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Обязанности: Тестирование мобильных приложений (Android/iOS) в продакшен-окружении: функционал, интеграции, UI. Локализация ошибок, мониторинг продов и составление понятных баг-репортов.

Show contacts Job description Стажер iOS QA Engineer для тестирования приложения «Тренировки» (iOS 26+).

Задачи: — Manual testing of features, bug fixes, and releases throughout the development process — Write, review, and execute test cases — Identify, document, and track bugs — Collaborate with developers and team members to resolve issues — Perform regression and post-release testing — Ensure product quality and consistency with requirements — Use AI tools (e.g., ChatGPT) to assist with testing tasks, writing test cases, and analyzing issues Требования: — Basic practical experience in QA (courses, pet-projects, or commercial) — Understanding of QA fundamentals (test cases, bug lifecycle, etc.) — Strong attention to detail and analytical thinking — English level B2 or higher (reading & writing) — Ability to work independently and in a team Плюсы: — Experience with Shopify — Familiarity with Agile/Scrum — Experience with bug tracking systems (JIRA or similar) Инструменты: — JIRA — Confluence — Google Workspace — Slack — Shopify Условия: — Fully remote, full-time (40h/week, flexible schedule) — Salary paid twice a month — 20 working days paid vacation — 10 paid sick leave days — Opportunities to grow in QA

•••••••••••••• Senior Manual QA Engineer Full time · Senior · Poland Python Linux Bash Important : if an employer asks you to log into their system via iCloud or Google, send a code, an SMS or Telegram password, run some code, or install software — refuse. Show contacts Job description •••••••••• is hiring a Senior Manual QA Engineer.

