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Location: Amsterdam, Netherlands About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role The Senior Payroll Specialist owns end-to-end Dutch payroll, operated in Nmbrs, and acts as the designated backup for the company’s other international payrolls. This role harmonizes, simplifies, and continuously improves payroll execution and payroll accounting across the company’s Dutch entities, pay groups, providers, and systems. This is to make sure payroll remains accurate, compliant, trusted, and understandable as the company grows without a proportional increase in manual work or risk. As international backup, the role maintains working proficiency in the company’s other country payrolls and provides continuity during peak periods, absences, and year-end. Your responsibilities will include: Own end-to-end Dutch payroll execution in Nmbrs across entities and pay groups: monthly and off-cycle runs, holiday allowance, year-end close and annual statements (jaaropgaven), and the payroll calendar; define consistent execution standards that meet Dutch statutory and contractual requirements. Ensure Dutch payroll tax and social security compliance: wage tax and national insurance contributions (loonheffingen), employee insurance and Zvw premiums, the work-related costs scheme (WKR), timely and accurate loonaangifte to the Belastingdienst, corrections, and resolution of Belastingdienst and UWV correspondence. Administer regulated and complex pay items accurately: the 30% ruling and other expatriate and cross-border arrangements, equity income (RSU/option) taxation, pension contributions and provider files, sick leave and statutory leave schemes, transition payments and termination settlements, wage garnishments (loonbeslag), and taxable benefits-in-kind. Run and reconcile shadow payrolls in Nmbrs for internationally mobile employees (inbound assignees, cross-border and remote workers, and outbound Dutch employees with a host-country shadow payroll) Serve as the designated backup for the company’s other international payrolls: maintain working knowledge of country calendars, providers, and controls, and run or support pay cycles during absences, peak periods, and year-end in coordination with the payroll owners. Lead root-cause analysis for recurring payroll, accounting, or service failures; design and implement durable fixes. Drive process harmonization and simplification to reduce manual steps, handoffs, rework, and off-cycle runs; make full use of Nmbrs functionality, integrations, and reporting. Lead payroll execution during transitions (new entity launches, provider or system changes, restructures/M&A), ensuring continuity and control. Ensure payroll accounting remains clean as scope changes: consistent journals, accruals, reversals, and reconciliation standards. Monitor preventative controls by design, including SOX-aligned payroll controls; improve evidence pack quality and audit readiness. Act as escalation owner for complex or sensitive pay issues; provide authoritative explanations to employees and senior stakeholders. Partner with HR Ops, Finance, Legal/Compliance, Nmbrs, and international payroll providers to deliver scalable operating models and service quality. We expect you to have: At least 7+ years of Dutch payroll experience, including multi-entity scope in complex environments (growth, change, or hybrid in-house/outsourced models). Hands-on Nmbrs experience is prefer: running payroll end to end, configuring wage components and journal mappings, managing loonaangifte and corrections, and using its reporting and integrations (HRIS, GL, pension and benefits providers). Deep working knowledge of Dutch payroll compliance: loonheffingen and social security, WKR, the 30% ruling, expatriate taxation and shadow payroll, pension administration, sick leave and statutory leave, termination settlements, and year-end reporting. Working knowledge of international payroll (running or overseeing outsourced country payrolls) and readiness to act as the designated backup for the company’s other payrolls. Strong payroll controllership skills: ability to design controls, enforce SoD/approvals, and ensure audit-ready evidence. Proven capability improving payroll processes (standardization, automation enablement, reducing manual effort and errors) and leading transitions (vendor/system/new entity) with structured planning and issue resolution. Strong payroll accounting knowledge and experience working with Finance during close and audits. Excellent stakeholder management; comfortable acting as an escalation owner and payroll authority. Fluent English and professional working proficiency in Dutch (statutory correspondence, legislation, and provider documentation are in Dutch). It will be an added bonus if you have: Dutch payroll qualification (PDL and/or VPS) and/or Nmbrs certification. Experience with international payroll providers or aggregators and multi-country operating models (SLAs, KPIs, remediation). Experience operating payroll controls in a SOX-compliant, US-listed company environment. Experience with equity compensation payroll (RSUs/options), including cross-border cases and trailing tax liabilities. Experience supporting Global Mobility programs: shadow payroll set-up, tax equalization/protection and hypothetical tax, gross-ups, and year-end assignee reconciliations with tax advisors. Experience turning statutory/operational requirements into system/workflow requirements (e.g., Nmbrs–HRIS–GL integrations). Project management certification or comparable experience leading cross-functional initiatives. Competencies & Behavioral Traits: Systems thinking: redesigns