Full Stack AI Engineer, Doodle

Doodle AI Ecosystem

Salary not provided

Python

Kafka

Airflow

Tensorflow

Redshift

Spark

Scikit-Learn

Snowflake

PyTorch

BigQuery

dbt

Senior and Expert level

Berlin

More information about location

Online scheduling and meeting software

Be an early applicant

Online scheduling and meeting software

101-200 employees

B2BInternal toolsProductivitySaaS

Be an early applicant

Salary not provided

Python

Kafka

Airflow

Tensorflow

Redshift

Spark

Scikit-Learn

Snowflake

PyTorch

BigQuery

dbt

Senior and Expert level

Berlin

More information about location

101-200 employees

B2BInternal toolsProductivitySaaS

Company mission

To make scheduling as simple as possible.

Job

Company

Role

Who you are

We are looking for a practical AI builder who thinks in terms of customer value. Your qualifications should include:
End-to-End Ownership: You're a full-stack engineer, comfortable with the entire lifecycle of an AI component, from planning and modeling to testing and continuous improvement. You are capable of delivering an entire AI use case from inception to production
Modeling Expertise: You have experience designing, evaluating, and prototyping a variety of models. You are proficient in Python and have expertise in ML frameworks like SciKit-Learn, TensorFlow, PyTorch, and the Hugging Face ecosystem
MLOps and Data Proficiency: You have strong experience in MLOps foundations and tools, including MLflow and ZenML. You also have proven experience in dbt modeling and structuring data in both data warehouses and data lakes
Pipeline and Data Skills: You have hands-on experience building and scheduling data pipelines in Airflow and experience defining tracking schemas and event-level data for reliable analytics. You are familiar with modern data stacks (e.g., Kafka, Spark, or cloud data warehouses like BigQuery, Redshift, or Snowflake)
Problem-Solving & Evaluation: You understand model behavior and output evaluation. You are capable of developing evaluation frameworks for testing model output quality, reliability, and alignment with user goals (e.g., hallucination detection, prompt regression, safety scoring). You are also familiar with RAG, Graph-based retrieval, prompt design, and multi-hop reasoning
Practical Mindset: You are committed to building things that are reliable, explainable, and usable by others

What the job involves

We are looking for a hands-on Machine Learning Engineer who can own and deliver an entire AI use case from end to end. This isn't just about a single function; you'll be a true full-stack engineer who designs, builds, and deploys intelligent systems that deliver real, measurable customer value
Your work will span from architecting our core analytics infrastructure to developing and deploying cutting-edge models. You'll build robust data pipelines, engineer features, curate high-quality datasets for training, and architect AI systems that can handle complex, multi-step tasks and adapt to a user’s context to make intelligent decisions
Architect and Implement AI Systems: Lead the design and implementation of intelligent systems that go beyond a single model. You'll focus on delivering production-ready features, not just impressive demos, by selecting the right tools for the job—even if that means going beyond Large Language Models
Data Preparation and Curation: Prepare and curate high-quality datasets for modeling, keeping data quality at the center of all experimentation and delivery. You'll also design and build our feature store to ensure data and features are consistently and reliably available for both training and inference
Build Robust Data and ML Pipelines: Design and maintain end-to-end data and machine learning pipelines, from data ingestion to model deployment and monitoring. This includes building our analytics infrastructure with reusable dbt models and designing scalable pipelines using Airflow
Develop and Deploy Models: Take a hands-on role in developing and training models, ensuring they are performant, reliable, and reproducible. You will design, explore, and prototype solutions using various neural network architectures, including and beyond LLMs
Implement Advanced Retrieval Systems: Explore and implement solutions like Retrieval-Augmented Generation (RAG) or Graph RAG to improve the quality of information retrieval and reasoning in LLM workflows. This includes graph construction, entity linking, and hybrid scoring strategies
Focus on On-Device Intelligence: Quantize large models into smaller, more efficient models to enable edge intelligence and on-device processing where appropriate
Collaborate and Strategize: Work cross-functionally to define tracking schemas and event-level data that power both analytics and AI/ML initiatives. You'll also contribute to our AI strategy and roadmap, helping shape how we scale models responsibly across the platform

Application process

We usually respond within two weeks
Hiring Journey:
Initial Application Review
Personality & Strengths Assessment (via BRYQ)
Home Assignment
Technical Interview
Meet the Team/Hiring Manager
Culture Fit Conversation
Final Offer & Next Steps

Our take

In recent years, creating an accessible and smooth remote working system has become a requisite for many companies. One company focused on this is Doodle, an online scheduling and meeting platform.

Boasting 30M monthly users across 70,000+ businesses, a huge sell is the software can be used for teams of all sizes. This means its features are accessible to small independent businesses through to large enterprises. As a result, the company has placed itself at the forefront of the market (competing with the likes of Calendly).