Staff Machine Learning Engineer

About SimpliSafe

We’re embracing a hybrid work model that enables our teams to split their time between office and home. Hybrid for us means we expect our teams to come together in our state-of-the-art office on two core days, typically Tuesday, Wednesday, or Thursday – working together in person and choosing where they work for the remainder of the week. We all benefit from flexibility and get to use the best of both worlds to get our work done.

Why are we hiring?

About the Role
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.

Qualifications
● 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.

The target annual base pay range for this role is $183,500 - $244,600

This target annual base pay range represents our good-faith estimate of what we expect to pay for this role. We use a market-based compensation approach to set our target annual base pay ranges and make adjustments annually. We carefully tailor individual compensation packages, including base pay, taking into consideration employees’ job-related skills, experience, qualifications, work location, and other relevant business factors.

Beyond base pay, we offer a Total Rewards package that may include participation in our annual bonus program, equity, and other forms of compensation, in addition to a full range of medical, retirement, and lifestyle benefits. More details can be found here.

We’re committed to fair and equitable pay practices, as well as pay transparency. We regularly review our programs to ensure they remain competitive and aligned with our values.