Embedded Visa Sponsorship Jobs in Massachusetts
Embedded systems engineers in Massachusetts find sponsorship opportunities concentrated in Boston's medical device corridor, defense contractors along Route 128 such as Raytheon and BAE Systems, and semiconductor firms in the Greater Lowell area. Companies here regularly sponsor H-1B visa and O-1 visas for candidates with firmware, RTOS, and hardware-software integration expertise.
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Get Access To All JobsAbout 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 seeking a highly motivated and experienced Embedded Machine Learning Engineer to join our growing Edge AI team. As a key contributor, you will lead the on-device inference and performance optimization of ML models powering outdoor monitoring in the home security space. This role is less about inventing new CV architectures and more about making models fast, power-efficient, stable, and shippable on real embedded hardware (outdoor cameras and doorbells). You will operate across the stack (from model runtime integration down to kernel/operator optimization, memory movement, scheduling, and accelerator utilization) to deliver reliable real-time behavior under tight compute, memory, bandwidth, and thermal constraints across device tiers.
Responsibilities:
- Own the embedded deployment and performance of on-device ML inference for outdoor monitoring workloads (real-time video/event pipelines).
- Optimize end-to-end inference performance across CPU/DSP/NPU/GPU (as applicable): latency, throughput (FPS), memory footprint, power, thermals, startup time, and stability.
- Perform kernel/operator-level optimization:
- vectorization (e.g., SIMD/NEON), tiling, cache-friendly memory layouts
- reducing bandwidth and memory copies, optimizing post-processing
- fusing ops, minimizing synchronization/overhead, thread scheduling
- Integrate and maintain ML models within embedded pipelines:
- model import/export validation, operator compatibility, graph transforms
- runtime integration in C/C++ (including pre/post-processing)
- robust error handling, watchdogs, and safe fallback behavior
- Drive quantization and deployment readiness from an embedded perspective:
- validate INT8/FP16 paths, calibration flows, numerical accuracy checks
- debug quantization edge cases and operator mismatches on target runtimes
- Build tooling for profiling, benchmarking, and regression tracking on devices:
- per-layer timing, memory tracking, thermal/perf tests, CI gating
- automated performance regression gating across device tiers and firmware versions
- Partner closely with ML engineers to translate model changes into deployment impact; provide constraints and design guidance that improve deployability and performance.
- Provide Staff-level leadership: set performance standards, lead technical reviews, mentor engineers, and influence platform roadmap for on-device ML.
Qualifications
- 8+ years of experience in embedded systems and/or performance engineering, with experience shipping production software on constrained devices.
- Strong C/C++ expertise with deep knowledge of low-level performance topics: CPU architecture, memory hierarchy, concurrency, and real-time considerations.
- Demonstrated experience optimizing ML inference on embedded targets, including operator/kernel tuning and end-to-end pipeline optimization.
- Familiarity with modern vision model families (transformer-based detectors such as DEIM/DFINE/RT-DETR series and CNN-based detectors such as YOLO family or similar) sufficient to optimize their execution characteristics (tensor shapes, attention/conv patterns, post-processing).
- Experience with on-device inference runtimes and deployment workflows (e.g., TFLite, ONNX Runtime, TensorRT or vendor runtimes), including operator support constraints and graph-level transformations.
- Strong debugging and profiling skills (perf, flame graphs, hardware counters, tracing) and ability to drive performance investigations to closure.
- Ability to lead cross-functionally across ML, firmware, and hardware teams; comfortable defining benchmarks/KPIs and making tradeoffs.
Bonus Points:
- Experience with embedded accelerators and vendor toolchains (DSP/NPU compilers, delegates, GPU compute, custom runtimes).
- SIMD expertise (ARM NEON/SVE), hand-tuned kernels, or experience with libraries like XNNPACK/QNNPACK/oneDNN/CMSIS-NN (or equivalents).
- Experience with quantized inference (INT8) at scale: calibration strategies, numerical debugging, overflow/underflow handling, and accuracy-performance tradeoffs.
- Experience with camera/doorbell pipelines: ISP/video decode/encode, DMA/zero-copy buffers, multi-threaded real-time streaming.
- Exposure to OS/firmware constraints (embedded Linux, RTOS), power management, thermal throttling behavior, and performance under sustained load.
- Security/privacy experience for edge devices (secure boot/TEE boundaries, model protection, safe telemetry).
- Experience building performance regression systems and device-lab automation for continuous benchmarking.
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 $185,500 to $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.
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 seeking a highly motivated and experienced Embedded Machine Learning Engineer to join our growing Edge AI team. As a key contributor, you will lead the on-device inference and performance optimization of ML models powering outdoor monitoring in the home security space. This role is less about inventing new CV architectures and more about making models fast, power-efficient, stable, and shippable on real embedded hardware (outdoor cameras and doorbells). You will operate across the stack (from model runtime integration down to kernel/operator optimization, memory movement, scheduling, and accelerator utilization) to deliver reliable real-time behavior under tight compute, memory, bandwidth, and thermal constraints across device tiers.
Responsibilities:
- Own the embedded deployment and performance of on-device ML inference for outdoor monitoring workloads (real-time video/event pipelines).
- Optimize end-to-end inference performance across CPU/DSP/NPU/GPU (as applicable): latency, throughput (FPS), memory footprint, power, thermals, startup time, and stability.
