Staff Machine Learning Engineer - AI Platform
- Analytics Team: composed of two Analytics Engineers, a Data Analyst, and an Analytics Manager.
- Machine Learning Team: composed of one Machine Learning Engineer.
- Define and evolve our infrastructure to allow for better ML and AI capabilities, with a focus on LLM-based and agentic systems.
- Contribute to the development and expansion of our agentic AI framework powered by AWS Bedrock, enabling both internal tools and customer-facing features.
- Identify, source, and refine datasets to allow tuning models, powering retrieval pipelines, or expanding agentic workflows.
- Pre-process data by using techniques such as data cleaning, feature engineering, and transformation.
- Train, evaluate, and deploy both LLM-based systems and traditional machine learning models into production.
- Monitor, debug, and continuously improve deployed models and AI tools.
- Support machine learning usage throughout the company, including selecting the right modeling approach for the use case (LLM vs. traditional ML).
- Support the integration and use of LLMs, including approaches such as fine-tuning, prompt tuning, and retrieval-augmented generation (RAG), to improve accuracy.
- You have an analytical mindset.
- You strive to understand business challenges and leverage ML and LLMs to solve them.
- You stay calm in the face of challenges and have an infectious, can-do attitude to solving problems.
- You have an aptitude for digesting complex ML or AI concepts and effectively communicating them to both technical and non-technical audiences.
- You are a team player with high integrity - you can remain flexible as we grow.
- You are autonomous and rigorous.
- You're resourceful - you might not have all the answers, but you are ready to find them.
- 5+ years of experience as a Data Scientist or Machine Learning Engineer.
- Experience working with LLMs (e.g., prompt engineering, fine-tuning, retrieval-augmented generation).
- Experience working with Agents for Amazon Bedrock or similar agent orchestration setups.
- Strong understanding of machine learning algorithms, statistical methods, and data preprocessing techniques.
- Experience with cloud platforms for model training and deployment, especially AWS.
- Proficiency in Python, including experience with libraries such as Scikit-Learn, NumPy, Pandas, and PyTorch/TensorFlow.
- Proficiency in SQL and experience working with data warehouses (e.g., Snowflake, GCP).
- Knowledge of MLOps best practices, including CI/CD pipelines, model monitoring, and versioning (e.g., MLflow, Airflow).
- Experience deploying models to production and supporting them post-deployment.
- Fluency in English.
- Experienced in a SaaS B2B business.
- Experienced within a product-led growth company.
- Contributions to open-source projects related to ML or AI.
- Fully remote working environment.
- Freedom to make an impact at a small, self-funded, and profitable tech startup by laying the foundation for machine learning and AI.
- Collaborative and open-minded culture based on rationality, humility, honesty, and long-term thinking.
- Benefits and perks are described below.
- Screening with our Talent Acquisition Partner, Jakub (45 min).
- Job Fit interview with our co-CTO, Nicolas (60 min) + a Data Team Member.
- Remote exercise.
- Exercise debriefing with two Data Team Members (60 min).
- Cultural fit interview with two Colleagues from other departments (60 min).
- Draft and refine job descriptions and case studies,
- Draft emails during the process,
- Find interview timeslots,
- Summarize interview notes,
- Assess your CV or profile,
- Evaluate interview performance,
- Conduct interviews,
- Grade technical tasks or case studies.
- Research our company, team, or product,
- Refine your CV, portfolio, or LinkedIn profile,
- Prepare for interviews and brainstorm potential questions,
- Polish your case study or presentation,
- Draft emails to us,
- Search for answers during interviews (unless we ask),
- Create documents (CV, portfolio, presentation) from scratch without your input,
- Build case studies or technical tests without your personal touch.
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