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.
Staff Machine Learning Engineer
- Pay
$183,500–244,600/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 $183,500 - $244,600 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- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingOwn the architecture and multi-quarter roadmap for the ML data and training platform
Build distributed training infrastructure for classical ML, deep learning, and LLM fine-tuning
Lead scalable data and feature pipelines that produce versioned training datasets
Own experiment tracking, model registry, and lineage so every model is reproducible
Mentor senior and mid-level engineers through design and code reviews
From the employer’s posting
Define and own the technical vision, architecture, and multi-quarter roadmap for SimpliSafe's ML data and training platform.
Build and evolve distributed training infrastructure for classical ML, deep learning, and LLM fine-tuning, including efficient GPU scheduling and utilization.
Architect and lead the delivery of scalable data and feature pipelines that produce reliable, versioned training datasets.
Own the experimentation stack, including experiment tracking, model registry, and dataset and model lineage, so that every model is reproducible.
Mentor and grow senior and mid-level engineers through design reviews, code reviews, and technical coaching
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
Posting record
Report an errorActively 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
- 9
- Last seen by us
- Nov 4, 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
Time since we first saw it
Day 7
Similar jobs usually come down between day 6 and day 69.
From 4,296 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
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
- Delta Lake
- Docker
- Iceberg
- Kafka
- Kubernetes
- MLflow
- Spark
- Terraform
- Airflow
- PyTorch
- TensorFlow
- SageMaker
Where the posting names them
Responsibilities: ● Define and own the technical vision, architecture, and multi-quarter roadmap for SimpliSafe's ML data and training platform. ● Architect and lead the delivery of scalable data and feature pipelines that produce reliable, versioned training datasets. ● Build and evolve distributed training infrastructure for classical ML, deep learning, and LLM fine-tuning, including efficient GPU scheduling and utilization. ● Own the experimentation stack, including experiment tracking, model registry, and dataset and model lineage, so that every model is reproducible. ● Develop CI/CD and automation for training pipelines, enabling data scientists to go from idea to trained, validated model quickly and safely. ● Partner with data science, data engineering, product, and security leaders to align the platform with business priorities and turn ambiguous problems into clear, executable plans. ● Set org-wide standards and paved paths for data versioning, training reproducibility, and data governance, and drive their adoption across teams. ● Lead build-vs-buy evaluations and make durable technology decisions on ML tooling, cloud services, and frameworks. ● Drive reliability and cost efficiency of training and data workloads in AWS, including capacity planning and compute and GPU cost optimization. ● Mentor and grow senior and mid-level engineers through design reviews, code reviews, and technical coaching; act as a force multiplier for the team. ● Participate in the team's on-call rotation for platform services the team owns, and lead postmortems for critical incidents. Qualifications ● 8+ years of experience in software engineering, data engineering, or a related field, with at least 4 years focused on ML infrastructure or MLOps. ● Proven track record of technical leadership: designing and shipping ML training platforms, data platforms, or large-scale distributed systems end to end, and leading cross-team initiatives without direct authority. ● Expert-level experience with AWS or similar public clouds, including compute, storage, networking, and container orchestration. ● Deep experience with CI/CD pipelines, Docker, Kubernetes, and infrastructure-as-code tools (e.g., Terraform, CloudFormation). ● Strong proficiency in Python, and hands-on experience with machine learning frameworks (e.g., PyTorch, TensorFlow). ● Hands-on experience with distributed training and large-scale data processing (e.g., Ray, Spark, PyTorch Distributed). ● Deep understanding of experiment tracking, model registries, and data and model versioning (e.g., MLflow, Weights & Biases, DVC). ● Excellent written and verbal communication skills, including writing technical design docs and influencing senior technical and non-technical stakeholders. Nice to Have ● Experience handling PII data ● Experience with workflow orchestration tools (e.g., Airflow, Kubeflow, Flyte, Step Functions). ● Experience with feature stores and data lakehouse technologies (e.g., Feast, SageMaker Feature Store, Delta Lake, Iceberg). ● Experience building pipelines for large unstructured datasets such as video, images, or audio, including labeling and curation workflows. ● Experience with event-driven or streaming data architectures (e.g., Kafka, Kinesis). ● Knowledge of cloud security, IAM, data privacy, and compliance best practices for ML data and workloads.
