Staff Machine Learning Engineer

SimpliSafe • Boston, MA

1d • Hybrid

About The Position

SimpliSafe is seeking a Staff Machine Learning Engineer to serve as the technical leader for its ML data and training platform. The role sets the technical direction for preparing data, training and fine-tuning models—including large language models—and tracking model development. The engineer owns the architecture of data and training infrastructure, drives multi-quarter initiatives across teams, and raises engineering standards through design reviews, mentorship, and technical guidance.

The role partners with data science, data engineering, product, and security leaders to align the platform with business priorities. It includes building scalable data and feature pipelines, distributed training infrastructure, and an experimentation stack that supports reproducibility. The engineer also drives automation, reliability, cost efficiency, and shared standards for ML platform work, and participates in on-call support for the platform services the team owns.

SimpliSafe uses a hybrid work model: teams are expected in the office on two core days, typically Tuesday, Wednesday, or Thursday, and can choose where to work for the rest of the week.

Requirements

• At least 8 years of experience in software engineering, data engineering, or a related field, including at least 4 years focused on ML infrastructure or MLOps.
• A proven 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 such as Terraform or CloudFormation.
• Strong proficiency in Python and hands-on experience with machine learning frameworks such as PyTorch or TensorFlow.
• Hands-on experience with distributed training and large-scale data processing, such as Ray, Spark, or PyTorch Distributed.
• Deep understanding of experiment tracking, model registries, and data and model versioning, such as MLflow, Weights & Biases, or DVC.
• Excellent written and verbal communication skills, including writing technical design documents and influencing senior technical and non-technical stakeholders.

Nice To Haves

• Experience handling PII data.
• Experience with workflow orchestration tools such as Airflow, Kubeflow, Flyte, or Step Functions.
• Experience with feature stores and data lakehouse technologies such as Feast, SageMaker Feature Store, Delta Lake, or 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 such as Kafka or Kinesis.
• Knowledge of cloud security, IAM, data privacy, and compliance best practices for ML data and workloads.

Responsibilities

• Define and own the technical vision, architecture, and multi-quarter roadmap for SimpliSafe's ML data and training platform.
• Architect and lead 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 every model is reproducible.
• Develop CI/CD and automation for training pipelines so data scientists can move 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 executable plans.
• Set organization-wide standards and paved paths for data versioning, training reproducibility, and data governance, and drive adoption across teams.
• Lead build-versus-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 develop senior and mid-level engineers through design reviews, code reviews, and technical coaching.
• Participate in the team's on-call rotation for platform services the team owns and lead postmortems for critical incidents.

Benefits

• Comprehensive total rewards package supporting wellness and providing security for employees and their families.
• Free SimpliSafe system and professional monitoring for the employee's home.
• Employee Resource Groups that provide opportunities to network, mentor, develop, and advocate for change.
• The total rewards package may include participation in the annual bonus program, equity, and other forms of compensation, in addition to medical, retirement, and lifestyle benefits.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed