Sr 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.

About the Role

We are looking for an experienced ML Infrastructure/MLOps Engineer to join our team as a Senior Machine Learning Engineer. In this role, you will drive the development and deployment of machine learning models, optimize ML workflows, and help ensure our infrastructure is scalable, reliable, and secure. If you have a passion for automation, cloud technology, and delivering high-impact solutions, we'd love to hear from you.

Responsibilities:

• Lead the architecture, deployment, and optimization of scalable ML model serving systems for real-time and batch use cases.
• Collaborate with data scientists, engineers, and stakeholders to operationalize ML models.
• Develop CI/CD pipelines for ML models enabling rapid, safe, and consistent model releases.
• Design, implement, and own comprehensive production monitoring for ML models/systems.
• Manage cloud infrastructure, primarily in AWS or other major public clouds, to support ML workloads.
• Drive best practices in model versioning, observability, reproducibility, and deployment reliability
• Serve in an on-call rotation as a first responder for software owned by your team.

Qualifications

• 5+ years of experience in software engineering, data engineering, or a related field, with at least 3 years focused on MLOps or ML infrastructure.
• Deep hands-on experience with AWS or similar public clouds, including compute, networking, container orchestration, and observability stacks.
• Hands-on experience with CI/CD pipelines, Docker, Kubernetes, and infrastructure-as-code tools (e.g., Terraform, Cloud Formation).
• Proficiency in programming languages like Python, and familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch).
• Solid understanding of ML lifecycle management, including experiment tracking, versioning, and monitoring.
• Strong communication skills for partnering with cross-functional technical and non-technical teams.

Nice to Have

• Experience with Ray for inference, or pipeline orchestration
• Hands-on experience with deploying large language models (LLMs) to production.
• Experience with frameworks such as vLLM is a plus.
• Experience with distributed systems and big data technologies (e.g., Spark, Hadoop).
• Experience with event-driven or streaming architectures (e.g., Kafka, Kinesis).
• Knowledge of cloud security, IAM, and compliance best practices for ML workloads.
• LLM application development, including prompt engineering and evaluation.

The target annual base pay range for this role is $152,900 - $203,800

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.