About the Role We are looking for a Staff Machine Learning Engineer to serve as the technical leader for our ML data and training platform. In this role, you will set the technical direction for how SimpliSafe prepares data, trains, fine-tunes, and tracks machine learning models, including large language models. You will own the architecture of our data and training infrastructure, drive multi-quarter initiatives across teams, and raise the engineering bar through design reviews, mentorship, and standards. If you want to shape how ML models get built at scale, we'd love to hear from you. Responsibilities: ● Define and own the technical vision, architecture, and multi-quarter roadmap for SimpliSafe's ML data and training platform. ● Architect and lead the delivery of scalable data and feature pipelines that produce reliable, versioned training datasets. ● Build and evolve distributed training infrastructure for classical ML, deep learning, and LLM fine-tuning, including efficient GPU scheduling and utilization. ● Own the experimentation stack, including experiment tracking, model registry, and dataset and model lineage, so that every model is reproducible. ● Develop CI/CD and automation for training pipelines, enabling data scientists to go from idea to trained, validated model quickly and safely. ● Partner with data science, data engineering, product, and security leaders to align the platform with business priorities and turn ambiguous problems into clear, executable plans. ● Set org-wide standards and paved paths for data versioning, training reproducibility, and data governance, and drive their adoption across teams. ● Lead build-vs-buy evaluations and make durable technology decisions on ML tooling, cloud services, and frameworks. ● Drive reliability and cost efficiency of training and data workloads in AWS, including capacity planning and compute and GPU cost optimization. ● Mentor and grow senior and mid-level engineers through design reviews, code reviews, and technical coaching; act as a force multiplier for the team. ● Participate in the team's on-call rotation for platform services the team owns, and lead postmortems for critical incidents. Qualifications ● 8+ years of experience in software engineering, data engineering, or a related field, with at least 4 years focused on ML infrastructure or MLOps. ● Proven track record of technical leadership: designing and shipping ML training platforms, data platforms, or large-scale distributed systems end to end, and leading cross-team initiatives without direct authority. ● Expert-level experience with AWS or similar public clouds, including compute, storage, networking, and container orchestration. ● Deep experience with CI/CD pipelines, Docker, Kubernetes, and infrastructure-as-code tools (e.g., Terraform, CloudFormation). ● Strong proficiency in Python, and hands-on experience with machine learning frameworks (e.g., PyTorch, TensorFlow). ● Hands-on experience with distributed training and large-scale data processing (e.g., Ray, Spark, PyTorch Distributed). ● Deep understanding of experiment tracking, model registries, and data and model versioning (e.g., MLflow, Weights & Biases, DVC). ● Excellent written and verbal communication skills, including writing technical design docs and influencing senior technical and non-technical stakeholders.
