Lead Machine Learning Infrastructure Engineer

About Us:

At Cobalt AI, we're advancing physical security through innovative AI-powered monitoring solutions. Our primary offering, Cobalt Monitoring Intelligence, builds on our established track record of protecting Fortune 1000 companies across more than 10 countries. We've effectively managed over 350 million alarm events and conducted 15 million hours of active patrols, resulting in a 90% noise reduction for our clients. We maintain a strong focus on both technological advancement and data protection, as demonstrated by our ongoing SOC2 compliance.

We're currently expanding our capabilities from hundreds of robots to hundreds of thousands of cameras, and we're looking for skilled professionals to join our growing team. We need individuals who can contribute to our early-stage architecture decisions and help drive our technological development. Our approach combines the analytical strengths of AI with human expertise to create more effective security solutions. This presents an opportunity to be part of significant advancements in how businesses and communities approach security.

At Cobalt, we pride ourselves on fostering a positive, people-first culture. We lead with empathy, prioritize work-life balance, and invest in our employees’ professional development. Our approach is simple: we treat people like people, not just resources. Join our results-driven team culture to work on innovative projects that drive the future of AI-enabled edge devices and security solutions, while experiencing a workplace where your wellbeing and growth are as important as your contributions.

What You’ll Do:

• Design and implement robust machine learning pipelines to crunch on a large number of simultaneous video streams.


• Improve the performance and reliability of our GPU workloads, on the edge and in the cloud.

• Design and implement scalable infrastructure for deploying CUDA-enabled ML models to our expanding fleet of edge devices.

• Discover, evaluate, and integrate cutting-edge computer vision models/algorithms to enable better decision making in time-sensitive contexts. 


• Critically assess and address networking and data storage security risks for the edge processors and their integration with our backend.


• Lead a mix of senior and junior engineers by example, establishing and maintaining best practices in ML infrastructure development.


• Lead design reviews and gather input from multiple teams to ensure high-quality outcomes.


• Tackle challenging interdisciplinary problems within our deep tech stack.


• Deploy changes with immediate impacts on our product, worldwide.

Requirements:

• Minimum 5+ years of experience in software engineering, with a focus on ML infrastructure design.


• Experience leading teams or managing projects.


• Demonstrated expertise in ML infrastructure development and best coding practices for Python, C++, Rust or equivalent.


• Experience with pipeline stability and resilience using tools, such as Datadog or equivalent.
  


• Passionate about delivering high-quality products that exceed user expectations.


• Eager to work with, mentor, and learn from peers through code reviews, design documents, and pair programming.


• Enthusiastic about diving into different areas of technology and problem-solving.


• Must be authorized to work in the United States.

Bonus Skills:

• Experience with generative AI technologies.


• Experience with event streams, like kinesis or equivalent.


• Familiarity with the Nix package manager and its ecosystem.


• Experience working in a fast-paced startup environment.


• Hands-on experience with AWS and DevOps practices.


• Expertise in networking and security protocols.


• Experience building and scaling high availability distributed systems.


• Background as a full-stack engineer.

Salary Range in SVBA: $160k - $210k (actual compensation will be determined based on experience, location, and other factors permitted by law)

Cobalt AI is an equal employment opportunity employer and values diversity.  Qualified candidates are considered for employment without regard to race, religion, gender, gender identity, sexual orientation, national origin, age, military or veteran status, disability, or any other characteristic protected by applicable law.