Software Engineer - Machine Learning Platform

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 de