cloud services and environments. Maintain and improve CI/CD pipelines, secrets management, and deployment workflows. Implement observability tooling, automate failure detection, and reduce alert fatigue. Collaborate with platform, product, and data teams to scale services and reduce latency or bottlenecks. Work cross-functionally with IT and network teams on system hardening, identity and access management, and secure provisioning. Contribute to DR plans, security incident response, and internal system reliability improvements. Requirements: Experience with cloud infrastructure, such as AWS, and container technologies, like Docker. Strong scripting or programming experience in languages such as Python, Bash, or Go. Hands-on experience building and maintaining CI/CD pipelines using tools like GitHub Actions, Jenkins, or GitLab CI. Experience with Infrastructure as Code (IaC) tools such as Terraform or CloudFormation. Solid understanding of Linux/UNIX systems, networking concepts, and security best practices (e.g., IAM, security groups). Familiarity with observability and monitoring tools (e.g., Prometheus, Grafana, Datadog). Ability to work cross-functionally, balance trade-offs, and drive measurable improvements to system health and engineering velocity.

Senior Software Engineer As a Senior Software Engineer, you are a technical leader and a trusted problem solver who works at the intersection of business and engineering. You collaborate closely with stakeholders across product, operations, and the business to deeply understand the “why” behind the work — ensuring that the systems you design and build directly support meaningful business outcomes and customer needs. You bring clarity to complex problem spaces, propose scalable solutions, and drive execution from concept through production. You lead the design and implementation of critical systems — often those that require architectural foresight, cross-team coordination, or innovative thinking. You’re adept at balancing competing constraints, identifying long-term risks, and making thoughtful trade-offs between speed, quality, and extensibility. You have a track record of making systems simpler, more reliable, and more maintainable over time — and influencing others to do the same. You are expected to model and champion the use of AI tools in your development process — using them to increase speed, quality, and exploratory capacity. You help others adopt AI responsibly, and you remain critical of its output. At this level, you also play a key role in identifying opportunities to integrate AI capabilities into the products and services we build — especially where it can improve customer experience, internal efficiency, or system intelligence. You set the bar for testing, automation, and delivery excellence. You are a strong partner to both engineering and business stakeholders. You help product managers frame complex trade-offs, clarify ambiguous problems, and align technical decisions with business strategy. You represent engineering in high-stakes discussions and coach others to do the same. At this level, you are also expected to take ownership of system security — ensuring that services you build handle sensitive data responsibly and defend against threats with secure-by-default design. What you’ll do: Lead the design and implementation of scalable systems and services with significant technical and business impact. Champion the use of AI tools in the development process — guiding adoption and best practices. Identify opportunities to integrate AI into the product to improve customer outcomes and internal efficiency. Partner with cross-functional and business stakeholders to define problem spaces and propose technical solutions. Balance delivery with engineering quality, continuously raising the bar on code, systems, and architecture. Provide technical leadership through code reviews, mentorship, and design guidance. Drive operational excellence by improving reliability, observability, and incident response. Set standards for testing — ensuring all changes are fully validated via unit, integration, e2e, and manual testing. Improve build health, deployment automation, and test infrastructure in CI/CD workflows. Lead with a security-first mindset — owning the integrity of systems handling sensitive data or business logic. Requirements: Expertise in full-stack or backend development; familiarity with modern front-end stacks is a plus. Proven track record of leading complex technical projects and making architectural decisions. Strong understanding of system design, distributed systems, and performance optimization. Experience with infrastructure as code, CI/CD, monitoring, and on-call best practices. Familiarity with secure system design, threat modeling, and data protection principles. Strong grasp of AI development tools (e.g., code generation, intelligent search, test assistance) and their appropriate use. Experience integrating AI into product features or internal tools is a strong asset. Effective communicator who can drive consensus across engineering and business functions. Passion for mentorship, collaboration, and continuous improvement.

Senior Software Engineer As a Senior Software Engineer, you are a technical leader and a trusted problem solver who works at the intersection of business and engineering. You collaborate closely with stakeholders across product, operations, and the business to deeply understand the “why” behind the work — ensuring that the systems you design and build directly support meaningful business outcomes and customer needs. You bring clarity to complex problem spaces, propose scalable solutions, and drive execution from concept through production. You lead the design and implementation of critical systems — often those that require architectural foresight, cross-team coordination, or innovative thinking. You’re adept at balancing competing constraints, identifying long-term risks, and making thoughtful trade-offs between speed, quality, and extensibility. You have a track record of making systems simpler, more reliable, and more maintainable over time — and influencing others to do the same. You are expected to model and champion the use of AI tools in your development process — using them to increase speed, quality, and exploratory capacity. You help others adopt AI responsibly, and you remain critical of its output. At this level, you also play a key role in identifying opportunities to integrate AI capabilities into the products and services we build — especially where it can improve customer experience, internal efficiency, or system intelligence. You set the bar for testing, automation, and delivery excellence. You are a strong partner to both engineering and business stakeholders. You help product managers frame complex trade-offs, clarify ambiguous problems, and align technical decisions with business strategy. You represent engineering in high-stakes discussions and coach others to do the same. At this level, you are also expected to take ownership of system security — ensuring that services you build handle sensitive data responsibly and defend against threats with secure-by-default design. What you’ll do: Lead the design and implementation of scalable systems and services with significant technical and business impact. Proficient in Python. Champion the use of AI tools in the development process — guiding adoption and best practices. Identify opportunities to integrate AI into the product to improve customer outcomes and internal efficiency. Partner with cross-functional and business stakeholders to define problem spaces and propose technical solutions. Balance delivery with engineering quality, continuously raising the bar on code, systems, and architecture. Provide technical leadership through code reviews, mentorship, and design guidance. Drive operational excellence by improving reliability, observability, and incident response. Lead with a security-first mindset — owning the integrity of systems handling sensitive data or business logic. Good to have - AWS Knowledge Requirements: 7 plus years of work experience Expertise in full-stack or backend development; familiarity with modern front-end stacks is a plus. Proven track record of leading complex technical projects and making architectural decisions. Strong understanding of system design, distributed systems, and performance optimization. Experience with infrastructure as code, CI/CD, monitoring, and on-call best practices. Familiarity with secure system design, threat modeling, and data protection principles. Strong grasp of AI development tools (e.g., code generation, intelligent search, test assistance) and their appropriate use. Experience integrating AI into product features or internal tools is a strong asset. Effective communicator who can drive consensus across engineering and business functions. Passion for mentorship, collaboration, and continuous improvement.