Sr. Machine Learning Engineer

Typeform

Software para Internet

España

Project Manager Sénior / Especialista sénior

Remoto

Experteer Overview

As a Machine Learning Engineer at Typeform, you design, build, and deploy scalable ML systems that power personalized, AI-driven user experiences. You collaborate with cross-functional teams to implement models and robust pipelines, supporting our AI-powered products with LLMs and generative AI. You’ll work in our Data & Insights team to push innovations and ensure production-ready solutions at scale. This is your chance to shape conversational experiences for millions of users in a fast-paced, remote-first environment.

Compensaciones / Beneficios

remote work
fully remote by design

Responsabilidades

Build and deploy scalable ML solutions and workflows with production readiness using Docker, Kubernetes, MLflow, Kafka, and AWS services
Design and implement vector database and streaming-based pipelines for large-scale, real-time ML data
Standardize ML workflows with MLflow (experiment tracking, model registry, deployment)
Automate and orchestrate ML processes using Airflow for scalable execution
Optimize ML infrastructure and leverage AWS to ensure reliable, cost-effective pipelines
Develop generative AI capabilities (LLMs) to enhance products and enable new AI features
Evaluate AI features, build automated evaluation pipelines, and establish benchmarks for accuracy, fairness, and reliability
Collaborate with Product, Engineering, Data Engineering, and Analytics to align ML with business goals
Stay current with AI research and best practices to drive innovation

Requisitos principales

4+ years of hands-on ML model building and production deployment
Strong Python and ML frameworks experience (PyTorch, LangChain, Agents)
AWS Cloud, Kubernetes, ArgoCD, Docker, Terraform, Jenkins; CI/CD for ML
ML monitoring (Datadog and/or OpenSearch)
Building ML services with FastAPI or stream processing with Faust
Jupyter Notebooks, AWS SageMaker, AWS Bedrock
Kafka and vector databases; MLflow for lifecycle management
Deep understanding of LLMs and generative AI in business contexts
Cross-functional collaboration and ability to explain technical concepts to non-technical stakeholders
Familiarity with Enterprise RAG Systems (chunking, reranking)