the process so it scales, rather than adding checks and manual work. Bias for prevention: anticipates issues and builds guardrails. Influence without authority; can align multiple stakeholders around standards. Comfort with ambiguity and change; makes decisions with incomplete information. Reliability and ownership: a dependable backup who keeps continuity across payrolls when it matters most. Strong coaching mindset; elevates capability across the payroll team. #LI-DY1 Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: Hybrid About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. About the Role We are looking for a Systems Engineer to help build and manage tools that help engineers deploy and operate the services that make Cloudflare work. Our mission is to provide a reliable and flexible platform to help product teams release new software efficiently and safely. The role includes both software engineering and operational responsibilities. Available Locations: Austin, TX; Lisbon, PT; London, UK. Responsibilities Design and build scalable and resilient systems that can keep up with company growth. Design and build developer productivity tooling. Harden the platform against security threats and resource contention issues. Improve our development systems and practices. Improve the efficiency of managing resources such as CPU, bandwidth and storage. Work with application teams to understand their potential challenges and help them choose the best way to architect their systems. Drive initiatives that make our engineers happier and more productive. Help respond and prevent incidents impacting core platforms. Desirable Skills, Knowledge, and Experience Demonstrable professional experience building and managing large-scale distributed systems or Linux production environments. Proficiency in at least one systems programming or scripting language (e.g., Go, Rust, C/C++, Python) with a track record of writing clean, maintainable code. Experience managing containerized workloads at scale using Kubernetes, Docker, or similar orchestration platforms. Experience with CI/CD pipelines and automated testing strategies for infrastructure. Source control experience including branching, merging and rebasing. Strong analytical and troubleshooting skills, with a methodical approach to diagnosing complex system-level failures. Bonus Points Experience with configuration management and infrastructure-as-code tools (e.g., Ansible, Terraform, SaltStack). Deep understanding of Linux/Unix operating system internals (e.g., storage subsystems, memory management) and core networking protocols (TCP/IP, DNS). Contributions to open-source systems software projects or active participation in the broader systems engineering community. Compensation Compensation may be adjusted depending on work location. For Portugal based hires: Estimated annual salary of €66,000-91,000. What Makes Cloudflare Special? We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet. Project Galileo : Since 2014, we've equipped more than 2,400 journalism and civil society organizations in 111 countries with powerful tools to defend themselves against attacks that would otherwise censor their work, technology already used by Cloudflare’s enterprise customers--at no cost. Athenian Project : In 2017, we created the Athenian Project to ensure that state and local governments have the highest level of protection and reliability for free, so that their constituents have access to election information and voter registration. Since the project, we've provided services to more than 425 local government election websites in 33 states. 1.1.1.1 : We released 1.1.1.1 to help fix the foundation of the Internet by building a faster, more secure and privacy-centric public DNS resolver. This is available publicly for everyone to use - it is the first consumer-focused service Cloudflare has ever released. Here’s the deal - we don’t store client IP addresses never, ever. We will continue to abide by our privacy commitment and ensure that no user data is sold to advertisers or used to target consumers. Sound like something you’d like to be a part of? We’d love to hear from you! Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs.  More details about this will be available at that stage of the interview process. This position may require access to information protected under U.S. export control laws, including the U.S. Export Administration Regulations. Please note that any offer of employment may be conditioned on your authorization to receive software or technology controlled under these U.S. export laws without sponsorship for an export license. Cloudflare is proud to be an equal opportunity employer.  We are committed to providing equal employment opportunity for all people and place great value in both diversity and inclusiveness.  All qualified applicants will be considered for employment without regard to their, or any other person's, perceived or actual race, color, religion, sex, gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship, age, physical or mental disability, medical condition, family care status, or any other basis protected by law. We are an AA/Veterans/Disabled Employer. Cloudflare provides reasonable accommodations to qualified individuals with disabilities.  Please tell us if you require a reasonable accommodation to apply for a job. Examples of reasonable accommodations include, but are not limited to, changing the application process, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.  If you require a reasonable accommodation to apply for a job, please contact us via e-mail at hr@cloudflare.com or via mail at 101 Townsend St. San Francisco, CA 94107.

