Staff Machine Learning Engineer

Lead the technical direction for an ML data and training platform that scales from classical ML through LLM fine-tuning.

Responsibilities

• Own the technical vision, architecture, and multi-quarter roadmap for SimpliSafe’s ML data and training platform.
• Design and deliver scalable data and feature pipelines that generate 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.
• Run the experimentation stack: experiment tracking, model registry, and dataset/model lineage to ensure reproducibility.
• Create CI/CD and automation for training pipelines so data scientists can move from idea to trained, validated models quickly and safely.
• Collaborate with data science, data engineering, product, and security leaders to convert ambiguous needs into executable plans aligned to business priorities.
• Set and drive org-wide standards for data versioning, training reproducibility, and data governance.
• Perform build-vs-buy evaluations and make durable decisions across ML tooling, cloud services, and frameworks.
• Improve reliability and cost efficiency for training and data workloads on AWS through capacity planning and GPU cost optimization.
• Mentor and grow senior and mid-level engineers via design reviews, code reviews, and technical coaching.
• Participate in the on-call rotation for platform services the team owns and lead postmortems for critical incidents.

Requirements

• 8+ years of experience in software engineering, data engineering, or a related field, including at least 4 years focused on ML infrastructure or MLOps.
• Demonstrated technical leadership: end-to-end delivery of ML training platforms, data platforms, or large-scale distributed systems, plus cross-team initiative leadership without direct authority.
• Expert experience with AWS (or similar public clouds), including compute, storage, networking, and container orchestration.
• Strong foundation in CI/CD, Docker, Kubernetes, and infrastructure-as-code tools such as Terraform and CloudFormation.
• Proficiency in Python and hands-on experience with ML frameworks such as PyTorch and 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/model versioning tools including MLflow, Weights & Biases, and DVC.
• Excellent written and verbal communication, including producing technical design docs and influencing senior technical and non-technical stakeholders.

Technologies

• AWS
• Terraform, CloudFormation
• Python
• PyTorch, TensorFlow
• Ray, Spark
• Docker, Kubernetes
• PyTorch Distributed
• MLflow, Weights & Biases, DVC
• CI/CD, LLM fine-tuning
• GPU scheduling
• Experiment tracking, model registry, data versioning, model versioning
• Infrastructure-as-code

Benefits

• Mission- and values-driven culture with a safe, inclusive environment.
• Comprehensive total rewards package supporting wellness and providing security for SimpliSafers and their families.
• Free SimpliSafe system and professional monitoring for your home.
• Employee Resource Groups (ERGs) for networking, mentorship, development, and advocacy.

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 (video, images, 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.

Location: Boston, MA (hybrid)

Compensation: USD 183,500 - 244,600 per year