Machine Learning Engineering Manager, Embedded AI in Boston

About SimpliSafe

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 and Wednesday, to work together in person, and teams can choose 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?

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.

About the Role

SimpliSafe is seeking a seasoned engineering manager with experience in the embedded machine learning space to join the Machine Learning team. As a key contributor, you will play a crucial role in developing and implementing cutting‑edge machine learning models for a range of edge devices.

Manage the edge AI team which is responsible for designing, developing, and deploying ML models to edge devices solve real‑world problems in the home security domain

Work with key stakeholders to identify key research initiatives that can have impact to business outcomes

Set the research direction / roadmap for model optimization techniques

Take research initiatives from idea to production

Plan, adapt and execute multiple initiatives independently and through others

Collaborate with engineers and product managers to achieve optimal performance (accuracy vs. power consumption) trade‑off for battery powered devices

Stay up‑to‑date on the latest advancements in emerging techniques for model optimization techniques such as compression and quantization

Contribute to the development of our machine learning infrastructure and tools

Influence team culture and exemplify best practices in applied research

Requirements

MS or PhD in Computer Science, Artificial Intelligence, or a related field

8+ years of experience in developing production‑grade machine learning solutions

Experience managing an engineering team

Strong understanding of deep learning architectures and statistical modeling techniques, especially as it relates to computer vision and natural processing

Skilled in Python and relevant machine learning libraries (e.g., PyTorch TensorFlow, Keras)

Skilled in C / C++

3+ years of experience developing and deploying models on edge devices leveraging techniques for quantization such as QAT, PTQ

Experience with data preprocessing, feature engineering, and model evaluation

Excellent communication and collaboration skills

Ability to work in a fast paced environment

Nice to Have

Experience with deep learning model architectures such as YOLO

3+ years of experience developing vectorized code on ARM using SIMD (Neon, Helium instructions)

Experience with time series data

Familiarity with cloud computing platforms (e.g., AWS, GCP)

What Values You'll Share

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 \

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