Location: San Francisco, CA Workplace: Hybrid About Niantic Spatial AI has learned from almost everything online, but it still struggles with the physical world, where 99.5% of economic activity happens. It doesn’t know where things are, how spaces fit together, or what changes over time. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. About the Real-World Test Lab (RWTL) Physical AI doesn't get graded on a leaderboard. It gets graded on a factory floor at shift change, in a substation with no GPS, on a site where the lighting is wrong and the stakes are real. The Real-World Test Lab closes the gap between the benchmark and the field. We bring the customer's world inside our walls — their devices, their environments, their hardest conditions, and their definition of success — and make it the bar every release has to clear. As Niantic Spatial's first and most demanding customer, we push our reconstruction, localization, and spatial understanding to their limits, find where they shine and where they break, and turn that into evidence that shapes what we build next. It's a new team at the frontier of physical AI, and you'll help invent how the job is done. About the Role We're hiring a Hardware Operations Engineer with a robotics specialism, reporting to the Director of the Real-World Test Lab, to own the physical layer of our evidence. You'll manage our fleet of capture devices and robots, engineer the test environments and the instrumentation inside them, support the captures our data programs depend on, and run physical test sessions, including the real-robot side of our sim-to-real work. A physical run only counts as evidence if the hardware is characterized, the environment is controlled, the ground truth is trustworthy, and someone can reproduce the run months later. You'll build what makes that possible: fixtures, ground-truth references, calibration and data-offload tooling, and the integrations that feed physical runs into the same automated evaluation system as everything else. You engineer the conditions, not just the setup. When a physical result didn't match expectations, you've worked out whether the hardware, calibration, environment, or system under test was responsible. Then you built the rig, reference, or logging that kept the question from coming back. You know an uninstrumented physical test is an anecdote, and in the Lab you'll be the person who can say with authority whether a strange result is real. What Success Looks Like - 30 days: Fleet inventory and characterization baseline complete, safe operating procedures defined for current spaces, and first instrumentation gaps identified. - 60 days: First physical robot run executed with ground-truth instrumentation, documented well enough to repeat, with results in the Lab's evaluation system. - 90 days: Test environments in regular use with controlled, documented conditions; capture support running smoothly with our data acquisition team; and the physical side of sim-to-real running on a standing instrumented protocol. What You'll Do - Own and Characterize the Fleet - Manage our capture devices, sensors, and robots end to end: inventory, calibration, firmware, maintenance, logistics, and spares. Characterize what each platform can and cannot do, so a device's limits are a known quantity rather than a surprise inside a result. - Engineer the Physical Test Environments - Design, build, and operate the spaces we test in, covering layout, lighting control, markers, obstacle configurations, and survey-grade ground truth references, across our office, rented or workplace-services-supported space, and partner or customer sites. Make each environment a controlled variable rather than an improvisation. - Build the Instrumentation - Create the rigs, mounts, references, and measurement setups that turn a physical run into quantitative data: trajectory ground truth, positional accuracy references, timing and synchronization across sensors, and automated condition logging. - Integrate Physical Runs Into the Evaluation System - Work with the AI Automation Engineer so physical results flow into the same scorecards and comparisons as software-only evaluations, through automated data offload, run metadata, and structured condition capture rather than a spreadsheet and a folder of files. - Run the Physical Test Sessions - Execute robot runs and device-in-the-loop tests: robot bring-up, teleoperation, scripted trials, and the repetition that makes a result statistically meaningful rather than anecdotal. - Own the Physical Half of Sim-to-Real - When a scenario has been evaluated in simulation, engineer and execute its physical counterpart so the comparison is valid, matching environment, route, lighting, sensor configuration, and robot setup, and instrumenting the run so divergence is explainable rather than mysterious. - Own Safety - Define and maintain safe operating procedures for robots and powered equipment in our test spaces, and coordinate with workplace services, IT, Legal, and site owners on access and safety requirements. - Automate Your Own Work - Script the repetitive parts, including configuration, calibration checks, data offload, and condition logging, so setup time falls and a colleague can run a session without you. What You'll Bring - Hands-on engineering experience with robotic hardware, covering bring-up, integration, teleoperation, troubleshooting, and maintenance. Mobile or wheeled platforms preferred. - Built test rigs, fixtures, or instrumentation that produced quantitative measurements of a physical system's behavior. - Designed and run structured physical experiments, including ground truth methodology, and documented conditions rigorously enough for someone else to reproduce them. - Worked with sensor calibration, multi-sensor synchronization, and coordinate frame conventions, and understand how errors in each propagate into a result. - Strong Python and command-line proficiency, with a track record of automating hardware configuration, data offload, or test execution. - Managed hardware fleets operationally: inventory, firmware, calibration schedules, logistics, and repair coordination. - Practical safety