Staff Machine Learning Engineer

About SimpliSafe We’re a high-tech home security company that’s passionate about protecting the life you’ve built and our mission of keeping Every Home Secure. And we’ve created a culture here that cares just as deeply about the career you’re building. Ours is a no ego culture of collaboration and innovation where those seeking their next challenge can find big opportunities and make a huge impact on the lives of all those who we protect. We don’t just want you to work here. We want you to grow and thrive here. We’re embracing a hybrid work model that enables our teams to split their time between office and home. Hybrid for us means we expect our teams to come together in our state-of-the-art office on two core days, typically Tuesday, Wednesday, or Thursday – working together in person and choosing where they work for the remainder of the week. We all benefit from flexibility and get to use the best of both worlds to get our work done. Why are we hiring? Well, we’re growing and thriving. So, we need smart, talented, and humble people who share our values to join us as we disrupt the home security space and relentlessly pursue our mission of keeping Every Home Secure. About the Role We are looking for a Staff Machine Learning Engineer to serve as the technical leader for our ML data and training platform. In this role, you will set the technical direction for how SimpliSafe prepares data, trains, fine-tunes, and tracks machine learning models, including large language models. You will own the architecture of our data and training infrastructure, drive multi-quarter initiatives across teams, and raise the engineering bar through design reviews, mentorship, and standards. If you want to shape how ML models get built at scale, we'd love to hear from you. Responsibilities: ● Define and own the technical vision, architecture, and multi-quarter roadmap for SimpliSafe's ML data and training platform. ● Architect and lead the delivery of scalable data and feature pipelines that produce reliable, versioned training datasets. ● Build and evolve distributed training infrastructure for classical ML, deep learning, and LLM fine-tuning, including efficient GPU scheduling and utilization. ● Own the experimentation stack, including experiment tracking, model registry, and dataset and model lineage, so that every model is reproducible. ● Develop CI/CD and automation for training pipelines, enabling data scientists to go from idea to trained, validated model quickly and safely. ● Partner with data science, data engineering, product, and security leaders to align the platform with business priorities and turn ambiguous problems into clear, executable plans. ● Set org-wide standards and paved paths for data versioning, training reproducibility, and data governance, and drive their adoption across teams. ● Lead build-vs-buy evaluations and make durable technology decisions on ML tooling, cloud services, and frameworks. ● Drive reliability and cost efficiency of training and data workloads in AWS, including capacity planning and compute and GPU cost optimization. ● Mentor and grow senior and mid-level engineers through design reviews, code reviews, and technical coaching; act as a force multiplier for the team. ● Participate in the team's on-call rotation for platform services the team owns, and lead postmortems for critical incidents. Qualifications ● 8+ years of experience in software engineering, data engineering, or a related field, with at least 4 years focused on ML infrastructure or MLOps. ● Proven track record of technical leadership: designing and shipping ML training platforms, data platforms, or large-scale distributed systems end to end, and leading cross-team initiatives without direct authority. ● Expert-level experience with AWS o