Location: Montreal, Canada 📍 Location: Montreal, Quebec, Canada 📄 Employment Type: Full-Time 🏢 Work Arrangement: TBD 🚀 Start Date: TBD 💰 Compensation: TBD — based on skills and experience Important Screening Requirements Must be eligible to work in Canada and capable of obtaining the required security clearance. Bachelor’s or Master’s degree in Electrical Engineering, Systems Engineering, Aerospace Engineering, or a related field. 15+ years of experience in satellite systems design, particularly for Low Earth Orbit (LEO) missions. Experience supporting the full satellite systems lifecycle, from design through verification, launch, and operations. Strong background in electrical system architecture, payload-bus integration, and subsystem interfaces. Experience with requirements management, system verification and validation, and technical documentation. Proficiency in MATLAB and familiarity with Agile methodologies. Senior Electrical Satellite Systems Engineer- JD.pdf About the Role We are seeking a Senior Electrical Satellite Systems Engineer to lead electrical systems engineering activities for satellite programs, with a focus on Low Earth Orbit (LEO) missions. The successful candidate will define electrical system architecture, oversee subsystem interfaces, manage requirements and technical documentation, and lead verification and validation activities throughout the satellite lifecycle. This role also involves technical leadership, stakeholder coordination, subcontractor oversight, and support for launch and early operations. Senior Electrical Satellite Systems Engineer- JD.pdf What We’re Looking For We’re looking for a senior-level satellite systems engineer with deep electrical systems expertise, strong end-to-end lifecycle experience, and the ability to lead complex technical activities across design, integration, verification, and operations. The ideal candidate combines systems-level thinking with practical engineering judgment, clear communication, and effective technical leadership. Senior Electrical Satellite Systems Engineer- JD.pdf Key Responsibilities System Design & Architecture Define electrical system architecture supporting payload and satellite bus integration. Manage system requirements, traceability, and technical documentation, including Concept of Operations (CONOPS). Oversee interfaces among satellite subsystems, including avionics, electrical power, and Attitude and Orbit Control Systems (AOCS). Coordinate system-level design activities to ensure compatibility across subsystems. Analysis & Reliability Conduct electrical power budgets and system-level analyses. Perform failure mode and reliability analyses. Develop and support Fault Detection, Isolation, and Recovery (FDIR) strategies. Identify system risks and support technical trade-off decisions. Verification & Validation Lead system verification and validation (V&V) activities. Develop verification strategies, test plans, and supporting documentation. Support ground testing, including Thermal Vacuum (TVAC) and Electromagnetic Compatibility/Interference (EMC/EMI) testing. Ensure verification activities address system requirements and interface performance. Testing & Operations Develop system-level test plans and procedures. Support launch preparation, Launch and Early Orbit Phase (LEOP), and Satellite Operations Control Centre (SOCC) activities. Assist with troubleshooting and resolving system-level issues during testing and operations. Senior Electrical Satellite Systems Engineer- JD.pdf Technical Leadership & Stakeholder Coordination Mentor engineers and provide technical guidance to engineering teams. Coordinate with stakeholders, subcontractors, and cross-functional engineering groups. Lead and participate in design reviews, including: System Requirements Review (SRR) Preliminary Design Review (PDR) Critical Design Review (CDR) Apply relevant engineering standards and practices, including ECSS and PUS-STD. Project & Process Management Oversee subcontractor activities and technical deliverables. Track technical issues, risks, and project progress. Support Agile engineering practices and continuous improvement initiatives. Senior Electrical Satellite Systems Engineer- JD.pdf Required Qualifications Bachelor’s or Master’s degree in Electrical Engineering, Systems Engineering, Aerospace Engineering, or a related discipline. 15+ years of satellite systems design experience, particularly involving LEO missions. Experience across the full satellite systems lifecycle. Strong understanding of electrical system architecture and payload-bus integration. Experience with avionics, electrical power, AOCS, and related subsystem interfaces. Experience with requirements management, traceability, and system documentation. Background in power budgeting, failure mode analysis, reliability engineering, and FDIR. Experience leading system-level verification and validation. Knowledge of ground testing, including TVAC and EMC/EMI. Experience supporting launch, LEOP, and satellite operations activities. Proficiency in MATLAB and familiarity with Agile methodologies. Strong communication, leadership, analytical, and problem-solving skills. Must be eligible to work in Canada and able to obtain the necessary security clearance. Senior Electrical Satellite Systems Engineer- JD.pdf Preferred Qualifications Experience with ECSS standards and PUS-STD. Experience leading satellite design reviews and coordinating technical stakeholders. Experience overseeing subcontractors and managing technical deliverables. Experience mentoring engineers and leading multidisciplinary engineering teams.