experience operating powered equipment or robots around people. - A bachelor's degree in a relevant field, or equivalent experience. Nice to Have - Worked with ROS or ROS 2, including writing nodes or integrating sensor drivers. - Operated NVIDIA Isaac Sim or Isaac Lab as a user, running scenarios someone else authored. - Worked with professional capture hardware such as terrestrial laser scanners, NavVis or Leica systems, LiDAR, 360 cameras, or drones. - Established survey-grade or motion-capture ground truth for localization or navigation evaluation. - Supported photogrammetry, Gaussian splat, or 3D reconstruction capture workflows. - Run test operations at customer or partner sites, with the coordination and discretion that requires. Competencies - Engineering judgment about physical systems. You reason about error sources: calibration drift, timing offsets, mounting rigidity, lighting variation, floor surface. When results disagree, you form a hypothesis and design the measurement that settles it. - Hands-on and unfazed. You'd rather be in the space with the robot than reading about it. Cables, mounts, batteries, firmware, and a session that starts at 7am are all part of the job. - Meticulous about conditions. You instrument rather than remember. You know the value of a physical test lives in how precisely its conditions were captured. - Safety-minded without being slow. You take real responsibility for operating robots around people, and you build procedures where the safe path is also the fast path. - Builds for other people. Your fixtures, scripts, and procedures get used by colleagues without you in the room. - Comfortable in an unfinished environment. Much of this does not exist yet. You'll be selecting equipment, designing the first rigs, and defining how a space gets configured, with support but without a playbook. Compensation & Benefits Base salary range of $142,200 to $193,000. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more. Location & Work Model This role is based in our San Francisco office, with three days per week in office. Inclusive Application We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed - you may be exactly who we're looking for. Equal Opportunity Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. Candidate Privacy I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Location: San Francisco, CA Workplace: Hybrid About Niantic Spatial AI has learned from almost everything online, but it still struggles with the physical world, where 99.5% of economic activity happens. It doesn’t know where things are, how spaces fit together, or what changes over time. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. About the Real-World Test Lab Physical AI doesn't get graded on a leaderboard. It gets graded on a factory floor at shift change, in a substation with no GPS, on a site where the lighting is wrong and the stakes are real. The Real-World Test Lab closes the gap between the benchmark and the field. We bring the customer's world inside our walls - their devices, their environments, their hardest conditions, and their definition of success - and make it the bar every release has to clear. As Niantic Spatial's first and most demanding customer, we push our reconstruction, localization, and spatial understanding to their limits, find where they shine and where they break, and turn that into evidence that shapes what we build next. It's a new team at the frontier of physical AI, and you'll help invent how the job is done. About the Role We're hiring an AI Automation Engineer, reporting to the Director of the Real-World Test Lab, to build the system that produces our evidence. Today our evaluations are manual, inconsistent, and slow. You'll design and own the automation that runs them end to end on every relevant release, with no human driving it, turning one-off experiments into an always-on service the whole company relies on. This role is about owning the evidence, not executing a test plan. You'll decide how each workflow gets exercised, and you'll be measured on whether the company can trust the results, not on how much automation exists. The work counts when it keeps running correctly months later, without you in the loop. You believe evaluation is engineering, not process. You've built systems that test other systems, and you know the difference between a script that works on your laptop and infrastructure a team can trust. When a result couldn't be reproduced, you fixed the tooling instead of arguing about the number. What You'll Do - Build the Evaluation Machine - Own the automation that executes Lab evaluations end to end: environment setup, run orchestration, artifact capture, and result collection. Make reruns free so we test constantly rather than occasionally. - Make Results Comparable - Instrument scorecards so a result can be compared across product versions, devices, and capture conditions. A number without its lineage is not evidence. - Automate the Agent-Driven Layer - Build the agent workflows that exercise priority customer use cases at realistic scale, across the graded difficulty spectrum from easy to frontier, and be honest about where agents cannot yet replace human judgment. - Kill Manual Work Permanently - Convert one-off experiments into standing protocols that run on every relevant release. Automate recurring inspection wherever it can be automated, and route what genuinely cannot to scalable human review. - Make Failures Actionable - Produce diagnostics precise enough that a finding reaches its owner with a reproducible case and data attached. Findings that need re-investigation before anyone can act on them are half-finished. - Keep Data From Being the Bottleneck - Work with the AI Data Manager so every run is reproducible from a known dataset state, without anyone downloading and re-uploading data by hand. What Success Looks Like - 30 days: One priority workflow evaluated end to end with no manual steps, with results in a comparable scorecard. - 60 days: Evaluations trigger automatically on relevant releases, with diagnostics that route failures to the right owners. - 90 days: Three priority workflows under standing