Senior Machine Learning Engineer

Summary

Join Typeform's Data & Insights team as a Machine Learning Engineer and design, develop, and deploy scalable machine learning systems for personalized user experiences. You will build and deploy ML solutions using tools like Docker, Kubernetes, and AWS, leverage vector databases and streaming systems, and standardize workflows with MLflow. Collaboration with cross-functional teams is key, as is developing cutting-edge generative AI capabilities and evaluating AI features. This role requires 4+ years of experience in building and deploying ML models, proficiency in Python and ML frameworks, and experience with AWS and various ML tools. The ideal candidate will also possess experience with orchestration tools and a deep understanding of LLMs and generative AI.

Requirements

• 4+ years of hands-on experience in building and deploying ML models in production environments
• Strong proficiency in Python and popular ML Frameworks such as PyTorch, LangChain, Agents
• Experience with AWS Cloud, Kubernetes, ArgoCD, Docker, Terraform, Jenkins and strong understanding of CI/CD pipelines for ML and model deployment best practices
• Experience with monitoring ML models using Datadog and/or OpenSearch
• Experience with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust
• Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock
• Hands-on expertise with Kafka and vector databases
• Experience managing ML lifecycle workflows with MLflow
• Deep understanding of LLMs and generative AI, with experience applying them to solve business problems
• Ability to collaborate with cross-functional teams and communicate technical concepts to non-technical stakeholders
• Familiarity with Enterprise RAG Systems, including chunking, reranking techniques, etc

Responsibilities

• Build and deploy scalable ML solutions: Design, train, and deploy machine learning models and workflows with a focus on production-readiness, leveraging tools like Docker Containers, Kubernetes, MLflow, Kafka, and AWS Services
• Leverage vector databases and streaming systems: Design and implement solutions with vector databases and Kafka to handle large-scale, high-dimensional, real-time data processing for ML and AI pipelines
• Standardize workflows: Use MLflow to manage the end-to-end ML lifecycle, including experiment tracking, model registry, and deployment
• Automate and orchestrate: Use orchestration tools like Airflow to manage complex ML workflows and ensure seamless execution at scale
• Optimize infrastructure: Design efficient ML pipelines and leverage cloud services like AWS to ensure reliable, scalable, and cost-effective solutions
• Develop cutting-edge generative AI capabilities: Apply your expertise in LLMs and generative AI to enhance our products and build new AI features, enabling new and creative ways to interact with AI
• Evaluate generative AI applications: Help R&D teams assess and refine AI features. Build automated evaluation pipelines for model performance. Develop benchmarks to ensure accuracy, fairness, and reliability
• Collaborate across teams: Partner with Product, Engineering, Data Engineering, and Analytics teams to align ML initiatives with business objectives and optimize for maximum impact
• Stay ahead of the curve: Keep up with emerging trends, research advancements, and best practices to drive innovation and enhance our AI capabilities

Preferred Qualifications

• You have experience working in a B2B SaaS company
• Experience with orchestration tools (e.g. Airflow)
• Familiarity with SQL, Spark, or other data processing frameworks
• Knowledge of Snowflake or other cloud data warehouses
• Strong familiarity with agentic frameworks for decision-making systems