Staff Machine Learning Engineer
SimpliSafe2 days ago
Base Salary
$184k - $245k/yr
Responsibilities
- Define the technical vision, architecture, and multi-quarter roadmap for the ML data and training platform.
- Architect scalable data and feature pipelines that produce reliable, versioned training datasets.
- Build distributed training infrastructure for classical ML, deep learning, and LLM fine-tuning, including GPU scheduling and utilization.
- Own experiment tracking, model registry, dataset lineage, and model lineage to ensure reproducibility.
- Develop CI/CD and automation for training pipelines.
- Partner with data science, data engineering, product, and security leaders to align platform priorities and execute ambiguous initiatives.
- Set organization-wide standards for data versioning, training reproducibility, and data governance.
- Lead build-versus-buy evaluations and make technology decisions involving ML tooling, cloud services, and frameworks.
- Improve reliability and cost efficiency of AWS data and training workloads through capacity planning and compute and GPU optimization.
- Mentor and grow engineers through design reviews, code reviews, and technical coaching.
- Participate in on-call rotations and lead postmortems for critical platform 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.
- Proven technical leadership designing and shipping ML training platforms, data platforms, or large-scale distributed systems end to end.
- Experience 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 Python proficiency 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 tools such as MLflow, Weights & Biases, or DVC.
- Excellent written and verbal communication skills, including technical design documentation and stakeholder influence.
- Preferred experience handling PII data; using Airflow, Kubeflow, Flyte, or Step Functions; working with feature stores or data lakehouses; building pipelines for video, image, or audio datasets; using Kafka or Kinesis; and applying cloud security, IAM, data privacy, and compliance practices.
Benefits
- Hybrid work model with two core in-office days, typically Tuesday, Wednesday, or Thursday, and flexibility to work from home for the remainder of the week.
- Comprehensive total rewards package with medical, retirement, lifestyle, wellness, and family-support benefits.
- Free SimpliSafe system and professional monitoring for the employee’s home.
- Employee Resource Groups offering networking, mentoring, development, and advocacy opportunities.
- Mission- and values-driven culture with an inclusive and supportive work environment.
Tech Stack
Categories
Data EngineeringML Engineering
About SimpliSafe
SimpliSafe builds DIY wireless home security systems—sensors, smart locks, and cameras—sold direct to consumers with optional 24/7 professional monitoring on a subscription basis. Founded in 2006 and headquartered in Boston, it is privately held and majority-owned by Hellman & Friedman. Its systems are used in over 2 million U.S. homes and are available online and through retail partners.