automated evaluation, with version-over-version comparison for leadership. What You'll Bring - Built and maintained production-grade automation or test infrastructure that other engineers relied on daily. - Experience evaluating systems where correctness is graded rather than binary, meaning quality, accuracy, or latency thresholds rather than pass/fail assertions. - Strong Python, with fluency in orchestration, CI-style pipelines, cloud storage, and reproducible environments. - Worked with backend services and APIs you did not own, integrating against them without becoming a bottleneck for their team. - Written up a technical finding clearly enough that a non-author could act on it without a meeting. - A bachelor's degree in a relevant field, or equivalent experience. Nice to Have - Built agent-based or LLM-driven automation for a task that previously required human judgment. - Worked on evaluation or benchmarking for computer vision, 3D reconstruction, or spatial systems. - Instrumented dashboards or scorecards that leadership used for release decisions. - Operated data provisioning or registry infrastructure across multiple environments and access models. Competencies - Intellectually honest. You design tests that produce answers people can trust, and you refuse to let a benchmark imply more than the evidence supports — including when the honest answer is inconvenient for your own work. - Pragmatic. You find the fastest path to a defensible result without going through one-way doors that undermine scaling. You have no patience for building infrastructure nobody has asked for yet. - AI-forward by instinct. You reach for agents and LLM-driven automation before you reach for headcount, and you're rigorous about verifying what they produce. - Builds for other people. You've maintained something after the initial excitement wore off. Your tooling is documented, your failures are legible, and colleagues use what you build without asking you to run it for them. Compensation & Benefits Base salary range of $158,400 to $210,000 per year. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more. Location & Work Model This role is based in our San Francisco office, with three days per week in office. Inclusive Application We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed - you may be exactly who we're looking for. Equal Opportunity Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. Candidate Privacy I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Location: Singapore Workplace: Remote We are currently growing our support team at ClickHouse who provides excellent service to our customers around the world. We are seeking a Senior Technical Customer Support Engineer to join our global Support Engineering team. We hope to find you ready to take on a large variety of tasks related to our customers locally in Singapore, as well as more regionally across APJ, APAC, and EMEA. This is a customer-facing role that will include customer interactions via chat, calls, Slack messages, and virtual meetings over video. You will be owning a wide variety of tasks: from triaging support tickets to user community support, helping with both pre- and post- sales customer activities, partnering with our Go To Market team with deeply technical aspects of POCs, and suggesting improvements to how we do things in Support at ClickHouse. This is a great opportunity for someone ready to roll-up their sleeves and help us continue to provide excellent customer support to our rapidly growing user base. Note: This is a remote position, but candidates must be located in Singapore. What you will be doing: - Supporting and guiding our ClickHouse users, customers, and prospects via cases, chat, Slack, community, and meetings - Develop solutions based on ClickHouse Cloud and ClickHouse open-source that can be shared with our users, community, and customers via documentation, knowledge base, blogs, meetups, webinars, and training - Work closely with our global Support Services, Engineering, Go to Market, and Product Management teams to help define functionality required by users and customers - Assist with mentoring, training, and sharing your knowledge with colleagues, users, and customers  And of course: - You will deliver excellent customer service as a first-line technical engineer and representative of ClickHouse. Our Support Engineers provide professional response, on-call coverage, and guidance within the required Service Level Agreements ("SLAs") on technical cases that are opened via a ticketing system, email, Slack, chat, and/or phone - You will build strong, trusted relationships with colleagues, customers, and partners What you bring along: - Technical breadth and depth in ClickHouse open-source or ClickHouse Cloud, or in domains relevant to ClickHouse, such as: SQL databases, OLAP, cloud-native SaaS, distributed systems - Previous technical experience in roles such as Support Engineer, Consultant, Database Administrator, Site Reliability Engineer, Solutions Engineer, Software Engineer, and/or Systems Engineer - Be present and available according to the scheduling required to deliver high-quality 24x7 Support in a global, distributed environment - Strong written and verbal English communication skills and the ability to work fully remote with reliable connectivity - A mindset of teamwork, global engagement, empathy, and solving challenging problems - A sense of adventure and urgency in building the most scalable, high-performing, largest, and fastest databases on the planet - The ability to build trusted relationships with colleagues, customers, and partners - You are self-driven, curious, and eager to continuously learn and grow Bonus points: - Experience with ClickHouse - Experience with OSS and open-source technologies, as a user, community member, or contributor - Experience with Azure, GCP or AWS - Experience with data pipelines such as Kafka, Kinesis, Spark, RabbitMQ, or others PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new team member who joins our company receives stock options. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f4-2129-488d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.

