15 beste Stellen als phd engineering in berlin, be – aktuell auf Personalsuche

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NVIDIA —Berlin
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- Master
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Boston Consulting Group —Berlin
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Auticon —Berlin
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Realtime Robotics —Berlin
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Senior Applied Scientist, Amazon Robotics
Amazon Development Center Germany GmbH —Berlin
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Databricks —Berlin
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Principal Product Manager - Pricing Product (all genders)
Zalando —Berlin
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Flix —Berlin
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Flix SE —Berlin
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Moody's —Berlin
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Engineer II-Device
Microchip Technology —Teltow
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Junini GmbH —Berlin
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System Engineer for Passenger Information Systems (f/m/d)
ALSTOM —Hennigsdorf
- Hybrides Arbeiten
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- Master
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Senior AI/ML Engineer, Security Log Intelligence

RedMimicry GmbH
Berlin

Direkt bewerben

Details zur Stelle

85.000 € – 94.000 € pro Jahr
vor 12 Stunden

Leistungen

Homeoffice-Möglichkeit
Kostenfreies oder vergünstigtes Deutschland-Ticket

Qualifikationen

Doktorat
Master

Vollständige Stellenbeschreibung

We are building a new capability that turns fragmented, noisy security logs into explainable, AI-powered threat analysis, delivered inside the RedMimicry platform.

As Senior AI/ML Engineer at RedMimicry, you will lead the applied AI/ML work behind new analysis capabilities for our breach and attack emulation platform. The core challenge is extracting useful structure from heterogeneous, partially unstructured security telemetry and relating it to known attacker activity.

The problem is broader than prompt engineering. You will determine where LLMs, embeddings, retrieval, learned ranking, and deterministic heuristics are justified. The standard is measurable improvement against reproducible baselines, not architectural fashion. Everything you build must operate under realistic latency, reliability, and deployment constraints.

This is a fixed-term position running until 31 October 2027.

Tasks

Develop Security-Log Parsing Methods: Design and implement methods for extracting typed events from heterogeneous SIEM, EDR, NDR, operating-system, and network telemetry.
Design Embeddings and Retrieval: Select, evaluate, and tune representations and retrieval methods for security events.
Handle Ambiguity Explicitly: Implement confidence scoring, calibration, and controlled treatment of ambiguous evidence.
Ground Results in Evidence: Ensure that results are supported by traceable evidence from the original telemetry.
Build Rigorous Evaluations: Define datasets, baselines, ablations, and metrics, and analyse failure modes systematically.
Optimise Inference: Make the pipeline practical for cloud operation and on-premises deployment.
Productise the Research: Work with backend, integration, and offensive-security engineers to turn experimental methods into maintainable services.
Document the Work: Produce clear experiment records, architecture decisions, and technical reports.
Contribute to Academic Research: Contribute, at minimum as a co-author, to an academic research paper published in the context of the project.

Requirements

You do not need to meet every requirement to apply. We care more about demonstrated depth, sound experimental judgement, and the ability to ship reliable systems than about a specific academic title.

Machine Learning and LLM Systems
Strong Python programming skills
Practical experience with PyTorch or a comparable framework
Experience with open-weight language models, structured outputs, embeddings, or retrieval systems
Experience with fine-tuning, PEFT, quantisation, model serving, or inference optimisation
Understanding of hallucination, calibration, distribution shift, and model failure analysis
Information Retrieval and Evaluation
Semantic retrieval, ranking, classification, or information extraction
Approximate nearest-neighbour search and vector indices
Evaluation using metrics such as Recall@K, MRR, F1, exact match, calibration, and ablation studies
Dataset construction, partitioning, and reproducible benchmarking
Software Engineering
Ability to turn experimental code into maintainable production components
Testing, profiling, observability, and performance analysis
Experience working with APIs, distributed services, and containerised environments
Cybersecurity Knowledge
Security logs, SIEM, EDR, NDR, detection engineering, incident response, or threat hunting are strong advantages
Understanding of endpoint, process, identity, and network telemetry is a plus
Research Background
MSc, PhD, or equivalent practical research experience in computer science, machine learning, data science, mathematics, or a related field
Ability to read, reproduce, and critically evaluate current research
Languages
English (required)
German (a plus)

Benefits

Work from anywhere in Germany, and use our Berlin office as often as you like
30 days of paid time off, and a quiet inbox while you are away
Company-paid Deutschlandticket
Annual budget for the courses and certifications you pick yourself
Modern tooling and extensive use of AI
Light process, clear communication, focus on what really matters

A close match is enough. If the role speaks to you, apply with your CV and anything else you would like us to see. What follows is short and transparent, a few conversations with the team and then your first week in Berlin. We are an equal opportunity employer and welcome applications from all backgrounds and genders. Questions about the role or the process are welcome at any point.

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