Founded in Berlin, autarc is a 30 person start-up who have just graduated from the Y Combinator in San Francisco. Our team consists of people in Sales, Tech and Operations, with the Engineering department currently 11 people and looking to grow to 14 by Q3 2025.
Role description
You will be working alongside our Senior Machine Learning Engineer and AI-focused Fullstack Engineer to help support our deep tech products and features. You will help integrate natural language processing and large language models into our product. You'll work on analysing written data and implementing generative text capabilities.
The role will include working with Computer Vision to a certain degree, although there will be no interaction with LiDAR, since these responsibilities lie with our Senior ML Engineer.
Tasks will generally include:
Design, develop, and optimize large language models for specific use cases
Fine-tune and customize pre-trained LLMs to improve performance on targeted tasks
Implement efficient inference pipelines and model serving infrastructure
Work with product teams to translate business requirements into technical specifications
Working alongside our Fullstack Engineer to provide them with the tools to build accessible UI
Stay current with the latest research advancements in LLM technology
The role is an 80/20 split, between Machine Learning (NLP, LLM) and Computer Vision.
Requirements
Bachelor's degree in Computer Science, Machine Learning, or related field
2+ years of hands-on Machine Learning experience
Strong programming skills in Python and experience with ML frameworks like PyTorch or TensorFlow
Experience with transformer-based models and natural language processing
Knowledge of prompt engineering techniques and LLM optimization strategies
Familiarity with distributed computing and ML infrastructure
Experience with ML deployment platforms like HuggingFace or MLflow
Based in Berlin with a willingness to work hybrid (office in Mitte)
Fluent in English (German is a plus)
What we offer
Freedom to choose your own equipment (within a budget)
26 paid vacation days
An annual team retreat
The choice between Dance or a public transport ticket for mobility
Urban Sports Club for fitness
Regular team events, lunches and activities
Next steps
Screening call with Louis (Recruiter)
Culture fit call with Bharath (Senior ML Engineer)
Take home coding challenge (approx. 2-4 hours)
Final on-site interview with Marius (CTO) and the team