Location: Berlin, Germany About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: Zurich, Switzerland About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: London, United Kingdom About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: Limassol; Belgrade Workplace: onsite About Unlimit Unlimit is the global financial infrastructure for the borderless agentic economy. Designed to bridge the gap between fragmented local markets and the future of autonomous commerce, Unlimit provides the programmable operating layer for the world’s most ambitious businesses. Through an integrated technology stack, the platform unifies global payment acceptance, programmable financial accounts, and digital asset rails into a singular financial layer. By mapping hyper-local payment ecosystems directly into its architecture, Unlimit enables businesses to move value and settle transactions instantly across continents. Built on decades of hard-won regulatory depth and an extensive global license portfolio, the platform provides direct access to a massive global infrastructure. With major hubs in London, San Francisco, Singapore, São Paulo, and Mexico City, Unlimit is building the infrastructure that makes global expansion a matter of code, not geography. About the role: We are looking for a senior engineer to become the technical lead of our card processing platform. You will own the SmartVista ecosystem end to end (FrontEnd and BackOffice), set the standards for how processing is built, changed and operated, and lead the work of finding and closing gaps in our processes, configuration and controls. This is not a pure support or configuration role. We expect you to replace manual work with automation and to put AI to practical use in processing operations: anomaly detection, intelligent alerting, automated root-cause analysis and AI-assisted analysis of scheme specifications and transaction data. The role combines card processing expertise + technical leadership + engineering (backend, scripting, integrations) + automation and AI adoption. What you will do: 1. Technical leadership and platform ownership Own the end-to-end architecture of the processing ecosystem (SmartVista FrontEnd and BackOffice, interfaces, HSM, scheme connectivity) and make architecture and technology-stack decisions. Act as the technical lead and senior escalation point for processing engineers; set engineering standards, review solutions. Lead major incidents, coordinate the response with internal teams, the vendor and card schemes, and drive post-incident reviews to permanent fixes. Manage the relationship with the SmartVista vendor on releases, patches, defects and change requests. 2. Process ownership and gap remediation Map the current processing processes (authorisation, clearing, settlement, disputes, reconciliation, reporting, release and change management, monitoring and on-call) and identify gaps, single points of failure and manual workarounds. Build and run a remediation plan: define owners, deadlines and measurable outcomes, and follow through to closure. Introduce and maintain operational standards: runbooks, release and change procedures, incident and problem management, SLAs and KPIs (e.g. availability, approval rate, incident resolution time). Make sure scheme mandates and regulatory changes are tracked, assessed and implemented on time, and that the evidence of compliance is documented. 3. Processing development and scheme compliance Deliver the technical implementation of new card products and services (acquiring and issuing) for Visa, Mastercard, UnionPay, American Express and other schemes. Translate scheme mandates and specifications into technical solutions, and lead certification projects with the schemes. Configure and develop SmartVista FrontEnd and BackOffice products, services and business rules. Prepare technical requirements for system upgrades; test and install releases and patches with minimal downtime. Diagnose complex issues across authorisation, clearing and settlement flows and drive them to root cause. Set up and maintain payment system emulators and test environments; take part in go-live and pilot activities. Maintain clear technical documentation of the platform and implementation procedures. 4. Monitoring and observability: design, development and implementation Design and build an end-to-end monitoring framework for the processing platform, covering infrastructure, applications, interfaces and business metrics. Develop and implement real-time monitoring of: - authorisation flows: approval and decline rates by scheme, BIN, issuer country, merchant, MCC and response code. - response times, timeouts and stand-in processing. - scheme, issuer and HSM connectivity and host availability. - transaction volumes and abnormal spikes or drops. Develop and implement monitoring of back-office processes: - clearing file generation, submission and acknowledgement (Visa, Mastercard and other schemes). - incoming scheme files and reports, settlement positions and reconciliation breaks. - end-of-day and batch jobs, interchange and fee calculation, chargeback and dispute deadlines. Monitor scheme compliance indicators (e.g. decline-code usage, data-integrity edits, excessive retries, fraud and dispute ratios) to catch issues before they become scheme non-compliance or fines. Define thresholds, alerting rules and escalation paths; integrate alerts with on-call, incident management and messaging tools. Build operational dashboards for engineering, operations and management, and define the related KPIs and SLAs. Develop health checks, synthetic test transactions and log-based monitoring for SmartVista components. Continuously tune monitoring to reduce false alerts, and turn incident findings into new checks and controls. 5. Automation and AI Identify manual and repetitive processes and automate them, including: - reconciliation and settlement. - scheme file exchange and report submission. - testing, regression and release management. - monitoring and alerting. Build CI/CD pipelines and automated deployment for processing components and configuration. Design and deliver AI-driven solutions for processing operations, for example: - anomaly detection on transaction flows, decline patterns and fraud signals. - intelligent alerting that reduces noise and escalates what matters. - automated root-cause analysis of declines and incidents - AI assistants or agents that analyse scheme specifications, mandates and authorisation messages and flag deviations. Introduce test automation and regression frameworks for scheme messages and processing flows. Promote the safe use of AI tools across the team, with appropriate controls for data security, PCI DSS and human oversight of changes to production. What you bring: 6+ years of hands-on experience in card processing, covering the full transaction