Senior Machine Learning Engineer

Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 200+ public data centers globally and thousands of private sites at the edge, the SASE-based Zero Trust Exchange is the world’s largest in-line cloud security platform. We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler. Role We are looking for a Senior Machine Learning Engineer to join our team. This is a remote (USA) role, reporting to the Manager AI Platform and Data Science in the AI Platform and Data Science department. This team’s mission is high-fidelity risk identification in customer data. Our goals are to catch all threats while minimizing noise. To do this, you will develop solutions applying data analysis and threat-research, while using AI, machine learning, and data engineering to build quality, data-driven components to automate security analysis. What you’ll do (Role Expectations) Translate risk identification methods into agent logic, understanding the benefits and limitations of agents, and how to ensure quality in different types of tasks, including risk analysis, explanations, and recommendations Collaborate closely with threat-research to understand data and threats and their approach to developing security heuristics I

About the Team & Role: We're building SAGE , Rubrik's Semantic AI Governance Engine, which is the first system designed to monitor, govern, and remediate autonomous AI agents in real time. SAGE powers Rubrik Agent Cloud: enterprises define governance policies in natural language, and SAGE's custom small language models act as judges on every agent action. These models are fast enough to sit in the live request path and accurate enough that customers trust them with allow/block decisions on production traffic. At its core, SAGE is "LLM-as-judge" applied to AI governance, utilizing the same technique most teams use for offline evaluation but productionized for real-time enforcement at enterprise scale. Our first-generation SLM Policy Guard already outperforms the larger frontier models we've benchmarked against on accuracy while running approximately 5x faster on the same workload. We're hiring to push that lead even further. As an Applied ML Engineer on the SAGE team, you'll work end-to-end across the model lifecycle: curating data, training small models, serving them at production latency, and closing the feedback loop with real customer signals. The models you build don't just enforce policies in the live request path; they will also drive Agent Rewind, Rubrik's capability to instantly and precisely undo destructive autonomous-agent actions and restore the affected data to a trusted state. We're a collaborative, applied team that ships models to enterprise customers within weeks, and we're passionate about proving that small, specialized models can outperform frontier LLMs at the problems that matter most for AI safety and governance. Nature of the Specialized Duties ➢ Training, Fine-Tuning, and Distilling Production Small Language Models and Classifiers (25% of time) Owning the full training lifecycle for the SLMs and classifiers in SAGE's real-time enforcement path, including base-model selection, supervised fine-tuning, preference optimization (DPO/RLA

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with the world’s largest collection of human conversations. From powering personalized recommendations and search to optimizing advertising systems and marketplace dynamics, our ML engineers tackle some of the most interesting and impactful problems in large-scale applied machine learning. We hire Machine Learning Engineers across both our Consumer and Ads organizations, giving you the opportunity to work on a wide range of high-impact problems across the Reddit ecosystem. We are looking for Machine Learning Engineers who are excited to build systems end-to-end, from research and modeling to production deployment, — and who want to help shape the future of discovery, relevance, and monetization at Reddit. If you love working on complex, real-world ML problems at massive scale, this role is for you. What You’ll Work On As a Machine Learning Engineer at Reddit, you will design and build production ML systems that power core experiences across the platform, including: Personalized recommendations, search, and ranking systems that help users discover the most relevant content and communities Intelligent advertising systems including ranking, bidding, measurement, and optimization Content, Advertisers, and User understanding, from building foundational content/user representations to deriving insightful signals Large-scale machine learning pipelines, model serving infrastructure, and real-time decision systems Applied AI and

As a Perception Engineer, you will be instrumental in designing and implementing cutting-edge detection models, utilizing fused sensor data. In particular, the role will be on advanced projects that are aimed at the next generation of driving software, including looking at joint detection and tracking models as well as ways to send rich embeddings to downstream models to relay semantic meaning and capture uncertainty. This role offers a unique opportunity to directly impact Zoox's expansion into new areas. By developing more generalizable and adaptable models, you will help us navigate diverse and challenging circumstances, accelerating our mission to deliver safe and reliable autonomous mobility. We are looking for a highly motivated individual passionate about pushing the boundaries of perception in the autonomous vehicle space. In this role, you will: Train ML models, perform various studies into incorporating SOTA approaches as well as ablations/modifications of existing models Document experimental outcomes and drive towards deploying these models into Zoox robots Visualize your data at the input and output to understand the issues Collaborate with teams such as ML optimization, data labeling, metrics and planner to ensure project is successful for Zoox releases Qualifications MS/PhD in Computer Science or related field Experience with PyTorch or related ML frameworks Experience deploying learned models into production Excellent collaboration and teamwork skills with proven track record of working on large-scale problems Bonus Qualifications Prior experience in the AV or related space with joint detection and tracking models Deep understanding of how to setup end-to-end training and evaluation processes About Zoox Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting a

The Perception team at Zoox is responsible for the robot’s understanding of the world, fusing data from Lidar, Radar, and Cameras to create a unified representation of the environment. In this role, you will contribute to the development of our next-generation 3D occupancy and segmentation networks. You will architect and optimize high-performance deep learning models that generate dense, temporally consistent voxel representations of the driving environment. This work is critical for enabling our vehicle to navigate complex urban scenarios, handle rare obstacles, and drive safely in tight spaces by providing precise geometry and motion estimates to downstream planners. In this role, you will... Design and implement state-of-the-art multi-modal sensor fusion architectures (Lidar, Camera, Radar) to predict 3D occupancy, semantic segmentation, and flow . Develop "vision-first" fusion strategies to enhance geometric understanding and reduce dependency on sparse sensor modalities . Engineer temporal processing modules to improve the stability and consistency of predictions over time. Optimize model architectures for real-time on-vehicle inference, balancing high-fidelity range extension with strict latency constraints . Collaborate with downstream consumers (Tracking, Prediction, Planner) to refine geometric outputs, such as contours and free-space estimations, for complex maneuvering. Qualifications MS or PhD in Computer Science, Robotics, Machine Learning, or related field with 6+ years of industry experience. Deep expertise in 3D Computer Vision and Deep Learning, specifically with voxel-based or BEV (Bird's Eye View) architectures. Strong proficiency in Python and deep learning frameworks (PyTorch) for model training and design as well as some experience in C++ for model integration. Experienc

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. As a Machine Learning Engineer on the Foundation AI organization, you will sit at the epicenter of our foundation model efforts. While the research world is focused on architecture, you will be the architect of the data flywheel that makes 3DGen possible. You aren't just building pipelines; you are building the infrastructure that defines how our models perceive and generate virtual worlds in three dimensions and across time. In this role, you will partner directly with our AI researchers to advance beyond experimental datasets and into the realm of dynamic, high-fidelity 3D data synthesis and evaluation. You will bridge the gap between research prototypes working locally to scaling for millions of users. You will design, implement, and scale robust, high-performance infrastructure to crawl, create, curate, store, and serve the massive 3D datasets required for these models. We are seeking accomplished software engineers with a passion for data, experience building large distributed systems, and a commitment to writing high-quality, well-tested code to solve complex data challenges at scale. Your contributions will ensure that our foundation models receive the highest quality data, thereby s