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Engineering Manager, Data Science
В офисеПолная занятостьEngineering ManagementCybersecurityEnterprise SoftwareAI Software
Lead and grow a multidisciplinary R&D team of ML engineers, data scientists, and software engineers developing AI safety, moderation, real-time inference, data platform, and MLOps systems.
Обязанности
- Lead, mentor, hire, onboard, and develop ML engineers, data scientists, and software engineers.
- Set technical direction and engineering standards across real-time inference, GenAI safety, data platforms, and MLOps.
- Translate company and product goals into a prioritized roadmap and deliver across parallel workstreams.
- Own system quality, reliability, delivery, cost, latency, testing, observability, CI/CD, and on-call practices.
- Review designs, unblock engineers, guide architecture decisions, and partner with Product, Platform, and GenAI safety stakeholders.
- Keep the team current with advances in machine learning, LLMs, and GenAI safety.
Требования
- Proven experience managing engineering or data science teams, or strong technical leadership experience transitioning into formal management.
- Strong production Python and software engineering experience with ML and data science foundations, including training, evaluation, deployment, and monitoring.
- Experience operating services at scale on AWS and Kubernetes and working with Spark or PySpark and lakehouse platforms such as Databricks.
- Daily, hands-on fluency with an AI coding assistant such as Claude Code, Cursor, or Codex, together with sound judgment about team adoption.
- Excellent communication and stakeholder-management skills.
Будет плюсом
- Production experience with LLMs, GenAI, prompting, evaluation, guardrails, red-teaming, or agentic systems.
- Experience with NVIDIA Triton or comparable model-serving systems and MLflow or Unity Catalog.
- Familiarity with Rust and Python/Rust interoperability using PyO3 or maturin.
- Experience in GenAI safety, security, content moderation, or other high-stakes model-quality domains.
- Knowledge of event-driven architectures such as Kafka or SQS and Infrastructure-as-Code with Terraform.