lifecycle: authorisation, clearing, settlement, chargebacks and reconciliation. At least 2 years in a senior, lead or architect role, with a track record of owning a processing platform and leading engineers, directly or as a technical lead. Strong practical experience with SmartVista (FrontEnd and/or BackOffice); deep experience with acomparable platform (e.g. Way4, TranzWare) may be considered. Proven experience of improving processes: finding operational gaps, designing controls and delivering a remediation plan to completion. Certification experience with Visa and Mastercard and the ability to read and apply scheme specifications and rules (ISO 8583, Visa Base I/II, Mastercard IPM/CIS). Hands-on automation skills: Python, SQL/PL/SQL, Bash; experience with CI/CD tools (e.g. GitLab CI, Jenkins), test automation and API testing (e.g. Postman). Practical experience applying AI or ML in production work, such as anomaly detection, LLM-based tools or agents, or AI-assisted analysis and coding, and a clear view of their limits and risks. Working knowledge of Oracle DB, WebLogic, Linux, REST/SOAP web services and HTTP. Experience with scheme tools: Visa Edit Package, VCX, Mastercard File Express (MFE) and scheme test tools and emulators (e.g. Mastercard MAX, Visa test systems). Good understanding of e-commerce and digital payments: EMV 3-D Secure, tokenisation (VTS/MDES), Apple Pay / Google Pay, recurring payments, AVS. Knowledge of card technologies and security: EMV, contactless, HSM (Thales), key management, PCI DSS. Experience with release and change management in a 24/7 production environment. Fluent English (B2+) for daily communication with card schemes, vendors and international teams. What we offer: A key role with real ownership of a business-critical platform and visible impact on approval rates, stability and time to market. The mandate and support to modernise processing with automation and AI. Competitive salary and performance-based rewards. Paid vacation, sick leave and public holidays. Flexible working hours and modern equipment. An international team of payments professionals in a fast-growing fintech. Join Unlimit Team now!   Unlimit is an equal opportunity employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We welcome applications from all members of society irrespective of age, sex, disability, sexual orientation, race, religion or belief.   We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Location: Almaty, Kazakhstan; Astana, Kazakhstan; Belgrade, Serbia; Cluj-Napoca, Romania; Krakow, Poland; Larnaca, Cyprus; Lodz, Poland; Lublin, Poland; Monterrey, Mexico; Montevideo, Uruguay; Remote.AR, Remote.LATAM-country; Remote.BR, Brazil; Remote.BG, Bulgaria; Remote.CO, Colombia; Remote.GE, Georgia; Remote.KZ, Kazakhstan; Remote.PL, Poland; Riga, Latvia; Rosario, Remote.LATAM-country; Sofia, Bulgaria; Tbilisi, Georgia; Varna, Bulgaria; Warsaw, Poland; Wroclaw, Poland; Yerevan, Armenia As a Solutions Architect, you will define and guide architecture across card acquiring and payment processing domains. You will work on transaction flows, processing and routing components, scheme and partner integrations, resilience patterns, and technical standards. You will work closely with engineering, product, and business stakeholders to translate complex payment requirements into scalable solution designs and guide engineering teams through implementation. - Define and evolve the solution architecture for card acquiring and processing capabilities, covering authorization, switching, routing, clearing, settlement and reconciliation; - Design end-to-end transaction flows for card-present and card-not-present scenarios, including authorization, capture, reversals, refunds and exception handling; - Design integration patterns with card schemes, processors, banks, payment gateways and merchant-facing systems, including ISO 8583 message flows and scheme-specific variations; - Define resilience and failure-handling patterns for high-throughput, low-latency transaction processing, including idempotency, retries, timeouts, duplicate detection and recovery; - Ensure the architecture meets PCI DSS and payment security requirements, including cardholder data protection and tokenization; - Translate business and product requirements into concrete technical solutions and communicate architecture decisions across engineering, product and business teams; - Remain hands-on and contribute directly to implementation where needed, particularly for complex transaction processing, integrations and core platform components; - Define technical standards and review solution designs and implementations across engineering teams; - Contribute to the evolution and modernization of payment processing capabilities and legacy components where relevant. - Strong experience as a Solution Architect, Technical Architect or Lead Architect in card acquiring or payment processing; - Proven experience designing or significantly evolving an acquiring platform, payment switch or card transaction processing system, including real-time switching and routing; - Deep understanding of the acquiring transaction lifecycle: authorization, capture, reversals, refunds, clearing, settlement and reconciliation; - Strong practical knowledge of ISO 8583, including message flows and scheme-specific implementations; - Strong knowledge of card-present and card-not-present transaction processing; - Good understanding of EMV and 3-D Secure transaction flows; - Experience integrating with card schemes, processors, banks, payment gateways or merchant-facing systems; - Strong understanding of distributed systems, including idempotency, retries, timeouts, duplicate handling and recovery scenarios; - Experience designing high-availability, high-throughput, low-latency transactional systems; - Good understanding of PCI DSS, cardholder data security, tokenization and payment security principles; - Strong software engineering background with recent hands-on development experience and the ability to contribute directly to production code when needed; - Excellent communication and stakeholder management skills; - Upper-Intermediate or higher level of English. - Hands-on experience designing and certifying direct acquiring integrations with major card schemes, such as Visa, Mastercard, UnionPay, JCB, American Express and Discover; - Experience with both POS and e-commerce acquiring; - Experience with HSMs, PIN processing, payment cryptography and key management; - Hands-on work with scheme clearing files and settlement processes, such as Visa BASE II and Mastercard IPM; - Experience with processing platforms such as Way4, BASE24, SmartVista, TranzAxis or similar; - Experience with acquiring platform modernization or migration; - Experience with event-driven or microservices-based transaction processing architectures.

