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Location: AU: Sydney (45 Clarence St); AU: Melbourne: (260 Burwood Rd); NZ: Auckland: Xero 4 (96 St Georges Bay Rd, Level 2 & 3); NZ: Wellington: Xero One (19-23 Taranaki St) Workplace: Hybrid The role You will lead the design and implementation of large-scale, production-grade distributed systems that power AI features for millions of daily users. You'll own the architecture decisions that keep our systems flexible, cost-effective, and robust, direct strategy for distributed systems, and manage technical debt across the AI Products estate. Beyond architecture, your impact lies in lifting the technical capability of the entire AI Products team. You will champion engineering excellence, mentor junior engineers, and collaborate across Xero to enhance data usability - applying modern AI research, including Large Language Models, only once it can be engineered into reliable, production-grade systems. The team You will join the AI Products group, a diverse team of scientists, engineers, product managers, and analysts within our broader Data & Science division. As a Machine Learning Engineer, you'll partner closely with Applied Scientists to build the interfaces and harnesses that safely and reliably transition models from research into production. Together, this collaborative team reduces toil and delivers beautiful, data-driven insights for small businesses. The team is currently working on - Production ML and AI engineering: Building and operating the platforms, services and practices that support traditional ML models, generative AI and agentic systems in production. - JAX (Just Ask Xero): Powering Xero's conversational GenAI assistant and evolving the underlying capabilities to support increasingly sophisticated, context-aware and agentic experiences. - Intelligent financial workflows: Developing capabilities such as Automated Bank Reconciliation and smart document capture, using ML and AI to match transactions, extract information from bills and receipts, and automate complex tasks. - XeroForce: Building the platform for AI-powered workflows, enabling agents to plan and execute complex tasks across Xero’s products and supporting reliable, reusable agentic experiences at scale. - End-to-end AI product experiences: Integrating AI into customer-facing experiences and product workflows, alongside the backend services, orchestration and infrastructure that make them work.   Where and how you can work Xero offers a flexible hybrid working model designed to blend the collaboration of office life with the autonomy of remote work. You will have access to our modern office spaces, with expectations around office days and collaborative 'boost days' aligned to help your team connect and ship great code effectively. Here are some of the things we are looking for - 5+ years building and operating production Python (or equivalent language) services at scale - this is a software engineering role first; strong system design and coding proficiency are non-negotiable - A track record of owning services or pipelines in production, including operational/on-call responsibility, incident response, and managing technical debt over time - Deep understanding of distributed processing principles (Spark, Dask, or similar) alongside strong SQL capabilities - Demonstrated experience integrating ML models or LLM-based features into production systems - you don't need a research background, but you should be comfortable working alongside Applied Scientists to productionize their work - Familiarity with ML tooling such as MLFlow, TensorFlow, or PyTorch, and data orchestration tools like Airflow or Prefect, is valued but production engineering depth matters more than research exposure - Nice to have: prior experience applying or fine-tuning LLMs in a product context, though this is not a substitute for the core software engineering bar above Apply even if your experience isn't a perfect match! At Xero, we hire based on your skills, passion, and the unique perspective you can bring to enhance our culture and team.

Location: 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 Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. 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 — $485,000 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.

Location: 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 Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. 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 — $485,000 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.

Location: Limassol, Cyprus We are looking for a PSP Operations Specialist to help manage day-to-day payment operations and resolve payment-related issues. You will work closely with our Head of PSP, Support team, developers, and payment providers to investigate failed transactions, coordinate issue resolution, and ensure smooth payment processing. This is a great opportunity for someone with initial experience in fintech, payments, or customer support who wants to develop their expertise in payment operations. Key Responsibilities Monitor payment transactions and investigate failed, declined, or delayed payments. Help the Support team resolve payment-related questions and customer complaints. Check transaction statuses, payment logs, and provider responses to identify potential issues. Communicate with payment providers to investigate problems and follow up on their resolution. Coordinate with developers and other teams to resolve payment-related issues. Track open requests, provide status updates, and escalate complex or critical incidents. Identify recurring payment issues and share findings with the team. Assist with payment performance monitoring, operational reports, and internal documentation. Support operational tasks related to new merchants and payment methods. Manage relationships with Service Providers Requirements Around 1 year of experience in fintech, payment operations, customer support, or a similar role. Basic understanding of payment processing and willingness to learn about PSPs and payment methods. Good analytical skills and attention to detail. Comfortable working with Excel or Google Sheets. Good communication skills and ability to coordinate with different teams. Proactive approach to problem-solving and ability to follow tasks through to completion. Fluent in Russian and strong written English skills for day-to-day communication with international payment providers and partners. Nice to Have Experience in a fintech company or payment service. Basic knowledge of card payments, APMs, or local payment methods. Familiarity with transaction statuses, decline codes, or payment logs. Experience with CRM, ticketing systems, or operational tools. What we offer Full relocation support and working visa sponsorship Competitive salary Days off when you need them What awaits you Working in a small and fast-paced Scrum team A practical role where you can influence how we work with data No bureaucracy - we focus on practical solutions and common sense Pleasant colleagues and a healthy internal culture A non-toxic environment with respect for work-life balance (we turn off Slack during vacations) You don't need to know everything about payment operations from day one. We value curiosity, attention to detail, and a willingness to learn.

