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Senior Machine Learning Engineer

Pay
$152,900–203,800/year · BaseLocation-specific pay — pay source
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.
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Work setting
Not stated
Employment
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What you’ll work on

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

From the employer’s posting
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.

What you’ll bring

All qualifications

Core experience

  • 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).

Preferred experience

  • Experience with frameworks such as vLLM is a plus.
Qualification wording
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.

Benefits in the posting

Full benefits wording
  • A comprehensive total rewards package that supports your wellness and provides security for SimpliSafers and their families (For more information on our total rewards please click here)
  • 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.

From the employer’s posting.

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.

In the employer’s words · Read in context

Apply at SimpliSafe

Posting record

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Actively hiringFirst seen by us 7 days ago, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Oct 27, 20267 days ago
Recorded sightings
8
Last seen by us
Nov 3, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

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.

Time since we first saw it

Day 7

Similar jobs usually come down between day 6 and day 69.

From 4,284 similar postings that came down. Coming down is not always a hire.

SimpliSafe is hiring

4 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Takes jobs down at a usual pace
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • AWS
  • Docker
  • Hadoop
  • Kafka
  • Kubernetes
  • Spark
  • Terraform
  • PyTorch
  • TensorFlow
Where the posting names them
- 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.

Your next step

Complete your application on job-boards.greenhouse.io. The employer’s form will show what is required.

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About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

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: 

  • 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.

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 “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.

What We Offer

  • A mission- and values-driven culture and a safe, inclusive environment where you can build, grow and thrive  
  • A comprehensive total rewards package that supports your wellness and provides security for SimpliSafers and their families (For more information on our total rewards please click here)
  • Free SimpliSafe system and professional monitoring for your home. 
  • 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. 

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.

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Pay
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.
Location & working pattern

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?
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