Qualifications ● 8+ years of experience in software engineering, data engineering, or a related field, with at least 4 years focused on ML infrastructure or MLOps. ● Proven track record of technical leadership: designing and shipping ML training platforms, data platforms, or large-scale distributed systems end to end, and leading cross-team initiatives without direct authority. ● Expert-level experience with AWS or similar public clouds, including compute, storage, networking, and container orchestration. ● Deep experience with CI/CD pipelines, Docker, Kubernetes, and infrastructure-as-code tools (e.g., Terraform, CloudFormation). ● Strong proficiency in Python, and hands-on experience with machine learning frameworks (e.g., PyTorch, TensorFlow). ● Hands-on experience with distributed training and large-scale data processing (e.g., Ray, Spark, PyTorch Distributed). ● Deep understanding of experiment tracking, model registries, and data and model versioning (e.g., MLflow, Weights & Biases, DVC). ● Excellent written and verbal communication skills, including writing technical design docs and influencing senior technical and non-technical stakeholders. Nice to Have ● Experience handling PII data ● Experience with workflow orchestration tools (e.g., Airflow, Kubeflow, Flyte, Step Functions). ● Experience with feature stores and data lakehouse technologies (e.g., Feast, SageMaker Feature Store, Delta Lake, Iceberg). ● Experience building pipelines for large unstructured datasets such as video, images, or audio, including labeling and curation workflows. ● Experience with event-driven or streaming data architectures (e.g., Kafka, Kinesis). ● Knowledge of cloud security, IAM, data privacy, and compliance best practices for ML data and workloads. What Values You’ll Share
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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
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 a Staff Machine Learning Engineer to serve as the technical leader for our ML data and training platform. In this role, you will set the technical direction for how SimpliSafe prepares data, trains, fine-tunes, and tracks machine learning models, including large language models. You will own the architecture of our data and training infrastructure, drive multi-quarter initiatives across teams, and raise the engineering bar through design reviews, mentorship, and standards. If you want to shape how ML models get built at scale, we'd love to hear from you.
Responsibilities:
● Define and own the technical vision, architecture, and multi-quarter roadmap for SimpliSafe's ML data and training platform.
● Architect and lead the delivery of scalable data and feature pipelines that produce reliable, versioned training datasets.
● Build and evolve distributed training infrastructure for classical ML, deep learning, and LLM fine-tuning, including efficient GPU scheduling and utilization.
● Own the experimentation stack, including experiment tracking, model registry, and dataset and model lineage, so that every model is reproducible.
● Develop CI/CD and automation for training pipelines, enabling data scientists to go from idea to trained, validated model quickly and safely.
● Partner with data science, data engineering, product, and security leaders to align the platform with business priorities and turn ambiguous problems into clear, executable plans.
● Set org-wide standards and paved paths for data versioning, training reproducibility, and data governance, and drive their adoption across teams.
● Lead build-vs-buy evaluations and make durable technology decisions on ML tooling, cloud services, and frameworks.
● Drive reliability and cost efficiency of training and data workloads in AWS, including capacity planning and compute and GPU cost optimization.
● Mentor and grow senior and mid-level engineers through design reviews, code reviews, and technical coaching; act as a force multiplier for the team.
● Participate in the team's on-call rotation for platform services the team owns, and lead postmortems for critical incidents.
Qualifications
● 8+ years of experience in software engineering, data engineering, or a related field, with at least 4 years focused on ML infrastructure or MLOps.
● Proven track record of technical leadership: designing and shipping ML training platforms, data platforms, or large-scale distributed systems end to end, and leading cross-team initiatives without direct authority.
● Expert-level experience with AWS or similar public clouds, including compute, storage, networking, and container orchestration.
● Deep experience with CI/CD pipelines, Docker, Kubernetes, and infrastructure-as-code tools (e.g., Terraform, CloudFormation).
● Strong proficiency in Python, and hands-on experience with machine learning frameworks (e.g., PyTorch, TensorFlow).
● Hands-on experience with distributed training and large-scale data processing (e.g., Ray, Spark, PyTorch Distributed).
● Deep understanding of experiment tracking, model registries, and data and model versioning (e.g., MLflow, Weights & Biases, DVC).
● Excellent written and verbal communication skills, including writing technical design docs and influencing senior technical and non-technical stakeholders.
Nice to Have
● Experience handling PII data
● Experience with workflow orchestration tools (e.g., Airflow, Kubeflow, Flyte, Step Functions).
● Experience with feature stores and data lakehouse technologies (e.g., Feast, SageMaker Feature Store, Delta Lake, Iceberg).
● Experience building pipelines for large unstructured datasets such as video, images, or audio, including labeling and curation workflows.
● Experience with event-driven or streaming data architectures (e.g., Kafka, Kinesis).
● Knowledge of cloud security, IAM, data privacy, and compliance best practices for ML data and workloads.
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 $183,500 - $244,600
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.
Source details
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Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- 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 $183,500 - $244,600 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?
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.