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

• 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
• Tasks Leading:
• 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
• Tasks Supporting (alongside Data Scientists):
• 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