Staff Machine Learning Engineer
SimpliSafe · Boston, MA
About this role
**About SimpliSafe** We’re a high-tech home security company passionate about protecting the life you’ve built and our mission of keeping Every Home Secure. We offer a no-ego culture of collaboration and innovation—where you can grow, thrive, and make a huge impact. **Hybrid work model** We embrace a hybrid model: come together in our state-of-the-art office on two core days (typically Tuesday, Wednesday, or Thursday). The rest of the week is flexible between office and home. **Why are we hiring?** We’re growing and thriving, and we’re looking for smart, talented, and humble people who share our values to help disrupt the home security space. --- **About the Role — Staff Machine Learning Engineer** You’ll serve as the technical leader for SimpliSafe’s ML data and training platform. You’ll set the technical direction for how we prepare data, train, fine-tune, and track machine learning models—including large language models. You’ll 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. --- **Responsibilities** - Define and own the technical vision, architecture, and multi-quarter roadmap for SimpliSafe’s ML data and training platform. - Architect and lead 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 (experiment tracking, model registry, dataset/model lineage) to ensure every model is reproducible. - Develop CI/CD and automation for training pipelines so data scientists can move from idea → 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 executable plans. - Set org-wide standards and paved paths for data versioning, training reproducibility, and data governance—and drive adoption across teams. - Lead build-vs-buy evaluations to make durable technology decisions on ML tooling, cloud services, and frameworks. - Drive reliability and cost efficiency of training and data workloads.
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