Разработка вакансии

Разрабатывать и поддерживать бекенд сервисы на Python. Участвовать в разработке архитектуры и концепции продукта.

Обязанности Разрабатывать микросервисы на Go; Проектировать архитектуру новых микросервисов;

SQLAlchemy Тестирование: pytest — обязательное; построение пирамиды тестирования, интеграционные тесты Управление зависимостями: pip / poetry / uv Знание принципов и архитектуры web-сервисов и REST API Проектирование микросервисных решений Уверенная работа с Docker (включая docker-compose);

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.

I am looking for a WordPress developer for outsource project. The goal is just to redesign the old website design into new one.

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).

Location: San Francisco, CA | New York City, NY 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: As a Field Executive Architect, you will serve as Anthropic's senior technical voice with our most strategic customers. You'll engage directly with C-suite executives and senior technical leaders at the world's largest enterprises to understand their business, align stakeholders around a clear vision for how AI fits into their strategy, and create momentum toward adoption of Anthropic's products. This is not a hands-on-keyboard engineering role. Your job is to walk into a room full of executives, quickly understand their business and technical landscape, and help them see a clear path forward with Claude. You'll then partner with Sales and Applied AI teams to ensure that vision translates into execution -- checking in on high-momentum accounts, maintaining executive relationships, and keeping deals on track. Internally, you'll serve as a sponsor for enterprise customer needs, working with Product to ensure that what matters most to our largest accounts is heard and prioritized. You'll bring deep experience in enterprise technology and a track record of operating at the intersection of business strategy and technical execution. Whether you've been a CTO, VP of Engineering, Field CTO, or a world-class senior technical salesperson, what matters is that you've seen how large organizations make technology decisions and can meet executives where they are -- with credibility, clarity, and conviction. Responsibilities: Engage directly with C-suite and senior technical leaders at Anthropic's largest and most strategic accounts, aligning business and technical stakeholders around a clear vision for AI adoption Prepare for and lead high-stakes customer engagements -- executive briefings, EBCs, on-site workshops -- that change the trajectory of key accounts at critical inflection points Partner with Sales and Applied AI teams before and after engagements to game plan approach, debrief on outcomes, and ensure the vision carries through to execution Lean in on high-momentum accounts after initial engagement, maintaining executive relationships, providing strategic guidance to the Sales and Applied AI teams, and sustaining deal momentum Serve as an internal sponsor for enterprise customer needs, translating field feedback into clear priorities for Product and ensuring the most important enterprise requirements are heard and acted on Bridge the gap between our field organization and Product org -- helping each side understand the other's priorities, constraints, and roadmap Travel regularly to customer sites for executive engagements, workshops, and strategic account meetings You may be a good fit if you have: 15+ years of experience in enterprise technology, with significant time spent in senior technical leadership or senior technical sales roles (CTO, VP of Engineering, Chief Architect, Field CTO, or similar) A track record of engaging credibly with C-suite executives and senior technical leaders at large enterprises -- you can hold the room and earn trust quickly Deep understanding of how large organizations evaluate, buy, and scale technology -- you've seen the inside of these decisions from a leadership seat or from the sales side of the table Enough technical depth to understand modern AI systems and their enterprise applications, and enough business acumen to connect technical capabilities to business outcomes Experience translating between technical and business audiences -- you can make complex topics clear and compelling for both Strong opinions about what makes enterprise technology adoption succeed or fail, informed by having led or closely advised these efforts The ability to navigate ambiguity and move fast in a rapidly evolving market -- you're comfortable operating without a detailed playbook and building the plane while flying it A collaborative mindset -- sales at Anthropic is a team sport Excitement about AI's potential to transform how enterprises operate, and a genuine desire to help customers succeed with it Willingness to travel regularly for customer engagements 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: $400,000 — $500,000 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.