Location: Boston, MA Workplace: OnSite At WHOOP, we’re on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. Our wearable technology collects rich physiological data, providing members with actionable insights into their recovery, training, and sleep. The Health team develops the algorithms and features that expand WHOOP's health sensing capabilities. The work spans women's health, software as a medical device, wellness and longevity, member insights, and emerging health signals. We combine continuous physiological data with clinical research and domain expertise to ship features that are scientifically grounded and useful to millions of members. As a Staff Applied Machine Learning Scientist on the Health team, you will turn ambiguous clinical and product questions into defensible studies, models, and evaluation plans on WHOOP's longitudinal wearable and member data (physiological signals, behavioral logs, labs and integrations, and related streams). You will set the methodological bar for a Health domain. You will partner with Machine Learning Engineers on the path to production, and with Product, clinical, and Digital Health partners on what the data can and cannot support. Success in this role requires applied algorithm and statistical craft, the judgment to operate across Health problem types, and Staff-level leadership: launching domain work independently, raising the quality and velocity of the people around you, and making clear prioritization calls under uncertainty. RESPONSIBILITIES - Lead the design, development, and validation of machine learning and statistical methods for Health features, using wearable physiological and behavioral data and related member sources. - Frame ambiguous health and product questions into well-scoped modeling or study problems, with clear performance criteria, evaluation plans, and honest treatment of uncertainty and failure modes. - Set the methodological and quality standard for a Health domain: reusable evaluation practices, review expectations, and documentation that others can inherit. - Develop and refine approaches for time-series, longitudinal, and multimodal data, including data curation, labeling strategy, feature and representation choices, and offline evaluation that holds up under real-world noise, missingness, and population shift. - Partner with Machine Learning Engineers to translate validated methods into production-ready systems, making modeling choices that account for scalability, reliability, latency, cost, monitoring, and (where relevant) controlled-release constraints. - Serve as a technical counterpart to Product and Digital Health (clinical science, regulatory, quality): pressure-test performance requirements, study design, and evidence quality, and push back when a claim is not supportable. - Raise the quality and velocity of the scientists and engineers around you through design reviews, mentorship, and the judgment of when to coach someone else to champion a problem versus taking it yourself. - Communicate trade-offs, risks, and results clearly to technical, clinical, product, and leadership audiences; prevent misalignment early; drive decisions with evidence and a clear point of view on domain direction. QUALIFICATIONS - Master's or PhD (or equivalent professional experience) in Biomedical Engineering, Electrical Engineering, Computer Science, Applied Mathematics, Biostatistics, Bioinformatics, Physiology, or a related quantitative or health-science field. - 7+ years of professional experience developing algorithms, applied ML models, or statistically rigorous analyses in health, wearables, medical devices, digital health, or a closely related domain. - Demonstrated Staff-level technical leadership: setting methodological standards others reuse, influencing multi-team outcomes, mentoring senior ICs, and making prioritization calls under uncertainty. End-to-end project ownership alone is not sufficient. - Strong foundation in statistics (Bayesian and frequentist) and machine learning for time-series and longitudinal data, with the ability to draw defensible conclusions from observational, noisy, or otherwise messy real-world data. - Experience with wearable or physiological signals (PPG, IMU, HR/HRV, sleep, activity, temperature, or similar) or a closely related adjacent time-series domain. - Hands-on experience designing studies or evaluation protocols end-to-end: data curation, metric choice, validation splits, subgroup and failure analysis, and documentation of assumptions and limitations. - Strong Python skills with reproducible scientific / ML engineering hygiene (clean, reviewable code; comfort with stacks such as NumPy/SciPy/pandas, scikit-learn, PyTorch/TensorFlow). - Clear, high-signal communicator who can move between deeply technical method discussions and cross-functional conversations with Product, clinical partners, and leadership. This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.   Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.   WHOOP is an Equal Opportunity Employer and participates in E-verify https://www.e-verify.gov/ to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Location: Worldwide Minerva Project | Fully remote, worldwide Annual compensation: US$100,000–$125,000, depending on experience. Nurturing critical wisdom for the sake of the world Why should you join Minerva? The salary and benefits are competitive; the people you’ll work with are authentic, thoughtful, driven, and human; but the reason we chose Minerva is our strong belief in the company’s mission: if we want to change the world, it’s imperative that we improve education everywhere. As our first step to achieving that goal, we built Minerva University from the ground up, refining and improving every aspect of a traditional university. Minerva University boasts an unbiased admissions process , seminars instead of lectures , and a global experience over 4 years ; and has been named the