Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Employee Resource Groups (ERGs) that bring people together, give opportunities to network, mentor and develop, and advocate for change. 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.Read the full posting
We are looking for an experienced ML Infrastructure/MLOps Engineer to join our team as a Senior Machine Learning Engineer.
Lead architecture and optimization of scalable ML model serving systems
Build CI/CD pipelines for fast, safe ML model releases
Own production monitoring for ML models and systems
Manage AWS or other public cloud infrastructure for ML workloads
Work with data scientists and engineers to operationalize models
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.
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.
Collaborate with data scientists, engineers, and stakeholders to operationalize ML models.
5+ years of experience in software engineering, data engineering, or a related field, with at least 3 years focused on MLOps or ML infrastructure.
Experience with Ray for inference, or pipeline orchestration
Hands-on experience with CI/CD pipelines, Docker, Kubernetes, and infrastructure-as-code tools (e.g., Terraform, Cloud Formation).
Hands-on experience with deploying large language models (LLMs) to production.
Proficiency in programming languages like Python, and familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch).
Experience with distributed systems and big data technologies (e.g., Spark, Hadoop).
Experience with frameworks such as vLLM is a plus.
From the employer’s posting.
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.
In the employer’s words · Read in context
Actively hiringFirst seen by us 7 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
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- 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.
- 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
- 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).
- 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).
- 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).
- 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.
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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, 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?
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
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:
Qualifications
Nice to Have
What Values You’ll Share
What We Offer
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.
We wholeheartedly embrace and actively seek applications from all individuals, no matter how they identify. We are committed to cultivating a diverse and inclusive workplace, and we believe our work is enriched when we incorporate a multitude of perspectives, backgrounds, and experiences. We want everyone who works here to thrive and contribute to not only our mission of keeping every home secure, but also to making our workplace safe and supportive for others. If a reasonable accommodation may be needed to fully participate in the job application or interview process, to perform the essential functions of a position, or to receive other benefits and privileges of employment, please contact careers@simplisafe.com.
Selected passages from the saved posting. Check the full description for conditions and exceptions.
Employee Resource Groups (ERGs) that bring people together, give opportunities to network, mentor and develop, and advocate for change. 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.
Boston, MA
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. Why are we hiring?
No clear work-authorization passage found. Eligibility is unconfirmed.