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

Location: United States, San Mateo, CA Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. Are you passionate about Responsible AI and ensuring products are built and deployed ethically and safely? Sony Interactive Entertainment is seeking a highly motivated and experienced Senior Responsible AI & Governance Technical Specialist to help ensure our AI products and technologies are developed and deployed ethically, safely, and in alignment with our values. Demonstrable experience in Responsible AI and AI Governance is required. Technical skills are also a must. In this role, you will operate at the intersection of modern AI technology, policy, and ethics. You will design and implement technical governance frameworks, guidelines, and best practices that support responsible AI innovation across the organization. You will identify and assess potential risks, conduct technical and business evaluations, and partner closely with cross-functional stakeholders to embed responsible AI principles into product development lifecycles, adapting to AI-enabled and agentic systems in particular, and identifying where AI-specific guidance or risk mitigations are needed. This is a highly collaborative role working across engineering, product, information security, legal, and business teams to ensure AI initiatives align with regulatory requirements and SIE’s broader strategic goals. The ideal candidate brings a strong understanding of generative AI use cases, technical and sociotechnical risk, and product development, balancing responsible AI commitments with internal and external innovation. If you are passionate about AI Governance and Responsible AI, and have demonstrable experience and skills required for this position, we encourage you to apply. This is an opportunity to build and mature an AI governance program within a fast-evolving landscape. What You’ll Be Doing: Contribute to long-term strategies for building safe, ethical, and innovative AI products and tools Serve as a technical Responsible AI advisor for generative and agentic AI systems, helping teams assess architectures, tool integrations, MCP implementations, data flows, permissions, autonomy, and appropriate human oversight and guardrails Engage stakeholders across the organization to gather feedback and address AI Governance and Responsible AI technical considerations Use AI governance, risk tooling and discovery insights to identify emerging risks and support appropriate remediation and technical solutions Experiment hands-on with emerging AI capabilities, including agents, MCP servers, tool integrations, APIs, and evaluation frameworks, to understand their capabilities and risks and translate findings into practical governance guidance Sit on relevant domain boards as a technical specialist, partnering closely with Product and Engineering Conduct ethical and safety risk assessments, advising teams on mitigation strategies related to bias, fairness, robustness, transparency, and societal impact Partner with engineers, product managers, designers, online safety and more, to proactively identify unintended consequences and emerging risks of AI Coordinate with Information Security and relevant technical teams to align AI governance reviews and risk assessments with security requirements and technical controls Contribute to company-wide best practices for responsible AI development and deployment, including AI literacy materials and case studies What We’re Looking For: At least 3 years of experience in AI Governance, Responsible AI, Corporate Governance, AI safety, AI policy, or a related field, with meaningful experience evaluating AI technologies and systems At minimum, 5 years of experience contributing to or leading cross-functional programs from strategy through operational implementation, partnering with product, engineering, legal, security, and operations teams Strong technical fluency in modern AI systems, including LLMs, agentic architectures, tool use/function calling, APIs, cloud services, and automation Hands-on experience working with or evaluating agentic AI systems; familiarity with MCP or comparable protocols and agent/tool integration frameworks strongly preferred Experience with AI red teaming, adversarial testing, or other technical approaches to identifying risks in LLM and agentic systems Understanding of agentic and sociotechnical AI risks, including excessive agency, permissions and data access, unintended tool use, human oversight, systemic risk, and emergent behaviors Ability to translate technical findings and risks into practical controls, mitigations, and guidance for engineering, product, and business teams Ability to independently drive outcomes across cross-functional teams with minimal supervision Strong analytical and communication skills, sound judgment, and the ability to challenge assumptions and navigate ambiguity in a fast-moving environment Ability and curiosity to stay current with emerging AI capabilities, risks, frameworks, and industry developments, particularly within technology, entertainment, and gaming Preferred Qualifications: Familiarity with relevant regulations, standards and ethical guidelines (e.g., EU AI Act, OECD Principles, NIST AI RMF, ISO/IEC 42001) Proficiency in producing meaningful visualizations and reports, and effectively presenting technical results to diverse audiences Industry experience in tech, gaming or (interactive) entertainment At SIE, we consider several factors when setting each role’s base pay range, including the competitive benchmarking data for the market and geographic location. Please note that the base pay range may vary in line with our hybrid working policy and individual base pay will be determined based on job-related factors which may include knowledge, skills, experience, and location. In addition, this role is eligible for SIE’s top-tier benefits package that includes medical, dental, vision, matching 401(k), paid time off, wellness program and coveted employee discounts for Sony products. This role also may be eligible for a bonus package. Click here to learn more. The estimated base pay range for this role is listed below. $132,800 — $199,200 USD Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.

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.

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.

Задачи: — Разработка и поддержка решений в области финтеха с использованием Python. Требования: — Опыт работы от 5 лет. — Опыт работы с Django, DRF, FastApi. — Плотное взаимодействие с PostgreSQL. — Опыт работы с одним или несколькими из Redis, Clickhouse. — Знание взаимодействия с месседж-брокерами, знакомство с Celery, Kafka или RabbitMQ. — Отличное понимание REST архитектуры. — Понимание работы с асинхронностью (AioHTTP, asyncio). — Умение писать SQL-запросы без использования ORM. — Понимание микросервисной архитектуры. Дополнительно приветствуется: — Знакомство или опыт работы с Oracle DB и SQLAlchemy. — Проектирование или опыт работы с микросервисной архитектурой. — Опыт работы с gRPC. Условия: — Удаленная работа с возможностью гибрида или офиса в Ростове-на-Дону. — Полная занятость. — Тип договора: ИП.

опыт написания операторов/контроллеров для k8s Опыт работы в облаках, хостинге или телекоме Знание систем мониторинга: Prometheus, VictoriaMetrics;

-участие в полном цикле разработки backend (архитектура, кодинг, запуск в промышленный контур). -проектирование и развитие REST API для внешних интеграций.

-проектировать архитектуру приложений: backend, frontend, БД, API и интеграции. -декомпозировать крупные задачи и проекты до понятных этапов и оценивать трудозатраты.

-разработка веб-сервисов с упором на функционал, MVP для новых стартапов. -опыт разработки сложных приложений.

Задачи: — Разрабатывать и развивать высоконагруженные сервисы и платформенные решения на Python — Проектировать и улучшать архитектуру продуктов вместе с командой — Оптимизировать производительность сервисов и автоматизировать процессы разработки — Работать с базами данных, распределёнными системами и современными инструментами разработки — Участвовать в code review, обсуждении технических решений и развитии инженерных практик — Влиять на развитие продуктов на всех этапах — от проектирования до сопровождения Требования: — Знание Python и понимание принципов его работы — Опыт участия в code review — Понимание многопоточности и асинхронного программирования — Умение писать читаемый и поддерживаемый код — Опыт работы с базами данных и понимание принципов их работы — Знакомство с инструментами наблюдаемости: логирование, метрики, трасировка — Понимание принципов ООП, SOLID и основы функционального программирования Преимущества: — Опыт проектирования распределённых систем и работы с Kubernetes — Опыт внедрения AI-решений в продукты или интерес к AI, LLM и RAG — Участие в улучшении процессов разработки и развитии продуктовых решений