AI/ML Engineering Intern

This is a full-time, remote internship role.

As an AI/ML Engineering Intern, you will work on the intelligence layer behind Atomity’s cloud decision platform. Atomity evaluates workloads against different infrastructure options and helps organisations understand where those workloads should run and why.

You will help develop models and decision systems that analyse cloud cost, infrastructure characteristics, workload requirements, performance signals, operational constraints and governance requirements to generate explainable recommendations. The goal is not simply to add an LLM to the product. You will work on the combination of structured data, rules, optimisation techniques, machine learning and AI that makes cloud recommendations reliable and explainable.

• Help build Atomity’s workload recommendation and decision engine.
• Develop methods for classifying workloads based on infrastructure and application characteristics.
• Analyse cloud cost, usage, configuration and operational datasets.
• Build models for cloud cost forecasting and workload demand forecasting.
• Detect unusual spending, resource utilisation and infrastructure behaviour.
• Help identify underutilised and incorrectly sized cloud resources.
• Develop recommendation logic for workload placement across different cloud environments.
• Work on multi-objective optimisation across cost, performance, operational requirements, compliance and sovereignty constraints.
• Build scoring and ranking mechanisms for comparing infrastructure alternatives.
• Help develop confidence scoring and uncertainty handling for recommendations.
• Build explainability mechanisms showing why a recommendation was generated.
• Explore rule-based, statistical, machine-learning and optimisation approaches depending on the problem.
• Use LLMs where they improve classification, reasoning, information extraction or explanation.
• Build evaluation datasets and benchmarks for recommendation quality.
• Measure model accuracy, recommendation consistency and business usefulness.
• Develop feedback loops that allow recommendations to improve from user decisions and observed outcomes.
• Explore retrieval systems for cloud-provider documentation, pricing and regulatory knowledge.
• Help structure unstructured infrastructure and policy information into machine-readable knowledge.
• Support continuous re-evaluation when cloud prices, workload requirements or infrastructure conditions change.

• Python
• Pandas / Polars
• NumPy
• scikit-learn
• PyTorch or equivalent ML frameworks where appropriate
• PostgreSQL
• Vector search and embeddings where appropriate
• REST APIs
• Structured cloud infrastructure data
• Time-series and utilisation data
• Optimisation algorithms
• Forecasting models
• LLM APIs and open-source models
• Retrieval-Augmented Generation where appropriate
• Model evaluation and observability

• Currently pursuing or recently completed a degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics or a related field.
• Strong Python fundamentals.
• Understanding of basic machine-learning concepts.
• Understanding of data preprocessing and feature engineering.
• Familiarity with supervised and unsupervised learning.
• Basic understanding of statistics and probability.
• Ability to work with structured datasets.
• Strong analytical and problem-solving skills.
• Ability to explain technical decisions clearly.
• Interest in cloud computing and infrastructure.
• Willingness to experiment, measure results and challenge assumptions.
• Comfortable working independently in an early-stage environment.

• Experience with forecasting or time-series modelling.
• Experience with recommendation or ranking systems.
• Knowledge of optimisation algorithms or operations research.
• Experience with LLMs, embeddings or retrieval systems.
• Experience evaluating LLM or ML outputs systematically.
• Familiarity with explainable AI techniques.
• Understanding of cloud computing concepts.
• Exposure to AWS, Azure, GCP or other cloud platforms.
• Familiarity with FinOps or cloud-cost datasets.
• Understanding of Kubernetes or distributed systems.
• Experience working with large or heterogeneous datasets.
• Research experience or participation in ML competitions.

• Duration: 3–6 months
• Location: Remote only
• Start date: Flexible

• How AI can support real infrastructure and cloud decisions.
• How multi-objective recommendation systems are designed.
• How deterministic rules can be combined with machine-learning approaches.
• How cloud workloads and infrastructure can be represented as decision variables.
• How to evaluate recommendation quality rather than only model accuracy.
• How infrastructure telemetry can be transformed into actionable recommendations.
• How explainability is incorporated into AI-assisted enterprise decisions.
• Where LLMs are useful and where deterministic systems are more reliable.
• How AI systems are integrated into a production B2B SaaS platform.
• How continuous feedback can improve infrastructure recommendations over time.

• Work on AI applied to a difficult real-world infrastructure problem.
• Help shape Atomity’s recommendation and decision architecture from an early stage.
• Work with real cloud, infrastructure and workload data.
• Build systems whose recommendations must be measurable and explainable.
• Work directly with the founders and engineering team.
• Significant freedom to test different AI, ML and optimisation approaches.
• High ownership from day one.
• Opportunity to grow into a full-time role as Atomity scales.

To apply for the AI/ML Engineering Intern position, please complete and submit the application form on this page. Make sure to include:

• Your resume/CV (PDF, DOC, or DOCX formats, max 10 MB).
• A link to your GitHub profile or relevant AI/ML projects.
• A link to your LinkedIn profile.
• A brief note describing an AI, ML or optimisation problem you have worked on and why you are interested in Atomity.

Technical Issues?
If you experience any issues while submitting the form, please send your application details and attachments directly to career@atomity.de with the subject line “AI/ML Engineering Intern”.