most innovative university in the world - three times . Now, we are expanding our reach and impact by partnering with emerging and established higher education institutions around the world. Our methodology, which emphasizes active class participation, deliberate practice, and other empirically-proven methods, is enabled by Minerva Forum. Built to address the central educational challenges of the modern era, Minerva Forum embodies the leading research on the science of learning to enable the rapid development of sophisticated, durable skills-based programs. The role We're looking for a senior product manager who can turn user needs, partner requirements and business goals into clear product decisions, then work with design and engineering to get the right changes shipped. You'll own the problem, priority, scope and success measures for your product work. You'll get close enough to the details to make useful tradeoffs, while keeping the team focused on the outcome we're trying to achieve. This is a hands-on individual contributor role in a small team, with substantial responsibility for moving work forward independently. What you'll do Decide what to work on and why. Bring together user research, support patterns, partner feedback and business priorities. Make a clear case for the problems worth solving now and the work that can wait. Own scope and sequence. Define goals, non-goals, acceptance criteria and dependencies. Work with engineering on capacity and feasibility, and keep the roadmap realistic when priorities compete. Connect product, design and engineering. Give designers the context and constraints they need, evaluate options together, and resolve product questions as implementation progresses. Keep decisions and their reasoning clear enough for people to work asynchronously. Make success measurable. Define useful measures such as adoption, task completion, failure rates or support load. Agree with engineering on the tracking needed before release, check that the data is usable, and use what we learn to decide what happens next. Manage stakeholder needs and commitments. Understand the needs of partners, academic teams, support and leadership, including integration, privacy and operational constraints. Make conflicting priorities visible and communicate changes early. Work directly with partners when needed to understand product needs, and coordinate with Support and account management on issues, expectations and follow-through. Carry work through release. Help test important workflows, prioritize defects and decide whether remaining gaps affect release readiness. Decide who needs to know about a change, when, and how it should be explained to customers. Work with the people closest to the product and its users to make launch communication and documentation accurate. Follow up after launch. Look at actual use and feedback, identify where the result differs from the intention, and turn that into concrete product improvements. What you'll bring Experience independently owning software product work from problem definition through launch and follow-up, with clear examples of decisions you made and what happened as a result. Sound judgment about prioritization and scope. You can explain what you chose not to build, what evidence informed the choice, and when you changed your mind. Experience working closely with designers and engineers on products with complex workflows, multiple user roles or competing stakeholder needs. Comfort reasoning with data, asking what a metric really tells us, and making decisions when the available evidence is incomplete. Strong writing and clear communication with both technical and nontechnical people. You can make a complicated product decision understandable without hiding the tradeoffs. The ability to organize your own work, surface risks early and close open questions in a distributed team. An interest in education and the patience to understand how learners, educators and administrators actually use software. Experience in education or education technology—including teaching—as well as work with institutional customers, learning-platform integrations or product analytics, is a plus. Show us the relevant work and judgment you've developed; a specific degree or tool background isn't the focus of this search. Working together This position is fully remote. We welcome candidates anywhere in the world who can maintain regular working-hour overlap with the team. We'll confirm the required window early in the process. You'll work closely with product and technology leadership, design, engineering and partner-facing colleagues. You'll have room to make decisions within an agreed area of responsibility, with leadership involved in company priorities and commitments beyond that scope. What to share Please share your resume or profile and a brief example of a product decision you owned: the problem, the options you considered, your choice and what you learned after shipping. Existing work or an anonymized summary is welcome. Salary: $100,000 – $125,000 / year

