Machine Learning Engineer, Doodle

AI Systems

Salary not provided

Python

Java

Kafka

Junior, Mid and Senior 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

Java

Kafka

Junior, Mid and Senior 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

You're more than a model-builder; you're a problem-solver, an innovator, and a continuous learner who thrives on turning data into impact
You're a practical AI builder who thinks in terms of production-readiness and reliability
You're comfortable writing production code, reviewing PRs, and delivering a reliable, explainable, and production-ready product
You're excited about MLOps and have experience implementing MLOps best practices
You are proficient in Python and have strong experience with Java or Go
Experience with Kafka and deploying models in a production environment is a major plus
You're familiar with vector stores, agent orchestration, and the challenges of deploying LLM-powered features
You are not afraid to think outside the box and use the right neural network architecture for a given task, not just LLMs

What the job involves

We believe in crafting elegant solutions to intricate problems, and we're looking for passionate Machine Learning Engineers who share this drive
You'll shape and optimize the intelligence behind our scheduling platform—designing, training, and deploying models that understand human behavior, adapt to complex constraints, and deliver seamless user experiences
Design, build, and deploy production-ready LLM-powered systems that support multi-step task automation, tool use, and context-aware reasoning within our product
Implement MLOps pipelines with best practices in mind, focusing on automation, monitoring, and continuous integration/continuous delivery (CI/CD)
Contribute to our core backend services built with Java and Go, ensuring seamless integration of AI models
Ensure the performance and reliability of models over time by implementing robust monitoring solutions
Collaborate closely with Data Scientists and Principal Engineers to translate model prototypes and research into scalable, shippable products
Collaborate on our AI strategy and roadmap, identifying and implementing the best-suited neural network architectures—whether they are LLMs or other models—for a given task
Bridge the gap between model development and product delivery by embedding AI capabilities into real user workflows, ensuring the technical architecture supports meaningful product outcomes

Application process

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

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).