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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer - **Company:** SimpliSafe, Inc - **Location:** Boston, MA, United States - **Experience:** Experienced - **Salary:** $183,500.0 - $244,600.0 - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Big Data, Cloud Computing Security, Code Review, Continuous Integration, Information Engineering, Data Governance, Distributed Computing Environment, Distributed Systems, Identity and Access Management, Python (Programming Language), Machine Learning, Cloud Services, Tensorflow, Software Engineering, Data Streaming, Unstructured Data, Management of Software Versions, Feature Store, Pytorch, Transfer Learning, Large Language Models, Apache Spark, State Machines, Deep Learning, Cloudformation, Data Lakes, Kubernetes, Infrastructure Automation Frameworks, Apache Kafka, Data Management, Machine Learning Operations, Terraform, Software Version Control, Data Pipelines, Docker - **Published:** October 26, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=e8a0836462cc1de2 ## About the Role * 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 or similar public clouds, including compute, storage, networking, and container orchestration. * Deep experience with CI/CD pipelines, Docker, Kubernetes, and infrastructure-as-code tools (e.g., Terraform, CloudFormation). * Strong proficiency in Python, and hands-on experience with machine learning frameworks (e.g., PyTorch, TensorFlow). * Hands-on experience with distributed training and large-scale data processing (e.g., Ray, Spark, PyTorch Distributed). * Deep understanding of experiment tracking, model registries, and data and model versioning (e.g., MLflow, Weights & Biases, DVC). * Excellent written and verbal communication skills, including writing technical design docs and influencing senior technical and non-technical stakeholders. Nice to Have * Experience handling PII data * Experience with workflow orchestration tools (e.g., Airflow, Kubeflow, Flyte, Step Functions). * Experience with feature stores and data lakehouse technologies (e.g., Feast, SageMaker Feature Store, Delta Lake, Iceberg). * Experience building pipelines for large unstructured datasets such as video, images, or audio, including labeling and curation workflows. * Experience with event-driven or streaming data architectures (e.g., Kafka, Kinesis). * Knowledge of cloud security, IAM, data privacy, and compliance best practices for ML data and workloads. ## Description 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., * Customer Obsessed - Building deep empathy for our customers, putting them at the core of our work, and developing strong, long-term relationships with them. * Aim High - Always challenging ourselves and others to raise the bar. * No Ego - Maintaining a "no job too small" attitude, and an open, inclusive and humble style. * One Team - Taking a highly collaborative approach to achieving success. * Lift As We Climb - Investing in developing others and helping others around us succeed. * Lean & Nimble - Working with agility and efficiency to experiment in an often ambiguous environment.