This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are looking for a Principal Machine Learning Engineer, a senior technical visionary, to be the Principal Technical Lead for the Growth Engineering team, responsible for setting up overall technical strategy, unified technical architecture and defining a roadmap for industry leading methodology for user and engagement growth. Strong hands-on machine learning background including deep learning architectures, generative AI, and large scale deployment and measurement of ML systems is required. As the Principal Machine Learning Engineer you'll be responsible for the technical direction, strategy and health of our Growth Engineering org. You'll ensure that our technology can deliver on the business/product requirements necessary to keep Pinterest as an engaging platform for everyone. This means working with other leads to set and execute a long-term strategy for the overall engagement growth of Pinterest, aligning the strategy with other internal partners where it makes sense and communicating to leadership our current status and path to having world-class Growth capabilities. You'll also foster a healthy community where all ML engineers can learn best practices, collaborate effectively and understand our technical direction. What you’ll do: - Develop strong partnerships with product teams to understand and proactively address future technology needs and current developer pain points. - Champion and drive large-scale, cross-functional initiatives that grow user visitation and engagement depth of our platform. - Act as the ultimate "advocate" for engineers on Growth including representing needs to leadership and prioritizing projects on the platform teams that ensure high quality capabilities and a world-class Pinner experience. - Scale your leadership through both direct mentorship and via best practices, processes, training and tools. - Ensure solid technical plans are in place for projects within Growth via direct review or delegation. - Be the technical point of contact for decisions that impact the whole Pinterest platform via the Growth initiatives and for cross-functional partners for an 125+ member org. Qualifications - Deep expertise building large scale ML systems at scale with modern frameworks. - Knowledge of (and a passion for) building responsible and quality first discovery surfaces to drive user visitations. - Track record of innovating and delivering large, cross-functional projects across multiple organizations. - Strong written and verbal communication skills and proven ability to collaborate cross-functionally. - Degree in Computer Science, Machine Learning, Statistics or related field. - 10+ years of professional experience as a hands-on engineer and technical leader leading multiple projects. Requirements - This role will need to be in the office for in-person collaboration 1-2 times every 6-months and therefore can be situated anywhere in the country. - This position is not eligible for relocation assistance. Benefits - Visit our PinFlex page to learn more about our working model.

• Design, build, and maintain large-scale multilingual datasets across high- and low-resource languages • Develop data pipelines for collection, cleaning, normalization, deduplication, and labeling • Implement quality filters using statistical, heuristic, and model-based methods • Work with researchers to define language coverage, benchmarks, and evaluation metrics • Analyze dataset bias, coverage gaps, and failure modes across regions and scripts • Support training, fine-tuning, and distillation workflows with high-quality multilingual data • Continuously iterate on datasets based on model performance and real-world usage

• Optimize large-scale model training pipelines (throughput, convergence, stability, and cost) • Improve distributed training strategies (data, model, and pipeline parallelism) • Tune optimizers, schedulers, batch sizing, and precision (bf16 / fp16 / fp8) • Reduce training time and compute cost via profiling, bottleneck analysis, and systems-level improvements • Collaborate with researchers on architecture-aware training strategies • Build and maintain robust training infrastructure (checkpointing, fault tolerance, reproducibility) • Evaluate and integrate new training techniques (e.g. gradient checkpointing, ZeRO, FSDP, custom kernels) • Own training performance metrics and continuously push them forward

• Optimize inference latency, throughput, and cost for large-scale ML models in production • Profile and bottleneck GPU/CPU inference pipelines (memory, kernels, batching, IO) • Implement and tune techniques such as: • Quantization (fp16, bf16, int8, fp8) • KV-cache optimization & reuse • Speculative decoding, batching, and streaming • Model pruning or architectural simplifications for inference • Collaborate with research engineers to productionize new model architectures • Build and maintain inference-serving systems (e.g. Triton, custom runtimes, or bespoke stacks) • Benchmark performance across hardware (NVIDIA / AMD GPUs, CPUs) and cloud setups • Improve system reliability, observability, and cost efficiency under real workloads