Location: Paris, France Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs. About the client Our client is a leading player in the online gaming and betting industry, operating one of the largest poker and sports betting platforms in Europe. Their platform serves hundreds of thousands of active users monthly and relies on highly scalable, real-time distributed systems designed to handle massive traffic volumes with high availability and low latency. The company combines strong technical excellence with a fast-moving product environment and offers engineers the opportunity to work on complex distributed architecture challenges at scale. About the Role We are looking for an experienced Senior Backend Engineer to join the Poker engineering team and contribute to the d e sign, development , and optimization of highly scalable microservices powering a large-scale real-time gaming platform. You will work on distributed, event-driven systems handling high-throughput traffic and real-time messaging workloads, while collaborating closely with infrastructure, frontend, data, and security teams. This role is ideal for engineers passionate about backend architecture, scalability, distributed systems, and high-performance platforms. Contract : PERMANENT - CDI Model : Onsite, 4 days week! Location : Paris, France (75007) Hiring Process: 4 interviews - HR screening, 2 Technical Interviews, Final Meeting and Offer Discussion NOTE: high preference for candidates who attended CPGE scientifiques — Classes Préparatoires aux Grandes Écoles scientifiques. Key Responsibilities Design and evolve the architecture of distributed backend systems Develop highly available and scalable microservices Build and maintain APIs and data models Work on real-time event-driven systems with high throughput requirements Implement unit, integration, and end-to-end testing Participate in code reviews and technical discussions Deploy and maintain services in AWS environments Monitor, troubleshoot, and optimize platform performance Contribute to architecture and technical documentation Collaborate with Frontend, Data, Infrastructure, Security, and Product teams Requirements Significant experience in developing back-end services on a high-traffic platform (10K+ req/s) Significant experience with load balancing, high availability, scaling, caching, database, and storage issues Proficiency in event-driven distributed architectures Proficiency in Redis or Kafka (data structures, pub/sub, streams, pipelines) Proficiency in relational databases (MySQL or PostgreSQL), modeling, optimization, transactions, and locks Knowledge of JavaScript and the Node.js ecosystem is desirable (microservices rely on Node.js) Knowledge of the cloud environment DevOps culture (CI/CD, Docker, Observability) Mandatory academic background from CPGE scientifiques (Maths Sup / Maths Spé) Benefits Permanent full-time contract (CDI) 4-day work week Profit-sharing and participation programs Company restaurant On-site gym facilities Long-term growth opportunities Modern engineering culture focused on scalability and innovation

