• Deploy and maintain containerized workloads to support machine learning development, deployment, and post-deployment monitoring. • Utilize tools like helm and kustomize to accelerate the deployment of machine learning models and data pipelines. • Apply various optimization techniques such as compilation, quantization-aware-training (QAT), and pruning to improve latency and throughput of models. • Utilize open-source software like Metaflow, Prefect, Temporal, and Argo Workflows to facilitate data science development. • Build and optimize machine learning models to analyze security data, extract actionable insights, and identify trends, anomalies, and other relevant security signals. • Develop and maintain systems for drift detection and model monitoring to ensure continuous improvement and accuracy of insights. • Collaborate with cross-functional teams to design data pipelines that can efficiently process petabytes of raw internet security data.

• Lead BEV model development: Define and execute the technical roadmap for BEV-based perception models across multiple tasks (e.g., detection, segmentation, road topology, and scene understanding). • Design advanced multi-modal architectures that fuse heterogeneous sensor data (camera, LiDAR, radar, HD maps) into unified spatial representations. • Develop foundational perception models leveraging BEV transformers, voxel-based encoders, or implicit scene representations. • Own large-scale training workflows — from data sampling strategies and augmentation pipelines to distributed training and hyperparameter optimization. • Advance model robustness and generalization, addressing long-tail conditions such as low visibility, occlusions, and rare scene configurations. • Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance. • Collaborate cross-functionally with sensor calibration, mapping, and fusion teams to ensure cohesive perception model interfaces. • Mentor and guide ML engineers, cultivating best practices in experimentation, code quality, and model validation. • Stay at the forefront of ML research, exploring self-supervised learning, large-scale pretraining, or foundation models for 3D perception.

• Deliver on technical initiatives that have significant company-wide impact • Set technical direction for the broader Relevance and Feeds teams at Reddit, able to identify opportunities and influence strategy across multiple orgs • Work with management on goal setting, planning, and de-risking critical projects • Mentor and grow Senior and Staff engineers • Create a strong healthy engineering culture

• Design and maintain scalable pipelines for deploying machine learning models whether in-cloud or in-vehicle. • Ensure models are securely integrated into production environments with minimal latency. • Implement monitoring systems to track model performance and flag issues. • Develop methods to evaluate and compare the performance of different models. • Automate processes for validating model accuracy and consistency in production. • Work closely with data scientists, developers, and stakeholders to understand their needs and provide tailored solutions. • Create comprehensive documentation for processes, pipelines, and workflows. • Provide training and guidance to team members on MLOps best practices.