Senior Machine Learning Engineer, Typeform

• 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

Desirable

• At Typeform, the Data & Insights team’s charter is to make data actionable through multiple mediums—reports, dashboards, AI/ML-models—that help us fuel growth and drive efficiencies within our business. Within that charter, our Data Science practice focuses on building cutting-edge machine learning capabilities that help revolutionize how we leverage data and empower our customers to collect information in a conversational and personalized way
• As a Machine Learning Engineer at Typeform, your mission will be to design, develop, and deploy scalable machine learning systems that enable Typeform to deliver more personalized and impactful experiences. You will work closely with cross-functional teams to implement ML models, build robust pipelines, and contribute to the innovation of our AI-powered products, including leveraging LLMs and generative AI
• 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