At Lacework, we strive to provide a supportive, collaborative environment where people are empowered to do the best work of their careers.

　　Our team members enjoy solving complex problems, big sky thinking, and obsess over getting the details right. We love what we do and are proud of our work to secure clouds and container environments for thousands of users worldwide.

　　Lacework is a cloud security services provider that automates cloud security at scale so customers can innovate with speed and safety. Lacework was founded on a core belief: better security starts with data. Lacework can collect, analyze, and accurately correlate data across an organization’s AWS, Azure, GCP, and Kubernetes environments, and narrow it down to the handful of security events that matter. We then use this wealth of data to build a behavior-based machine learning engine - Polygraph. The polygraph technology used by Lacework creates a behavioral model of the infrastructure and client services in real time. The hierarchy of processes, containers, pods, and machines are all understood by the model. Then, it creates behavioral models that the polygraph checks for unusual behaviors and anomalous patterns. After that, Lacework generates the proper alerts and warnings and gives the customer a tool to look into and prioritize problems. The Security Efficacy team is the central ML team at Lacework. The goal of the team is to deliver multi-cloud detection coverage that is best in class while also delivering alarms that are precise, timely, and contextualized.

　　To help with this aim, the Security Efficacy team is hiring a Machine Learning Software Engineer. We are seeking applicants with ML/AI experience who are keen to work in the area of large-scale behavior modeling and anomaly detection using graph mining and neural networks.

　　The responsibilities for this role include:

　　Develop an in-depth understanding of the Lacework platform and customer value proposition

　　Understand the competitive product landscape and Lacework differentiation

　　Drive projects/technical initiatives related to ML platform engineering

　　Influence and define delivery timelines in alignment with our field and product teams while balancing speed, accuracy and precision

　　Build instrumentation, observability, and analytics into the machine learning services to support data-driven decisioning and incident response

　　Work with leadership to track key performance, cost, and efficiency metrics as service level objectives (SLOs)

　　Partner with our security efficacy team to mutually enhance our detection quality

　　Build strong cross functional partnerships (ML research, data platform, cloud economics, etc)

　　Demonstrate good communication skills and present work to company leadership and at company-wide events

　　Demonstrate openness to feedback, effectiveness at collaborating with diverse groups of people and resolving conflicts with empathy

　　Actively participate in recruiting and mentor new members of the team

　　Strive to use readily available, general and scalable methodologies and tools; stay current with latest tools and techniques

　　Minimum Qualifications

　　Bachelor’s Degree in quantitative field (e.g. Computer Science, Engineering, Mathematics, Statistics, Operations Research or other related field)

　　8+ years of experience with software development and deployment using modern cloud platforms and developing and debugging distributed systems

　　4+ years of experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining, artificial intelligence, or a related technical field

　　Strong experience developing data-intensive applications

　　Hands-on design and development of Go and/or Java-based microservices

　　Exposure to modern software delivery release models and associated tooling (CI/CD, monitoring, observability)

　　Ability to deal with ambiguity, driving design and implementation to conclusion with limited supervision

　　Track record of setting technical direction for a team, driving consensus, and successful cross-functional partnerships

　　Leading projects or small teams of people to help them unblock, advocating for ML excellence

　　Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)

　　You'll help develop industry-leading solutions that power next-generation, large-scale platforms and AI services to help connect billions of people around the world

　　Preferred Qualifications

　　Advanced degree (Master’s or PhD or equivalent experience) in Computer Science

　　Experience of ML Ops and CI/CD integrations and tools

　　Experience with data processing/ML platform tools (sagemaker, tensorflow, spark, etc.)

　　Familiarity with cloud-based data warehouses (snowflake, redshift, etc.)

　　Familiarity with data streaming solutions (kafka, spark streaming, etc.

　　Experience working in Cloud Security or Infrastructure Security

　　Experience recruiting and mentoring other Engineers

　　Cloud certifications or other demonstrable cloud domain knowledge

　　Experience developing workflows for large scale AI training

　　Salary Range: $137k - $300k USD Annually + Benefits (https://www.lacework.com/careers/) + Bonus + Equity

　　Actual compensation may vary based on factors such as geographic location, work experience, education/training and skill level.

　　Location: Mountain View, CA | Seattle, WA | Ireland | United Kingdom

　　Lacework is an Equal Opportunity Employer. It is the policy of Lacework to provide equal employment opportunity to all persons, regardless of age, race, religion, color, national origin, sex, political affiliations, marital status, non-disqualifying physical or mental disability, age, sexual orientation, membership, or non-membership in an employee organization, or on the basis of personal favoritism or other non-merit factors, except where otherwise provided by law