At our company, we move fast. Were an ultra-collaborative company with brilliant people who care deeply about the details. Together, were solving interesting and complex puzzles to keep the worlds data safe.
We work in a flexible, hybrid model, so you can choose the home-office balance that works best for you.
Responsibilities
Design and Build ML Infrastructure: Develop and maintain scalable, production-ready infrastructure for both traditional ML (anomaly detection, user behavior analytics) and LLMs across enterprise environments.
Optimize Model Performance: Analyze and optimize LLM and ML performance using techniques like knowledge distillation, quantization, and efficient data structures to boost efficiency and lower resource costs.
Deploy and Integrate: Collaborate heavily with software and data engineers to integrate models into production pipelines, cloud-native environments, and on-premises workflows.
Drive MLOps & Tooling: Manage the complete model lifecycle (monitoring, retraining, deployment) and actively build custom tools from scratch to improve the team's ML workflows.
Elevate Engineering Standards: Perform rigorous code reviews, ensure robust Python production standards, and provide technical guidance to data scientists and junior engineers.
Cross-Functional Partnership: Partner with cybersecurity researchers and product teams to translate research insights and threat analysis features into highly performant production code.
Open-Source Engagement: Actively engage with the open-source community by contributing code and expertise to relevant ML/LLM projects.
Requirements: Experience: 5+ years of experience in a backend, ML engineering, or MLOps role with a demonstrable track record of successfully deploying and maintaining code in high-volume production environments.
Programming Mastery: Strong proficiency in Python with a deep understanding of software engineering principles, design patterns, and debugging.
Applied ML/LLM Knowledge: Hands-on experience developing and fine-tuning models using frameworks like PyTorch, HF ecosystem and deepspeed, alongside practical experience with LLMs, prompt engineering, and vector databases.
Data & MLOps Infrastructure: Strong experience with Data/MLOps tools (e.g., MLflow, Airflow, DVC) and deployment technologies (CI/CD, Kubernetes, containerization).
Big Data & Cloud: Proficiency with big data platforms (like Databricks or PySpark) and a solid understanding of public cloud platform architectures.
Ownership: Exceptional problem-solving skills with the ability to take full ownership of complex tasks from the design phase through to full production implementation.
Advantages
Prior experience building cybersecurity, data protection, or enterprise threat detection products.
Familiarity with user behavior-based anomaly detection or metadata analytics.
Experience with advanced retrieval-augmented generation (RAG) frameworks.
This position is open to all candidates.