Location: Lisbon, Portugal; Warsaw, Poland; Madrid, Spain; Prague, Czechia CloudLinux is a global remote-first company. We are driven by our principles: do the right thing, employees first, we are remote first, and we deliver high-volume, low-cost Linux infrastructure and security products that help companies to increase the efficiency of their operations. Every person on our team supports each other and does what we can to ensure we all are successful. Check out our website for more information: cloudlinux.com We are currently hiring a Staff Security Engineer within the Imunify360 product. Imunify360 is CloudLinux's security suite for Linux web servers. It protects hundreds of thousands of hosting servers and the websites running on them, so it sees most large-scale web attacks early — often in the first hours. Check out our website for more information: https://imunify360.com/ Role Mission & Scope At Imunify360, protecting customers from evolving cyber threats requires close collaboration between security research, engineering, and other teams. Responding to complex attack campaigns involves more than identifying threats — it requires understanding attack techniques, developing effective detection and mitigation strategies, and ensuring customers are protected. As a Staff Security Engineer , you'll take end-to-end technical ownership of complex security challenges, from initial threat discovery and analysis to detection, mitigation, and post-incident review. This is a hands-on technical leadership role focused on practical security engineering rather than SOC monitoring, compliance, or purely theoretical research. You'll analyze malware and exploit code, develop detection and mitigation tools, anticipate emerging attack techniques, and ensure security findings translate into production-ready detections, mitigations, and fixes. You'll work closely with engineering, security research, support, external partners, and the broader security community to coordinate responses and drive continuous improvements in our security capabilities. As an individual contributor , you won't have direct reports or people management responsibilities. All security research and testing are conducted within authorized boundaries, with clearly defined scope and responsible disclosure practices. Your mission is to strengthen Imunify360's ability to detect, prevent, and respond to emerging threats, delivering measurable security improvements that protect customers at scale. What you will own Malware and campaign mitigation Lead response to large-scale exploitation and malware campaigns: triage, root cause, and mitigation across every protection layer of the product. Reverse-engineer malware and exploits targeting Linux servers and web applications. Track campaigns over time, not just individual samples: cluster indicators, connect activity to known actors and toolkits, anticipate the next variant. Define and run measurable outcomes per campaign: time to detect, time to protect, cleanup effectiveness, reinfection rate. Security response coordination Own the security incident and response process, both for threats against our customers and for vulnerabilities in our own product: severity model, runbooks, communication, escalation. Coordinate with external parties — software vendors, partners, CERTs/CSIRTs, abuse desks, other security vendors. Drive responsible disclosure where we find vulnerabilities. Be the technical voice in security advisories and partner briefings, together with Product and Support. Triage external vulnerability reports. Offensive security on our own product Red-team our product and infrastructure. Assume a capable attacker who already has a foothold. Threat-model new features before they ship; own the security review gate for high-risk changes. Turn findings into fixes with the owning teams, not just reports. Detection engineering and tooling Raise the quality bar of detections: false-positive budgets, test corpora, staged rollout with measurement gates and fast rollback. Build and automate the analysis pipeline: dynamic analysis, honeypots, telemetry-driven hunting, ML-assisted triage where it measurably helps. Write production code for these tools; expect 30–50% of your time to be hands-on building. Turn telemetry into intelligence: emerging exploits, attacker infrastructure, trends — shared as internal briefs and selected public research. People multiplier (no direct reports) Mentor analysts and engineers; raise the security skill level across teams. Bar-raiser in security hiring loops. Key Milestones 30 days: Analyzed real threats end-to-end. Mapped the current response flow and where it breaks. Shipped at least one detection or mitigation to production. 90 days: Security response process and severity model published and agreed. Led at least one real campaign response with a written post-mortem. Baseline response metrics in place. 6 months: Time from first sighting to protection measurably reduced vs. baseline. First red-team review completed, top findings fixed. Working relationships with key external partners. 12 months: Recognized internally and externally as the owner of security response. Public research or disclosures published. Teams can run a standard campaign response without you. Requirements Must have 10+ years in security engineering, malware research, incident response, or offensive security, with several years leading cross-team work at Staff/Principal/Lead scope. Deep hands-on malware analysis and reverse engineering skills. Strong web application security and practical exploitation experience. Deep Linux knowledge from both the attacker's and the defender's side. Proven incident response leadership on large-scale events: you have run the response, written the advisory, and closed the post-mortem. Production coding in Python plus a systems language (Go, Rust or C). Experience with coordinated vulnerability disclosure and working with the security community. Clear, short writing for both engineers and non-technical stakeholders; works well async across time zones. Daily user of AI tools for analysis and coding, with critical judgment about their output. Nice to have Background in hosting or large web ecosystems. Detection authoring at scale (signatures, WAF/IDS rules). Kernel-level monitoring and rootkit detection. Threat intelligence practice and actor tracking. ML for malware classification. Public track record: CVEs, published research, talks, CTFs, open-source security tools. Relevant certifications are a plus, not a requirement. Benefits What's in it for you? Stay hands-on technically while owning complex, high-impact engineering work. Work on challenging Linux, automation, and cybersecurity problems across mature and newer products. Influence technical direction and make meaningful architectural decisions. Work independently with a high level of ownership and autonomy. Fully remote work with flexible working hours. 24 paid vacation days, 10 national holidays, and unlimited sick leave. Private medical insurance compensation. Co-working and gym/sports reimbursement. Education budget. Opportunity to receive a reward for innovative ideas that the company can patent. By applying for this position, you consent to the processing of your personal data as described in our Privacy Policy ( https://cloudlinux.com/candidate-privacy-notice ), which provides detailed information on how we maintain and handle your data.