The role involves building production GenAI systems, RAG pipelines, recommendation/personalization models, and scalable ML infrastructure from development through deployment.

• Коммерческий опыт в DevOps или SRE от 4 лет. • Реальный опыт проектирования и построения инфраструктуры с нуля.

• Практический опыт работы с Docker/docker-compose; • Опыт с Qdrant, REST/gRPC и Qdrant Cloud;

We're looking for a founding engineer to build a fintech product where a bug costs real money 🚀. • Daily work with AI coding agents (Claude Code, Codex, Cursor).

Requirements - Masters degree in Electrical Engineering, Electronics Technology, Electrical Engineering Technology, Electrical and Computer Engineering or related field.

Develop and maintain Terraform/OpenTofu modules for vSphere VM provisioning, placement/resource-pool configuration, and cloud-init/cloudbase-init integration, following designs defined by the VMware Architect. Create and run Ansible playbooks for post-provisioning configuration handoff on VMware-provisioned VMs.

You will lead and execute core 0-to-1 initiatives in cutting-edge domains such as Agentic AI. You will translate high-level strategic vision into concrete execution roadmaps, driving rapid implementation from prototype to production.

Implement and maintain firewall automation modules across PaloAlto, F5, and Cloudflare (security rules, address/service objects, NAT, VIP/pool configuration, WAF policy, certificate binding), following designs defined by the Network Architect. 3+ years of hands-on experience in network engineering with a focus on automation.

Location: Boston, MA Workplace: OnSite At WHOOP, we're on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. WHOOP is hiring a Staff Hardware Test Engineer (Manufacturing) to lead manufacturing test strategy and deliver scalable test solutions that ensure every WHOOP device meets the highest standards of quality and reliability. You will take ownership of complex manufacturing test challenges from high-level objectives through strategy, execution, and production outcomes. Working across Manufacturing Engineering, Electrical Engineering, Firmware, Mechanical Engineering, Quality, and Reliability, you will drive alignment on test strategy, influence product and manufacturing decisions, and deliver measurable improvements in test coverage, quality, yield, cycle time, and cost. You will also establish best practices, raise engineering standards, and mentor engineers across the team.   RESPONSIBILITIES: - Lead the architecture, development, validation, and deployment of automated test equipment (ATE), electrical test fixtures, and control systems for engineering validation, NPI, and mass production. - Develop and establish scalable automated test methodologies and applications using Python to validate electrical, functional, RF, and sensor performance on manufacturing lines. - Drive cross-functional alignment across Hardware, Firmware, Manufacturing, and Quality engineering teams to make tradeoffs around test coverage, product quality, cycle time, yield, cost, and schedule. - Lead complex and ambiguous manufacturing test investigations, structuring problems, identifying technical and operational risks, and driving cross-functional teams to root cause and resolution. - Own manufacturing test readiness and outcomes through factory bring-up and production ramp with manufacturing partners, including fixture qualification, calibration, correlation, troubleshooting, and continuous improvement. - Analyze manufacturing and test data to identify systemic opportunities and deliver measurable improvements in yield, process capability, test coverage, quality, cycle time, and cost. - Establish manufacturing test best practices, methodologies, and standards that improve execution quality and scalability across programs. - Identify technical and execution risks proactively and drive mitigation strategies with engineering teams and manufacturing partners. - Contribute to product and manufacturing roadmaps by identifying test technology, infrastructure, and capability investments required for future products. - Mentor engineers and provide technical guidance on test architecture, debugging, automation, and manufacturing test methodologies. QUALIFICATIONS: - Bachelor's degree in Electrical Engineering, Computer Engineering, or a related technical discipline. - 10+ years of experience developing hardware test systems, automated test equipment, or manufacturing test solutions for high-volume consumer electronics or similarly complex products. - Demonstrated experience independently owning manufacturing test strategy and execution from product development through NPI, ramp, and mass production. - Proven ability to lead complex, ambiguous technical initiatives and drive outcomes across multidisciplinary engineering and operations teams. - Deep understanding of electronics, instrumentation, measurement systems, and communication protocols such as UART, I²C, SPI, USB, Bluetooth, and NFC. - Experience designing electronic circuits and PCBAs using tools such as Altium Designer. - Strong proficiency in Python for developing automated manufacturing test applications; experience with C/C++ or other systems languages is a plus. - Extensive experience designing, validating, and debugging manufacturing test fixtures, instrumentation, and production hardware. - Strong understanding of manufacturing test methodologies and data analysis, including test coverage, calibration, correlation, GR&R, process capability, yield analysis, and failure analysis. - Extensive experience working with CMs and JDMs, including factory bring-up, NPI, production ramp, and sustaining support. - Demonstrated ability to influence technical decisions, align stakeholders, and communicate effectively across engineering, operations, and leadership. - Track record of delivering measurable improvements in manufacturing quality, yield, efficiency, test coverage, or cost. - Experience mentoring engineers and improving engineering practices within a team. - Ability to travel up to 15%, both domestically and internationally. - Strong commitment to leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions. This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office. Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply. WHOOP is an Equal Opportunity Employer and participates in E-verify https://www.e-verify.gov/ to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. The WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values. At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long-term growth and success. The U.S. base salary range for this full-time position is $180,000.00 - $215,000.00 Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training. In addition to the base salary, the successful candidate will also receive benefits and a generous equity package. These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements. Learn more about WHOOP https://www.whoop.com/us/en/careers/?srsltid=AfmBOopKmph9d0DLBlogZu8mx6do0dzjNS8eJlfc4PQqQtdU6F8DGKBg.

