SimpliSafe is seeking a Staff Machine Learning Engineer to set the technical direction for the ML data and training platform. This role focuses on building reliable pipelines and distributed training infrastructure, including support for LLM fine-tuning, while improving reliability, reproducibility, and cost efficiency.
Role Overview
You will define and own the technical vision, architecture, and multi-quarter roadmap for SimpliSafe’s ML data and training platform. The position includes end-to-end responsibility for scalable data preparation, versioned datasets, experimentation and lineage tracking, and engineering standards that enable reproducible model development.
Key Responsibilities
- Define and own the technical vision, architecture, and multi-quarter roadmap for the 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, including experiment tracking, model registry, and dataset and model lineage to ensure reproducibility.
- Develop CI/CD and automation for training pipelines so data scientists can move from idea to trained and validated models quickly and safely.
- Partner with data science, data engineering, product, and security leaders to align the platform with business priorities and convert 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 to 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 senior and mid-level engineers through design reviews, code reviews, and technical coaching.
- Participate in the on-call rotation for platform services you own, and lead postmortems for critical incidents.
Minimum 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.
- Proven technical leadership experience designing and shipping ML training platforms, data platforms, or large-scale distributed systems end to end, including 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 and CloudFormation.
- Strong proficiency in Python, with hands-on experience with machine learning frameworks such as PyTorch and TensorFlow.
- Hands-on experience with distributed training and large-scale data processing such as Ray, Spark, and PyTorch Distributed.
- Deep understanding of experiment tracking, model registries, and data and model versioning, including MLflow, Weights & Biases, and DVC.
- Excellent written and verbal communication skills, including writing technical design documents and influencing senior technical and non-technical stakeholders.
Preferred Qualifications
- 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.
Tools and 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.
Compensation and Location
- Location: Boston, MA (hybrid)
- Salary Range: USD 183,500 - 244,600 per year
Benefits
- Mission- and values-driven culture with a safe, inclusive environment to build, grow, and thrive.
- 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) that bring people together, enable networking, mentoring, development, and advocacy for change.