Location: Prague, Czechia; Lisbon, Portugal; Madrid, Spain; Warsaw, Poland CloudLinux is a global remote-first company. We are driven by our principles: do the right thing, employees first, we are remote first, and we deliver high-volume, low-cost Linux infrastructure and security products that help companies to increase the efficiency of their operations. Every person on our team supports each other and does what we can to ensure we all are successful. Check out our website for more information: cloudlinux.com We are currently hiring Staff Engineers for various projects within the Imunify360 product. Imunify360 is CloudLinux's multi-layer security suite for Linux web servers: antivirus and malware cleanup, WAF, IDS/IPS, Proactive Defense, patch management (KernelCare/Patchman), and IP/domain reputation. It runs on hundreds of thousands of shared, VPS and dedicated hosting servers, under cPanel, Plesk, DirectAdmin and standalone. The engineering org is 60+ engineers in squads covering backend, core, server agent, malware research, WAF/WebShield, and Patchman. Core services are Python; agents and performance-critical paths are Go and Rust. We run on Hetzner and our own bare metal. Check out our website for more information: https://imunify360.com/ Role Mission & Scope Most complex technical challenges in Imunify360 go beyond individual squads. A single detection change can impact the scanner, agent, UI, reputation backend, and release process. While squad leads own delivery within their teams, the Staff Engineer ensures technical alignment across the product. As a Staff Engineer, you'll be a hands-on technical leader working across the entire product , focusing on the most critical and high-impact challenges as priorities evolve. You'll drive technical decisions, improve architecture, establish engineering standards, and help teams build sustainable solutions. This is an individual contributor role , without direct people management responsibilities. You'll remain actively involved in development, spending approximately 30–50% of your time writing production code , rather than focusing solely on architectural design. While deep expertise in a particular area is valued, this role requires the flexibility to work across different components, collaborate with multiple squads, and tackle challenges wherever your expertise can make the greatest impact. What you will own Architecture and technical direction Own the end-to-end architecture of Imunify360 as a product: agent ↔ server ↔ cloud backends, data flows, and the contracts between them. Write and drive ADRs and RFCs for cross-squad changes. Run the Wed/Fri architecture sync together with the Technical Director. Keep a living tech-debt and risk register (security / compliance / uptime) with owners and dates, and get the top items funded on the roadmap. Hard cross-cutting problems (examples of what lands on your desk) Agent resource footprint on dense shared hosts: CPU, memory and I/O budgets for scanning, file monitoring (inotify/fanotify) and WAF, measured on real kernels, not benchmarks from elsewhere. Detection pipeline quality: false positive / false negative rates, signature and ML model rollout with staged gates and rollback. Upgrade and release safety across a large, heterogeneous fleet (CentOS/AlmaLinux/CloudLinux/Ubuntu, multiple control panels, old kernels). Backend scale and cost of reputation, ThreatIntel and telemetry services. Migrating hot paths from Python to Go/Rust where the data shows it pays off, and saying no where it doesn't. Engineering standards Set the bar for design reviews, code review, testing strategy, observability (Prometheus/Grafana/Sentry) and secure SDLC (OWASP ASVS, CIS Benchmarks as relevant). Lead or co-lead SEV1/SEV2 incidents when the cause is unclear or cross-squad; make sure the blameless post-mortem ships within 24 h and its actions get closed. Drive the AI-augmented SDLC in practice: show what good agentic coding looks like (prompt specificity, context provision, review quality, tool boundaries) on real changes, not slides. People multiplier (no direct reports) Mentor senior engineers and squad leads toward staff-level scope. Act as the technical bar-raiser in hiring loops for senior and lead roles. Be the engineering voice in product and sales discussions: estimates, feasibility, and no dated customer commitment without a written squad-lead estimate. Key Milestones 30 days: Shipped at least one non-trivial change to production. Can explain the full request path from agent to backend. Has a personal list of top 10 technical risks. 90 days: Owns one cross-squad initiative with an approved ADR, a measured baseline, and a staged rollout plan. Published risk register reviewed in arch sync. 6 months: That initiative is in production with before/after metrics. At least one engineering standard (e.g. release gating, agent perf budget, design review template) is adopted by 3+ squads. 12 months: Recognized by squad leads as the default escalation point for cross-cutting technical decisions. At least one senior engineer grown into broader scope with your mentorship. Requirements Must have 10+ years building and operating production software, with 3+ years in a Staff/Principal/Tech Lead scope spanning several teams. Strong production experience in Python and at least one of Go or Rust . Deep Linux internals : processes, filesystems, networking, kernel interfaces (inotify/fanotify, eBPF or similar), packaging (RPM/DEB), systemd. Track record of designing and running distributed systems at scale: async backends, queues, databases, caching, failure modes. You have owned systems that run on customer machines (agents, daemons, on-prem software) and know what upgrade safety, backward compatibility and resource budgets mean there. Security mindset: threat modeling, secure coding, handling of untrusted input (files, HTTP traffic, user content). Writes clear, short technical documents and drives decisions in async, remote teams across time zones. Uses AI coding tools (Cursor, Claude, MCP-based tooling) daily and can judge their output critically. Nice to have Experience in server security: AV/malware detection, WAF (ModSecurity/Coraza), IDS/IPS, rootkit detection, CVE/patch management. Experience with WordPress and Node.JS web application hosting at scale ML in production for classification/detection, including evaluation and safe model rollout. Observability at scale: Prometheus, Grafana, Sentry, ClickHouse or similar. CI/CD ownership: GitLab CI, Jenkins, build systems for multi-distro packaging. Open-source contributions in security or systems tooling. Benefits What's in it for you? Stay hands-on technically while owning complex, high-impact engineering work. Work on challenging Linux, automation, and cybersecurity problems across mature and newer products. Influence technical direction and make meaningful architectural decisions. Work independently with a high level of ownership and autonomy. Fully remote work with flexible working hours. 24 paid vacation days, 10 national holidays, and unlimited sick leave. Private medical insurance compensation. Co-working and gym/sports reimbursement. Education budget. Opportunity to receive a reward for innovative ideas that the company can patent. By applying for this position, you consent to the processing of your personal data as described in our Privacy Policy ( https://cloudlinux.com/candidate-privacy-notice ), which provides detailed information on how we maintain and handle your data.

