Lookout, Inc. is a globally recognized cybersecurity leader delivering advanced protection for the most vulnerable element of any enterprise security strategy — human error and manipulation. Cloud-native by design, the Lookout platform offers rapid, scalable deployment and simplified security operations, defending the frontline of human-centric attacks—the mobile device. Attackers now target the human element more than ever, with mobile devices providing the most direct path to their victims. Using social engineering techniques that exploit basic human instincts like trust, curiosity, and urgency, they deceive users into revealing sensitive credentials, allowing them to slip past legacy security solutions.
Lookout Endpoint Detection and Response (EDR) continuously monitors mobile endpoints for signs of human-centric attacks, as well as traditional malware, software vulnerabilities, and other anomalous activity. It uses advanced threat detection techniques, including artificial intelligence (AI) and behavioral analysis, to identify threats before they escalate across the enterprise. Learn more at www.lookout.com and follow us on the Lookout Blog, LinkedIn, and X.
Lookout's backend system supports a massive volume of data and an increasingly high level of demand, from data ingestion to delivery to dynamic analysis. We're looking for talented and motivated engineers to build core components and services, as well as to contribute to evolving the architecture. We're a small team working on a very large, modern system with a user base of millions, so you'll get to work on a cutting-edge product and service on a large scale. We're responsible for building and maintaining several of the services powering the various Lookout applications..
- You will be part of a dynamic and technically diverse group of engineers, where you will get to contribute, influence, learn and grow top notch technical skill sets, while building key features for our customers.
- Own the full machine learning lifecycle, from initial research and design to production deployment, monitoring, and continuous retraining.
- Establish and implement robust frameworks to train, evaluate, stress-test, and monitor ML/AI systems, driving continuous improvement and preventing model drift.
- Address and navigate real-world scale and performance challenges, ensuring all ML/AI models and systems are efficient, robust, and maintain target quality/latency under production constraints.
- Set technical direction as a senior Individual Contributor (IC) and mentor junior engineers.
- Quality is the forefront of all we do, so you will assist with improving all forms of automated testing (unit, integration, functional etc).
- We believe in, and practice, end to end service ownership, so you will fully participate in the ownership of your services and components, including on-call duties.
- BS/MS in Computer Science or related field/degree with at least 5-8 years of work experience.
- A proven track record of architecting and deploying practical, high-impact ML/AI systems over multiple years, translating complex models into robust, real-world solutions.
- Experience working cross-functionally with data engineers, platform engineers, and product stakeholders to bring ML/AI systems to life.
- Expert proficiency in Python and strong software engineering skills, including the ability to write reliable, production-grade code. In-depth experience with major ML/Deep Learning frameworks: PyTorch, TensorFlow, scikit-learn.
- Familiarity with modern AI/ML stacks like Hugging Face and LangChain.
- Deep expertise in a wide range of standard ML algorithms, including classification, clustering, regression, and deep learning models.
- Hands-on experience with the LLM/RAG stack components: embeddings, knowledge graphs, vector databases, retrieval strategies, and prompt engineering.
- Proven ability to measure and optimize model efficacy using statistical methods.
- Hands-on experience with production ML infrastructure, including feature stores, model management, training/serving pipelines (MLOps), and monitoring tools.
- Up-to-date with new advances in NLP, LLMs and agent-frameworks.
- Strong grasp of software engineering fundamentals and algorithms.
- Experience with implementing secure, scalable API services.
- Practical experience applying advanced techniques to enhance model efficiency, including model distillation (creating smaller, faster student models) and model quantization (reducing memory footprint and latency).
- Experience with efficient Large Language Model (LLM) fine-tuning methods like LoRA and other PEFT techniques.
- Experience working with coding agents (Cursor, Copilot, Claude Code etc..)
- Experience with event streaming frameworks, technologies, patterns and architectures (particularly Kafka or similar)
- Professional programming experience with Scala and Golang
- Experience with serverless architectures on AWS, GCP.
- Experience with Kubernetes, container technology (Docker, ECS, EKS).
