Who are we looking for? 

We are seeking a talented and experienced Backend Engineer (SDE – 2) to join our distributed team. In this role, you will be responsible for designing, developing, and maintaining scalable and secure backend systems. As a key contributor, you will collaborate with engineers, product managers, and stakeholders to ensure backend infrastructure delivers an exceptional user experience while maintaining high performance and reliability. 

Responsibilities: 

• Design, develop, and maintain backend services, focusing on performance, security, and scalability. 

• Proactively communicate with team members before starting implementation to align on goals, requirements, and potential challenges. 

• Build reusable, maintainable code and ensure backend systems are optimized for performance and efficiency. 

• Collaborate closely with frontend engineers to ensure backend systems align with product needs and contribute to a seamless user experience. 

• Work with product designers and managers during the design phase to review requirements, ask questions, and clarify any ambiguities, ensuring that the technical implementation supports the overall product vision. 

• Take ownership of backend architecture, suggesting improvements to enhance scalability, reliability, and code quality. 

• Break down complex technical issues into manageable tasks and milestones, ensuring timely delivery while maintaining high standards. 

• Write clean, testable code and participate in code reviews to maintain high-quality standards across the codebase. 

• Continuously monitor and improve system performance, addressing any bottlenecks and ensuring scalability. 

Requirements: 

• 3+ years of experience in backend development using any modern technology stack. 

• Strong expertise in backend development, including API design, database management, and cloud infrastructure. 

• Proactive in communication, ensuring alignment and clarity across team members before starting projects/features. 

• Strong problem-solving skills, with the ability to quickly grasp new concepts and apply them effectively. 

• Familiarity with testing, CI/CD pipelines, and version control tools. 

• Excellent collaboration and communication skills, with the ability to work effectively in a distributed team environment. 

Preferred: 

• Knowledge of Ruby on Rails or similar frameworks. 

• Understanding of the Multiwoven open-source project, including its architecture and key components. 

• Leverage 5+ years of experience in data science to design, implement, and optimize machine learning models and pipelines.
• Develop, fine-tune, and evaluate large language models (LLMs) for a variety of applications, ensuring accuracy, performance, and robustness.
• Collaborate with engineering and product teams to integrate AI/ML solutions into our platform in a scalable and maintainable way.
• Conduct applied research, staying current on advances in LLMs, generative AI, and data science methodologies, and translate them into practical solutions.
• Build end-to-end workflows, from data exploration and feature engineering to training, validation, deployment, and monitoring in production.
• Apply modern containerization and orchestration techniques (e.g., Docker, Kubernetes) to support reproducible experimentation and deployment.
• Work with cloud platforms (e.g., Databricks, AWS, GCP, Azure) to manage data pipelines, large-scale training jobs, and distributed systems.
• Collaborate across teams to ensure our AI capabilities align with platform goals and business needs.

• 5+ years of experience as a Data Scientist or Machine Learning Engineer, with proven success in deploying models to production.
• Hands-on experience with large language models (LLMs); fine-tuning experience strongly preferred.
• Strong background in Python and ML frameworks such as PyTorch or TensorFlow.
• Proficiency in containerization and orchestration technologies (Docker, Kubernetes).
• Experience with cloud platforms and ML ecosystems (Databricks, AWS, GCP, Azure).
• Familiarity with MLOps best practices, including model deployment, monitoring, and CI/CD for ML.
• Strong analytical and problem-solving skills, with the ability to translate research into production-ready solutions.
• Excellent communication and collaboration skills, with the ability to work effectively across product, engineering, and leadership teams.
• A proactive, self-starter mindset with a passion for applied research and innovation.

 Position Overview

We are seeking a skilled and proactive QA SDET – 2 to join our distribution team. This isn't your typical "just run the test cases" role. We need someone who can deeply understand our AI ecosystem, take ownership of quality across the product, and drive testing excellence through automation and first-principles thinking. You'll work autonomously in a remote environment, requiring strong self-direction, excellent communication, and genuine curiosity about both the product and the testing craft. 

 You will be responsible for 

• Design, develop, and maintain automated test suites with a goal of automating 80% of integration tests, ensuring comprehensive coverage and reliability. 

• Go beyond surface-level testing by truly grasping how our AI product works, the user journey, edge cases, and potential failure modes. 

• Identify quality risks early, surface issues before they reach production, and continuously improve our testing processes without waiting to be told. 

• Build reusable test frameworks and components using modern testing tools and best practices. 

• Break down complex testing challenges from the ground up, questioning assumptions and designing effective test strategies. 

• Produce clear video demonstrations of issues, test scenarios, and product features, along with well-structured documentation for test cases, automation frameworks, and quality processes. 

• Own the delivery of an entire software development test suites and framework 

• Work closely with cross-functional stakeholders to understand requirements, provide quality insights early in the development cycle, and ensure testability is built into features. 

• Articulate quality concerns, test results, and recommendations clearly in a remote environment through written updates, documentation, and team discussions. 

Requirements 

• 2+ years of experience in QA/SDET roles with a strong focus on test automation and integration testing 

• Proficiency in languages like JavaScript/TypeScript, Python, and Ruby for writing test automation 

• Experience with tools like Playwright, Selenium, Jest, or similar frameworks for frontend and API testing 

• Proven ability to design and implement comprehensive test strategies that cover complex user workflows 

• Ability to break down problems from fundamentals, question assumptions, and design effective testing approaches rather than following rote processes 

• Comfortable creating detailed documentation, test plans, bug reports, and asynchronous updates for a distributed team 

• Takes initiative to identify issues, improve processes, and learn new technologies without constant direction 

• Passion for understanding how things work, staying current with testing best practices, and diving deep into the AI/ML domain 

• Proven ability to work effectively in a distributed team environment with minimal supervision 

• Design, implement, and maintain ML deployment pipelines for scalable production systems.
• Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability.
• Build robust model monitoring, logging, and alerting systems to track performance and detect drift.
• Partner with data scientists to transition models from research/prototype into production-ready deployments.
• Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment.
• Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems.
• Apply containerization and orchestration (Docker, Kubernetes) to enable reproducible, scalable systems.
• Collaborate with cross-functional teams to ensure ML systems align with platform goals and business requirements.

• 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.
• Proven experience deploying and maintaining machine learning models in production at scale.
• Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
• Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
• Deep knowledge of containerization (Docker) and orchestration (Kubernetes) for production ML systems.
• Expertise with cloud platforms (AWS, GCP, Azure) for ML deployment and scaling.
• Strong understanding of MLOps best practices, monitoring, and automation.
• Excellent problem-solving skills, with an emphasis on building reliable, scalable systems.
• Strong communication and collaboration skills across technical and non-technical teams.