Location: São Paulo, Brazil; Buenos Aires, Argentina; Bogotá, Colombia We are hiring in Latin America! 🟢 At Dev.Pro, a US-based outsourced software development company, we're looking for contractors to join our global team of IT professionals helping tech companies scale their business through software engineering expertise. Operating as a remote-first company, our experts work across 55+ countries on five continents, giving us the flexibility and skillset to deliver high-quality engineering for projects of any size or complexity. We invite a Software Engineer with solid expertise in .NET and Angular technologies to join our team and contribute to the development of a brand-new project in the Point-of-Sale domain. You’ll help plan and implement the integration of large products, where the goals are to bring the most value to the end user and offer new opportunities for clients. 🟩 What's in it for you: Enhance your PoS systems expertise while contributing to a globally expanding project that is already widely present in the US, Canada, and EU markets Work with complicated logic and modern tech stack (.NET Core and .NET Framework 4.7.2, REST API, Angular v13+) Gain hands-on experience in cloud platforms and tools by working with the Azure ecosystem ✅ Is that you? 3+ years' work experience with .NET Hands-on experience with Angular, strong JavaScript and TypeScript skills Experience with REST API, ASP.NET MVC Good knowledge of relational DB concepts, MS SQL and SQL/T-SQL Expertise in software application architecture and familiarity with common design patterns Experience in working with distributed systems, availability, deployment Solid OOP/OOD, Open API Specification, SOLID skills Good familiarity with Agile methodologies, CI/CD pipeline tools and practices Upper-Intermediate+ English level Desirable: Working experience with the Azure cloud platform and services Knowledge of Point-of-Sale systems or related domains Experience with SonarQube to measure and analyze code quality Experience with Redis 🧩 Key responsibilities and your contribution In this role, you will be responsible for developing new software features, supporting and maintaining the existing ones, and contributing to the overall quality of the product, so strong technical expertise, teamwork, communication, and quality assurance skills are necessary. Understand the client’s business domain and participate in planning sessions Develop new product features and maintain existing functionalities Identify and fix bugs while introducing improvements to enhance the overall quality of the product Assess technical solutions and highlight potential technical risks Collaborate on work decomposition and estimation Follow the project SDLC and suggest improvements when necessary Contribute to enhancing code quality through pull requests and self-review Document technical solutions; improve CI/CD practices Collaborate on design with Tech Lead and Principal Architect Our next steps: ✅ Submit a CV in English — ✅ Intro call with a Recruiter — ✅ Internal interview — ✅ Offer Interested? Find out more: 📋How we hire 💻 LinkedIn Page 📈 Our website 💻IG Page By submitting application, I've read and agree to the Dev.Pro Private Policy and Consent on Processing of Personal Data . I agree to have my Personal Data being stored and processed by Dev.Pro.