- Perform kernel/operator-level optimization:
- vectorization (e.g., SIMD/NEON), tiling, cache-friendly memory layouts
- reducing bandwidth and memory copies, optimizing post-processing
- fusing ops, minimizing synchronization/overhead, thread scheduling
- Integrate and maintain ML models within embedded pipelines:
- model import/export validation, operator compatibility, graph transforms
- runtime integration in C/C++ (including pre/post-processing)
- robust error handling, watchdogs, and safe fallback behavior
- Drive quantization and deployment readiness from an embedded perspective:
- validate INT8/FP16 paths, calibration flows, numerical accuracy checks
- debug quantization edge cases and operator mismatches on target runtimes
- Build tooling for profiling, benchmarking, and regression tracking on devices:
- per-layer timing, memory tracking, thermal/perf tests, CI gating
- automated performance regression gating across device tiers and firmware versions
- Partner closely with ML engineers to translate model changes into deployment impact; provide constraints and design guidance that improve deployability and performance.
- Provide Staff-level leadership: set performance standards, lead technical reviews, mentor engineers, and influence platform roadmap for on-device ML.
Qualifications
- 8+ years of experience in embedded systems and/or performance engineering, with experience shipping production software on constrained devices.
- Strong C/C++ expertise with deep knowledge of low-level performance topics: CPU architecture, memory hierarchy, concurrency, and real-time considerations.
- Demonstrated experience optimizing ML inference on embedded targets, including operator/kernel tuning and end-to-end pipeline optimization.
- Familiarity with modern vision model families (transformer-based detectors such as DEIM/DFINE/RT-DETR series and CNN-based detectors such as YOLO family or similar) sufficient to optimize their execution characteristics (tensor shapes, attention/conv patterns, post-processing).
- Experience with on-device inference runtimes and deployment workflows (e.g., TFLite, ONNX Runtime, TensorRT or vendor runtimes), including operator support constraints and graph-level transformations.
- Strong debugging and profiling skills (perf, flame graphs, hardware counters, tracing) and ability to drive performance investigations to closure.
- Ability to lead cross-functionally across ML, firmware, and hardware teams; comfortable defining benchmarks/KPIs and making tradeoffs.
Bonus Points:
- Experience with embedded accelerators and vendor toolchains (DSP/NPU compilers, delegates, GPU compute, custom runtimes).
- SIMD expertise (ARM NEON/SVE), hand-tuned kernels, or experience with libraries like XNNPACK/QNNPACK/oneDNN/CMSIS-NN (or equivalents).
- Experience with quantized inference (INT8) at scale: calibration strategies, numerical debugging, overflow/underflow handling, and accuracy-performance tradeoffs.
- Experience with camera/doorbell pipelines: ISP/video decode/encode, DMA/zero-copy buffers, multi-threaded real-time streaming.
- Exposure to OS/firmware constraints (embedded Linux, RTOS), power management, thermal throttling behavior, and performance under sustained load.
- Security/privacy experience for edge devices (secure boot/TEE boundaries, model protection, safe telemetry).
- Experience building performance regression systems and device-lab automation for continuous benchmarking.
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 $185,500 to $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.
Embedded Job Roles in Massachusetts
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Search Embedded Jobs in MassachusettsEmbedded Jobs in Massachusetts: Frequently Asked Questions
Which companies sponsor visas for embedded engineers in Massachusetts?
Defense and aerospace contractors including Raytheon Technologies and BAE Systems are among the most active H-1B sponsors for embedded roles along Route 128. Medical device companies such as Boston Scientific and Analog Devices also have consistent sponsorship histories for firmware and hardware engineers. Smaller semiconductor and IoT startups in Cambridge and Waltham sponsor as well, though less predictably than larger employers.
Which visa types are most common for embedded roles in Massachusetts?
The H-1B is the most common visa for embedded engineers in Massachusetts, as roles requiring a degree in electrical engineering, computer engineering, or a related field typically qualify as specialty occupations. Candidates with exceptional publication records or major industry awards may be eligible for the O-1A. Australians should also consider the E-3 visa, which operates outside the H-1B lottery and has a much shorter processing timeline.
Which cities in Massachusetts have the most embedded sponsorship jobs?
The Route 128 corridor, spanning Waltham, Lexington, and Burlington, concentrates the highest density of embedded sponsorship roles due to its defense and semiconductor industry clusters. Boston and Cambridge add significant volume through medical device, robotics, and university-affiliated spinouts. Lowell and Andover also host embedded engineering positions tied to established chip design and industrial automation companies.
How to find embedded visa sponsorship jobs in Massachusetts?
Migrate Mate filters job listings specifically by visa sponsorship availability, making it straightforward to search for embedded roles in Massachusetts without sorting through positions that won't support international candidates. You can narrow results by location and role type to surface firmware, RTOS, and hardware-software positions at Massachusetts employers with active sponsorship histories. Creating a profile also helps match you to relevant openings as they appear.
Are there state-specific factors that affect embedded sponsorship hiring in Massachusetts?
Massachusetts employers filing H-1B Labor Condition Applications must meet Department of Labor prevailing wage requirements for the specific job level and metro area, with the Boston-Cambridge-Nashua region carrying its own wage determinations. The state's strong university pipeline from MIT, Northeastern, and UMass Amherst means employers are accustomed to sponsoring new graduates. Defense contractors additionally require candidates to be sponsorship-eligible for security clearances, which can affect hiring timelines.
What is the prevailing wage for sponsored embedded jobs in Massachusetts?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.