Machine Learning Consultant (m/w/d) at FRAMEN GmbH

Berlin, Berlin, Germany -

Full Time

Start Date

Immediate

Expiry Date

11 Oct, 26

Salary

4166.0

Posted On

13 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Linear Algebra, Probability, Optimization, Python, NumPy, PyTorch, JAX, Mathematical Modeling, Algorithmic Logic, Statistics, MLOps

Industry

technology;Information and Internet

Description

About the role FRAMEN is one of Europe’s fastest-growing Digital Outdoor Media networks, with 50+ employees working with leading brands and advertising agencies globally. We combine premium indoor environments, strong content context, and technology to deliver high-attention advertising experiences. Our engineering and MLOps team designs and scales the infrastructure behind FRAMEN’s products. To complement their strengths, we are looking for a freelance specialist with deep mathematical and theoretical expertise in machine learning. You will focus on the conceptual and mathematical foundations of our models, while the engineering team takes care of implementation, deployment and infrastructure. This is an initial project term of three months at approximately 20 hours per week, with the option to extend by mutual agreement depending on the scope and progress of the project. Key Responsibilities Develop the mathematical logic and optimization methods behind our machine learning models, providing the conceptual blueprint that the engineering team implements. Assess models in development for structural or mathematical weaknesses and recommend improvements. Lead in-person working sessions that translate complex algorithmic concepts into clear, actionable logic the engineering team can build on. Follow relevant developments in AI research, evaluate their mathematical foundations, and advise how new methods could strengthen our products. Job requirements Your profile A strong theoretical foundation in machine learning, ideally supported by an advanced degree in Mathematics, Statistics, Physics, Computer Science or a related field. Deep knowledge of linear algebra, probability and optimisation. The ability to communicate complex mathematical concepts clearly to engineers and other technical colleagues. Working proficiency in Python (e.g. NumPy, PyTorch, JAX) to prototype and share your ideas. A collaborative approach and enjoyment of solving problems together with the team in person. A collaborative approach and a readiness to work on-site at FRAMEN's Berlin headquarters, where the consultancy project will be delivered and rolled out. What’s in it for you? Compensation of €4,166 per month for the 20 consultancy hours per week. Be part of a well-funded, ambitious start-up with a motivated and fast-growing team, united by the goal of making FRAMEN the most innovative out-of-home advertising company and a market leader across multiple segments. Trust is one of our core values: you’ll be given independence and responsibility from day one. Team Spirit Your contact About us FRAMEN is an intelligent content and DOOH platform that transforms screens in premium locations into dynamic environments. Our media tech enables playback on all standard screens in commercially used spaces. Venue partners can easily manage their screens and content while advertisers use our technology to effortlessly launch and manage targeted campaigns. Our technology delivers relevant advertising, real-time information, and curated content to audiences in coworking spaces, gyms, and hotels, making public media impactful and effortless. FRAMEN is an Equal Opportunity Employer. There shall be no discrimination on the basis of age, disability, sex, race, religion or belief, gender reassignment, marriage/civil partnership, pregnancy/maternity, or sexual orientation.

Responsibilities

Develop the mathematical logic and optimization methods for machine learning models to provide conceptual blueprints for the engineering team. Assess models for structural weaknesses and translate complex algorithmic concepts into actionable logic.