Location: Belgrade, Serbia We are seeking a skilled Technical Artist or UI Artist to join our team. This position is open to candidates with a background in UI design who possess technical expertise in working with game engines, as well as to Technical Artists with experience in UI. If you are passionate about UI design and have a technical background, we encourage you to apply and become a vital part of our dynamic team! Responsibilities Create and implement prefabs based on designs provided in Figma Manage assets, including preparing images, creating and organizing Unity Game Objects, and maintaining fonts and atlases Troubleshoot and resolve UI issues within prefabs. Please Note: This role does not involve creating new designs in Figma or Photoshop Coding or scripting is not part of this position Requirements Unity: Familiarity with the prefab structure (excluding coding), ability to work within Unity to create UI prefabs Figma: Understanding of UI design principles, features, and components relevant to the project Animation: Basic knowledge of animation types and presentation flows, along with experience using prefabs Ability to create & maintain 2D animations in Unity Nice to have Familiarity with Figma, Photoshop, and Adobe Animate Experience in maintaining documentation Benefits Working with an international team of world-class professionals on exciting and challenging projects Learning & Development opportunities – mentoring, lectures, participation at industry conferences and events Medical Care package Sensibly flexible working hours Breakfasts, snacks and fruits available during the day, tea and coffee machines

Проектирование, разработка и поддержка backend-сервисов на NestJS и TypeScript. Разработка и развитие микросервисной архитектуры, обеспечение отказоустойчивости и масштабируемости.

Разработка микросервисов на Python (FastAPI, asyncio) для обработки финансовых данных; Интеграция с Kafka, PostgreSQL, ClickHouse, Formance Ledger через API;

разработка, поддержка и оптимизация программного продукта; написание, чтение, рефакторинг кода;

Задачи: — разработка бэкенда (Java/Spring) по готовым ТЗ, создание простых прототипов — документирование и тестирование REST/gRPC‑API — участие в планировании спринтов, декомпозиция своих задач — работа с БД (CRUD, модели данных) и настройка взаимодействия сервисов — участие в Code Review (проверка и доработка кода) — помощь в интеграции LLM и пилотировании AI‑инструментов (под контролем старших). Требования: — базовое знание Java (ООП, Collections, исключения) — основы Spring (Boot, DI) — достаточно учебных проектов — знание SQL (JOIN, GROUP BY) и принципов реляционных БД — умение работать с Git — понимание HTTP/REST — готовность учиться и работать в команде Условия: — формат работы полностью удаленный — официальное оформление — ДМС с 1 дня — возможности карьерного роста

Разрабатывать и поддерживать CI-пайплайны: сборку компонентов, запуск автоматических проверок, публикацию артефактов и подготовку тестовых окружений; Автоматизировать создание, настройку, обновление и удаление стендов разработки и тестирования;

Разработка новых подсистем; Доработка существующих типовых и нетиповых конфигураций;

Чем предстоит заниматься: Проектирование и разработка новых микросервисов и бизнес-логики. Оптимизация производительности существующих компонентов системы, работа с высоконагруженными участками кода.

Задачи: Вести людей и команду: регулярные 1:1, развитие, обратная связь, рост инженерной планки; Планировать delivery вместе с продуктом/аналитикой: приоритеты, декомпозиция, оценки, риски, прогнозируемость;

Location: San Francisco, CA 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 Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. 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 — $485,000 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.