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About This Role
Who we are
Samsara (NYSE: IOT) is the pioneer of the Connected Operations™ Cloud, which is a platform that enables organizations that depend on physical operations to harness Internet of Things (IoT) data to develop actionable insights and improve their operations. At Samsara, we are helping improve the safety, efficiency and sustainability of the physical operations that power our global economy. Representing more than 40% of global GDP, these industries are the infrastructure of our planet, including agriculture, construction, field services, transportation, and manufacturing — and we are excited to help digitally transform their operations at scale.
Working at Samsara means you'll help define the future of physical operations and be on a team that's shaping an exciting array of product solutions, including Video-Based Safety, Vehicle Telematics, Apps and Driver Workflows, and Equipment Monitoring. As part of a recently public company, you'll have the autonomy and support to make an impact as we build for the long term.
About the role:
Samsara is building a brand-new role at the intersection of security engineering and applied AI. This person will be the most AI-forward technologist on our Application Security team — using frontier AI models to both strengthen our security posture and stress-test it. You'll spend one day using AI to red-team our cloud environment and the next validating firmware vulnerabilities on our hardware, with the goal to secure our applications for AI Adversaries. This is a highly visible, org-wide role reporting into a newly formed function, with a clear path to shape Samsara's AI security strategy for years to come.
This is a remote position open to candidates residing in the US except the San Francisco Bay Metro Area, NYC Metro Area, and Washington, D.C. Metro Area.
You should apply if:
- You want to impact the industries that run our world: Your efforts will result in real-world impact – helping to keep the lights on, get food into grocery stores, reduce emissions, and most importantly, ensure workers return home safely.
- You are the architect of your own career: If you put in the work, this role won't be your last at Samsara. We set up our employees for success and have built a culture that encourages rapid career development, countless opportunities to experiment and master your craft in a hyper growth environment.
- You're energized by our opportunity: The vision we have to digitize large sectors of the global economy requires your full focus and best efforts to bring forth creative, ambitious ideas for our customers.
- You want to be with the best: At Samsara, we win together, celebrate together and support each other. Youwill be surrounded by a high-calibre team that will encourage you to do your best.
In this role, you will:
- Deliver accurate, consistent security assessments of Samsara's software, code, and firmware, evaluating their resilience against AI-driven/adversarial attacks, and implementing fixes.
- Create and translate general AI security findings into Samsara-specific fixes for our application security layer— including adapting techniques built for software to apply to hardware and embedded firmware.
- Build and pressure-test novel AI-enabled security tooling and workflows, then drive adoption of what proves valuable across the broader security org.
- Partner cross-functionally with engineering, hardware, and leadership (including VP-level threat modeling sessions) as Samsara's go-to expert on AI-related security risk.
- Help define & drive Samsara's medium- and long-term strategy for adopting AI safely and effectively within security operations.
- Use frontier AI/LLM tools (e.g., Anthropic's and OpenAI's cybersecurity-specific programs) hands-on for both offensive validation (red-teaming our own systems) and defensive tooling — required experience integrating, securing, or red-teaming AI-enabled systems (e.g., LLMs, agentic workflows, prompt injection/adversarial testing) is essential to this role.
- Champion, role model, and embed Samsara's cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices
Minimum requirements for the role:
- 8+ years of experience in application security, vulnerability management, threat modeling, offensive security, or a closely related discipline, with demonstrated application fix impact and influence across a large part of an organization.
- Hands-on experience applying AI/LLM tools to real security workflows (not just using AI as an end user) — e.g., building with APIs, agents, or automation to support security use cases.
- Experience conducting technical security assessments (e.g. offensive security or validation exercises) across both software and hardware/firmware environments.
- Experience in creating solutions for secure code with engineers using languages such as GoLang.
- Experience developing AI and agentic solutions using Github, and developing within AWS cloud.
An ideal candidate also has:
- Experience with AI-specific attack techniques (prompt injection, jailbreaks, model/agent abuse) for vulnerability management and application security.
- Experience building or validating security controls for agentic AI, RAG systems, or LLM-integrated platforms.
- A track record of driving org-wide security strategy or presenting technical findings to senior/executive stakeholders.
- Relevant industry certifications (e.g., OSCP, GIAC, CISSP) or AWS certifications.
Total Rewards
At Samsara, we build for the people who keep the global economy moving. We want owners, not passengers, which is why our rewards are designed to fuel high-impact builders. Our compensation program delivers above-market total compensation through a combination of base salary, performance-based bonus/variable pay, and equity (for eligible roles) in a high-growth public company. We meaningfully differentiate pay for our top performers, who have the opportunity to earn above-market compensation that can outpace the broader market over time.
Beyond compensation, we provide the foundations that enable long-term success: a flexible, employee-led remote model, a professional development stipend, comprehensive health and parental leave plans, and more. If you're ready to build for the long term and own the outcome, your journey starts here.
Flexible Working
At Samsara, we embrace a flexible working model that caters to the diverse needs of our teams. Our offices are open for those who prefer to work in-person and we also support remote work where it aligns with our operational requirements. For certain positions, being close to one of our offices or within a specific geographic area is important to facilitate collaboration, access to resources, or alignment with our service regions. In these cases, the job description will clearly indicate any working location requirements. Our goal is to ensure that all members of our team can contribute effectively, whether they are working on-site, in a hybrid model, or fully remotely. All offers of employment are contingent upon an individual's ability to secure and maintain the legal right to work at the company and in the specified work location, if applicable.
Belonging at Samsara
At Samsara, we welcome everyone regardless of their background. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender, gender identity, sexual orientation, protected veteran status, disability, age, and other characteristics protected by law. We depend on the unique approaches of our team members to help us solve complex problems and want to ensure that Samsara is a place where people from all backgrounds can make an impact.
Accommodations
Samsara is an inclusive work environment, and we are committed to ensuring equal opportunity in employment for qualified persons with disabilities. Please email [email protected] or click here if you require any reasonable accommodations throughout the recruiting process.
Our Commitment to Authenticity
We use Tofu, a fraud detection tool, to validate the authenticity of applications and protect against identity fraud. This ensures we are connecting with real people and allows us to prioritize genuine candidates. Please see Samsara's Candidate Privacy Notice for more information.
Fraudulent Employment Offers
Samsara is aware of scams involving fake job interviews and offers. Please know we do not charge fees to applicants at any stage of the hiring process. Official communication about your application will only come from emails ending in @samsara.com, @us-greenhouse-mail.io or @mail3.guide.co. For more information regarding fraudulent employment offers, please visit our blog post here.
Salary Context
This $157K-$238K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Samsara, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($198K) sits 7% below the category median. Disclosed range: $157K to $238K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Samsara AI Hiring
Samsara has 2 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer. Positions span Seattle, WA, US, California City, CA, US. Compensation range: $238K - $238K.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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