Location: San Francisco, CA | New York City, NY | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Data Center Security Engineering (DCSE) owns physical security across Anthropic's data center footprint: the sites, the hardware inside them, and the supply chain that delivers it. The Security Fusion Platform is where physical security signals from across that footprint are correlated and put in front of the analysts who act on them. It carries continuous monitoring, audit evidence, metrics, and analyst response, along with the classification and analytics pipelines that turn sensor data into events. You'll build and own the platform analysts work from. This is a foundational role. You'll build the software platform that carries a signal from source to analyst decision, ship its first versions, and keep them running in production. The sensors and systems at each site belong to other DCSE engineers; you define the event contract their owners deliver to, and you build the classification and analytics pipelines that run over what those sources produce. Physical security systems engineers own the operations spaces; you own the software that runs in them. You'll work alongside engineers focused on the sites themselves. What you lead, you own end to end: the design, the technical approach, and the result in production. You'll work with external vendors and integrators, setting requirements and acceptance criteria for what you own and deciding what the team builds itself. We use Claude throughout our engineering workflow and expect you to as well. This role involves regular travel to data center sites. No security clearance is required. These are critical services, and you'll take part in on-call for them. Key responsibilities Design and build the event bus and normalized event schema: source identity, sequencing, health state, and the adapter contract that owners of each sensor and system feed build to, plus adapters where needed Build correlation and alerting: the rules and services that turn events from independent sources into alerts an analyst can act on Build the classification and analytics pipelines: computer vision and multi-sensor fusion, with results landing on the event bus Build analyst surfaces: the queue, escalation tooling, dashboards, camera viewers, and video wall content a 24/7 watch works from, plus video analytics integration so camera streams arrive on the bus as events Deliver audit evidence: durable, tamper-evident event history, evidence exports, and the retention and access controls around them Instrument metrics and self-monitoring: detection rates, nuisance ratios, alert aging, time to resolve; heartbeats and loss-of-telemetry alarms on every feed, tested fail-secure behavior, and change audit on every rule Deploy across sites: site-local components, central services, and the sync that keeps configuration, rules, and state consistent, then commission each site and hand over to the analysts Specify the compute the platform runs on (servers, storage, and the platform's network segment) to the team's segmentation and hardening baseline Write requirements and acceptance criteria for what vendors and integrators deliver, review deliverables against them, and make the call on accepting or rejecting the work Minimum qualifications Have built and run event-driven backend systems in production Are proficient in at least one backend language such as Python, Go, or Rust, and have worked with a message bus or event streaming system such as Kafka Have owned a system end to end: set the design, made the tradeoffs, shipped it, and answered for it in production Have integrated with external systems you did not control (vendor APIs, on-prem appliances, video platforms, building systems) and designed for a life beyond any one vendor Have directed vendor or integrator work: written requirements and acceptance criteria, reviewed deliverables against them, and decided what gets accepted Design for the failure you won't see coming (a silent feed, a lost network path, a replayed event, a de-tuned rule), know how your system behaves when a source lies or goes quiet, and test for it Build tools for people who use them under pressure and iterate with those operators Preferred qualifications Experience building or operating a security operations platform (SOC, physical security operations, or the detection engineering behind one) Experience building ML or computer vision inference pipelines over video and sensor streams PSIM (physical security information management) or VMS (video management system) integration experience, or familiarity with physical access control and building systems data Experience producing evidence for auditors or assessors: log integrity, retention, chain of custody Experience using AI-assisted development tooling as a core part of your workflow The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $405,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.