Remote Dev Jobs
4 jobs available
RedMimicry GmbH · 🇩🇪 Berlin, GermanyRemote57y ago
We are building a new capability that turns fragmented, noisy security logs into explainable, AI-powered threat analysis, delivered inside the RedMimicry platform. As Platform and Integration Engineer at RedMimicry, you will connect our security-analysis services to real security telemetry and make them deployable in cloud, on-premises, and restricted customer environments. You will work with SIEM, EDR, NDR, operating-system, and network exports; containerised services; GPU-backed workloads; and enterprise environments with strict security and data-residency requirements. This is not a pure DevOps position. You must understand the structure and failure modes of security telemetry and build reliable interfaces between customer systems, our analysis services, and the existing RedMimicry platform. This is a 32h/week part time job. This is a fixed-term position running until 31 October 2027. Tasks Build Security-Data Ingestion: Design and implement import paths for SIEM, EDR, NDR, operating-system, and related security telemetry. Normalise Critical Metadata: Standardise timestamps and expose relevant host, user, process, and network identities without destroying source provenance. Build Reproducible Test Environments: Maintain fixtures and environments for ingestion and regression testing. Package and Operate the Services: Containerise our services and implement deployment automation, monitoring, logging, and health checks. Support On-Premises and Restricted Environments: Develop reliable installation and upgrade processes for customer-operated environments. Automate Validation and Recovery: Build environment checks, smoke tests, backup and restore workflows, and diagnostic tooling. Support Customers: Prepare, install, troubleshoot, and validate customer environments. Document Operations: Maintain deployment guides, runbooks, and known limitations. Requirements You do not need to meet every requirement to apply. We are looking for strong systems judgement and the abilit
RedMimicry GmbH · 🇩🇪 Berlin, GermanyRemote57y ago
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 publis
RedMimicry GmbH · 🇩🇪 Berlin, GermanyRemote57y ago
We are building a new capability that turns fragmented, noisy security logs into explainable, AI-powered threat analysis, delivered inside the RedMimicry platform. As an AI/ML Engineer at RedMimicry, you will build the datasets, benchmarks, and evaluation infrastructure behind our applied AI/ML work on security telemetry. The quality of this work depends on accurate ground truth, disciplined data handling, reproducible experiments, and systematic error analysis. Your work will determine whether reported improvements are real and whether regressions are caught before deployment. You will work closely with the Senior AI/ML Engineer and Offensive Security Engineer. This role is suitable for an early-career or intermediate engineer with strong programming and data skills who wants to work on applied AI in a technically demanding cybersecurity environment. This is a fixed-term position running until 31 October 2027. Tasks Curate Security Datasets: Prepare telemetry from controlled engagements and test environments. Build Ground Truth: Create and maintain labelled examples, annotation guidelines, and consistency checks. Maintain Evaluation Partitions: Separate training, validation, and test data along relevant dimensions. Automate Benchmarks: Implement metrics and maintain reproducible benchmark and regression pipelines that run locally and in CI. Run Experiments and Error Analysis: Evaluate models and methods, and identify recurring failure modes and data-quality issues. Maintain Data Quality: Implement schema checks, provenance tracking, deduplication, validation, and dataset versioning. Document Experiments: Produce clear experiment records, plots, tables, and technical summaries that support engineering decisions. Requirements You do not need to meet every requirement to apply. Strong practical work, research projects, open-source contributions, or a relevant thesis can compensate for limited commercial experience. Programming and Data Work Good Python programming ski
RedMimicry GmbH · 🇩🇪 Berlin, GermanyRemote57y ago
We are building a new capability that turns fragmented, noisy security logs into explainable, AI-powered threat analysis, delivered inside the RedMimicry platform. As a Full Stack Engineer at RedMimicry, you will take a leading role in developing a new security analytics capability within our platform. You will build the user interface and the application layer that connects the underlying components into a reliable workflow for security analysts. This is a fixed-term position running until 31 October 2027. Tasks Build Backend Services and APIs: Implement services and stable interfaces for ingestion, analysis, and reporting workflows. Design Domain Models: Define maintainable data models for security events, analysis results, and supporting evidence. Build Analyst Workflows: Develop complex, data-heavy interfaces that let analysts review results efficiently. Make Evidence Inspectable: Allow analysts to drill down from a conclusion to the exact source fields and log fragments behind it. Represent Uncertainty: Present confidence, ambiguity, and alternatives clearly rather than reducing complex results to a binary status. Implement Reporting: Build consistent JSON and PDF reporting pipelines for machine-readable and human-readable outputs. Handle Long-Running Jobs: Implement asynchronous processing, progress reporting, retries, cancellation, and useful failure states. Build Secure Multi-Tenant Components: Apply authentication, authorisation, tenant separation, and secure data-handling patterns. Maintain Engineering Quality: Add tests, review code, document interfaces, monitor performance, and translate analyst feedback into concrete improvements. Requirements You do not need to meet every requirement to apply. We are looking for strong product and systems engineering judgement, not a checklist match. Backend Engineering Strong experience with Go or another statically typed backend language API design, versioning, validation, and error handling Relational data modelling