{"id":24043,"url":"https://alion.io/job/simplisafe-staff-software-engineer-ml-infrastructure","title":"Staff Software Engineer, ML Infrastructure","company":{"id":5386,"name":"SimpliSafe","domain":"simplisafe.com","url":"https://alion.io/company/simplisafe","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":78,"open_postings":3,"ghost_share":0,"stale_share":0.667,"repost_share":0,"time_to_fill_p50_days":73,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Boston, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":146600,"max":215100,"currency":"USD","period":"year","gross":null,"usd_annual":215100},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"LLM","optional":false},{"name":"Amazon EKS","optional":true},{"name":"Amazon Kinesis","optional":true},{"name":"Amazon S3","optional":true},{"name":"Apache Kafka","optional":true},{"name":"AWS","optional":true},{"name":"C++","optional":true},{"name":"CI/CD","optional":true},{"name":"Flink","optional":true},{"name":"IAM","optional":true},{"name":"KServe","optional":true},{"name":"Kubernetes","optional":true},{"name":"KV Cache","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"MLFlow","optional":true},{"name":"Python","optional":true},{"name":"Quantization","optional":true},{"name":"Ray","optional":true},{"name":"Rust","optional":true},{"name":"SGLang","optional":true},{"name":"Speculative Decoding","optional":true},{"name":"TensorRT","optional":true},{"name":"TensorRT-LLM","optional":true},{"name":"TGI","optional":true},{"name":"Triton","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-05-21T19:15:33Z","employer_posted_date":"2026-07-16","last_verified_at":"2026-10-16T13:29:32Z","board_verified":true,"closed_at":null,"days_open":147,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":147},"description":"About SimpliSafe\nWe’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.\nWe’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.\nWhy are we hiring?\nWell, 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.\nAbout the Role\nWe're looking for a Staff Software Engineer to join our Cloud ML team - the team that owns both the cloud-side ML infrastructure and the applied ML research that powers SimpliSafe's intelligent home security products. This is a senior individual contributor role for a distributed systems expert who wants to apply that craft to one of the most demanding problem domains in the company.\nYou'll partner closely with other Staff and Principal engineers to drive architecture, mentor across the team, and set the technical direction for our ML platform. The work spans two of our most demanding workloads: real-time computer vision inference that processes video from cameras and doorbells across our customer base, and LLM/GenAI infrastructure that will power our future generation of intelligent applications. Both are, fundamentally, distributed systems problems - high-throughput, low-latency, multi-tenant, GPU-aware, and unforgiving of regressions.\nThis role is for someone who has built and operated large-scale distributed services in production - high-QPS APIs, real-time platforms, low-latency serving systems - and is excited to bring that depth to ML infrastructure. Prior ML experience is a plus, not a prerequisite. If you've shipped systems that serve a lot of traffic, scale gracefully, and stay up at 3am, we want to talk to you.\nWhat You'll Do\nSet technical direction for ML infrastructure\nDrive architecture decisions for our Kubernetes-based ML platform - anchored on Ray for inference, alongside KServe, Triton, and vLLM - across real-time and batch workloads.\nLead deep technical reviews on system design, capacity planning, and reliability for the highest-stakes ML systems at SimpliSafe.\nIdentify and remove the systemic bottlenecks in our ML deployment infrastructure - whether that's serving reliability, deployment friction, observability gaps, scaling, or cost.\nBuild and operate real-time CV inference at scale\nOwn the design and evolution of cloud-side inference systems that process live video and events from SimpliSafe devices in real time.\nDrive throughput, latency, and cost improvements (batching strategies, GPU utilization, autoscaling, multi-model serving) for production CV models.\nBuild the feedback loops between cloud inference, edge devices, and the data flywheel that improves model quality over time.\nStand up LLM/GenAI serving infrastructure\nHelp shape how SimpliSafe serves LLMs in production - model serving patterns, KV-cache and batching strategies, evaluation pipelines, guardrails, and cost controls.\nPartner with applied ML engineers to take new GenAI-powered product features from prototype to scaled deployment.\nRaise the engineering bar across Cloud ML\nMentor engineers across the team through design reviews, code reviews, pairing, and written guidance - a meaningful uplift on everyone you work with.\nEstablish and evangelize best practices for model lifecycle management (registry, deployment, monitoring, rollback, drift) and on-call.\nWrite the documentation, runbooks, and architectural decision records that make the platform legible and durable.\nOwn reliability and operational excellence\nLead incident response and postmortems for critical ML systems; turn lessons learned into platform-level improvements.\nDefine SLOs, observability standards, and on-call practices for ML services in production.\nQualifications\n8+ years of software engineering experience, with a clear track record of building and operating large-scale distributed systems in production.\nDeep expertise in high-throughput, low-latency services - ad serving, recommendations, real-time APIs, online platforms, or similar - including the operational reality of running them at scale.\nStrong production experience on Kubernetes and AWS (EKS, S3, IAM, networking) and with Kafka, containerized deployments, CI/CD, and infrastructure-as-code.\nDemonstrated experience with the building blocks of high-scale systems: load balancing, autoscaling, batching, caching, multi-tenancy, queuing, and capacity planning.\nProficiency in Python is required; experience with a systems language (Go, C++, Rust) for performance-sensitive components is a plus.\nStaff-level technical leadership: ability to drive ambiguous, cross-cutting initiatives, align senior stakeholders, and elevate the engineers around you without formal authority.\nStrong written and verbal communication - you can make complex technical tradeoffs legible to ML scientists, product, and other infra teams.\nML exposure is preferred - having deployed or operated production ML systems, worked closely with ML teams, or built ML-adjacent infrastructure. Exceptional distributed systems engineers without direct ML experience are encouraged to apply; we'll help you ramp.\nBonus Points\nHands-on experience with Ray, KServe, Triton, vLLM, or other ML serving stacks.\nHands-on experience with LLM serving in production (vLLM, TGI, TensorRT-LLM, SGLang) - KV cache management, continuous batching, speculative decoding, quantization for serving.\nExperience building real-time video or streaming pipelines (Kafka, Kinesis, Flink, or similar) at scale.\nExperience operating GPU-based inference systems - GPU-aware scheduling, multi-model serving, accelerator utilization optimization.\nFamiliarity with ML fundamentals - how models are trained, evaluated, versioned, deployed, monitored, and rolled back in production.\nExperience with model lifecycle tooling (MLflow, Weights & Biases, model registries, drift detection, shadow deployments).\nOpen source contributions to distributed systems or ML infrastructure projects.\nExperience operating in environments with strong security and compliance requirements.\nWhy This Role\nThe Cloud ML team owns the full surface area - infrastructure and applied research - which means your work as a Staff infra engineer directly shapes what's possible for the science. You'll have unusual leverage: the platform you build determines how fast SimpliSafe can ship intelligent features, and the features we ship directly impact whether someone's home is safer tonight than it was yesterday.\nWhat Values You’ll Share\nCustomer Obsessed - Building deep empathy for our customers, putting them at the core of our work, and developing strong, long-term relationships with them.\nAim High - Always challenging ourselves and others to raise the bar.\nNo Ego - Maintaining a “no job too small” attitude, and an open, inclusive and humble style.\nOne Team - Taking a highly collaborative approach to achieving success.\nLift As We Climb - Investing in developing others and helping others around us succeed.\nLean & Nimble - Working with agility and efficiency to experiment in an often ambiguous environment.\nWhat We Offer\nA mission- and values-driven culture and a safe, inclusive environment where you can build, grow and thrive \nA 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)\nFree SimpliSafe system and professional monitoring for your home. \nEmployee Resource Groups (ERGs) that bring people together, give opportunities to network, mentor and develop, and advocate for change.\nThe target annual base pay range for this role is $146,600 to $215,100.\nThis 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. \nBeyond 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.\nWe’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.\nWe 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 .","description_format":"text","description_chars":10085,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity","Hybrid work"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Smart Home Devices","Locks, Safes & Home Security Products","Access Control & Alarm Systems"],"lifecycle":[{"event":"open","at":"2026-05-21T19:15:33Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 8 · green card filings: 6","filings_12m":8,"filings_prev_12m":17,"green_card_filings_12m":6,"median_offered_wage_usd":141071,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":6}],"liveness":{"score":22,"band":"cold","label":"Long shot","p_open":1,"p_active":0.776,"p_room":0.28,"age_days":147,"expected_fill_days":73,"reasons":["conf:1","velocity","win:tail","crowd:"],"computed_at":"2026-10-16T05:45:00Z"},"pay":{"stated_usd_annual":215100,"is_top_pay":false},"html_url":"https://alion.io/job/simplisafe-staff-software-engineer-ml-infrastructure","json_url":"https://alion.io/job/simplisafe-staff-software-engineer-ml-infrastructure.json","meta":{"generated_at":"2026-10-16T14:18:02Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":13824,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-17T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":5386},"rest":"https://alion.io/mcp/rest/get_company?id=5386"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fsimplisafe-staff-software-engineer-ml-infrastructure"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fsimplisafe-staff-software-engineer-ml-infrastructure"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fsimplisafe-staff-software-engineer-ml-infrastructure"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/simplisafe-staff-software-engineer-ml-infrastructure\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fsimplisafe-staff-software-engineer-ml-infrastructure"}]}