Sr Machine Learning Engineer

We’re a high-tech home security company that’s passionate about protecting the life you’ve built and our mission of keeping Every Home Secure. And we’ve created a culture here that cares just as deeply about the career you’re building. Ours is a no ego culture of collaboration and innovation where those seeking their next challenge can find big opportunities and make a huge impact on the lives of all those who we protect. We don’t just want you to work here. We want you to grow and thrive here.

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.

Well, we’re growing and thriving. So, we need smart, talented, and humble people who share our values to join us as we disrupt the home security space and relentlessly pursue our mission of keeping Every Home Secure. 

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. 

• →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.

• 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. 

• 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.

• Customer Obsessed - Building deep empathy for our customers, putting them at the core of our work, and developing strong, long-term relationships with them.
• Aim High - Always challenging ourselves and others to raise the bar.
• No Ego - Maintaining a “no job too small” attitude, and an open, inclusive and humble style.
• One Team - Taking a highly collaborative approach to achieving success.
• Lift As We Climb - Investing in developing others and helping others around us succeed.
• Lean & Nimble - Working with agility and efficiency to experiment in an often ambiguous environment.