Location: Bangalore, India; Remote, Poland GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role At GitLab, People technology never stands still, and as a People Systems Engineer, you’ll build what keeps it moving. Reporting to the Senior Director, People Technology, and working alongside our Lead People Systems Engineer, you’ll design, build and maintain the automations, integrations and AI-powered workflows that connect Workday, Greenhouse, Slack and the tools that power our global workforce. You’ll own features from request through tested code, deployment and ongoing reliability across Workato, Google Cloud and Claude. With an AI-forward mindset, you’ll rethink how People work gets done, eliminating manual effort and creating streamlined, delightful experiences for team members. If you think like a software engineer, love solving puzzles and want to grow your career at the intersection of people systems, AI and automation in an all-remote, async-first environment, this is your role. What You’ll Do Build, test and maintain People system automations and integrations across Workato, Google Cloud Platform, Claude, Workday, Greenhouse and GitLab, owning your features from design through deployment. Develop integrations between Workday, Greenhouse, Slack and internal tools using Workday Web Services (WWS) and Slack’s API and Bot SDK, and build Slack apps and bots that turn manual steps into simple, automated workflows for team members around the world. Write clean, readable automation code in Ruby or a comparable scripting language, stored and versioned in GitLab, with clear documentation and peer code reviews. Keep pace with a high-growth company by monitoring integration health, troubleshooting failures quickly and fixing root causes so our automations stay reliable at scale. Ship changes safely through GitLab CI/CD pipelines, following testing, change-management and Sarbanes-Oxley (SOX) IT general controls (ITGC) standards with care. Lead with AI: use AI tools, large language models (LLMs) and Model Context Protocol (MCP) frameworks, including Claude, every day to accelerate development, prototype AI agents and reimagine how People work gets done, then share what you learn so the whole team moves faster. Look past the surface of a request to find the real problem, then recommend and build solutions that are durable, scalable and easy to support. Partner with People Technology Analysts on configuration, testing and releases, and with Information Security, IT and AI Engineering on security and standards. Raise the bar for how automations are architected, documented and maintained, learning from and contributing alongside our Lead People Systems Engineer. What You’ll Bring Hands-on experience building automations or integrations, ideally with human resources or business systems. Experience writing and maintaining code in Ruby or a comparable scripting language, such as Python or JavaScript, and a willingness to learn Ruby if you haven’t already. Hands-on experience with Workato or a comparable integration platform as a service (iPaaS) in a production environment. Experience working with APIs and web services, including REST and SOAP, and an understanding of how data moves between systems. Proficiency with Git for version control and code review; you treat automation code like production software. A forward-thinking, AI-forward mindset: you use AI tools every day to automate tasks, speed up development and improve processes, and you’re energized by what AI makes possible next. The ability to work independently, solve problems with curiosity, come with options and a recommendation, and collaborate openly. You protect highly confidential information and take ownership of code quality, reliable outcomes and the end user’s experience. Additional relevant experience includes Workday integrations (WWS, Reporting as a Service or Workday Studio), Slack apps or bots, CI/CD pipelines, Google Cloud Platform or Amazon Web Services, Docker or Kubernetes, building with LLMs, AI agents or MCP frameworks, SOX ITGC controls, Greenhouse, Serval, Google Workspace, or all-remote and asynchronous work. A related computing degree or equivalent experience is also welcome; none of these is required. About the team The People Technology team is building the systems foundation that lets GitLab scale as a global, all-remote company. We’re always raising the bar for what People technology can do, improving reliability, usability and intelligence across the platforms that power our People organization. We work across core systems, integrations, reporting and compliance, and partner closely with stakeholders across time zones to move fast, build with purpose and create seamless experiences that empower people to do their best work. If you want to grow your career building something that matters at a company working at the edge of what’s possible, this is the team. How GitLab will support you Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental leave Home office support Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Location: Washington DC Workplace: Remote ABOUT US At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale. We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry. More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale. ABOUT US At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. Today, LangChain, LangGraph, LangSmith, and Agent Builder are used by teams shipping real AI products across startups and large enterprises. Millions of developers trust LangChain to power AI teams at companies like Replit, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, and 35% of the Fortune 500. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. ABOUT THE TEAM The LangChain Deployed Engineering - Federal team works directly with U.S. Government customers — including the Department of Defense (DoD), Civilian Agencies, and the Intelligence Community (IC) — who are building and operating AI agents in mission-critical environments. This is a hands-on, highly technical team that partners closely with government engineers, technical program managers, and system integrators across the full lifecycle — from pre-award technical shaping and evaluations to post-deployment advisory work in secure environments. The focus is on achieving the technical win, architecting secure and compliant agent systems, and enabling agencies to operate AI agents reliably at scale using the LangChain suite. Deployed Engineers sit at the intersection of engineering, product, security, and go-to-market. You will shape how LangChain is adopted across classified and unclassified environments, ensuring alignment with federal compliance requirements while feeding mission-driven insights back into the platform. ABOUT THE ROLE As a Deployed Engineer, you’ll work on some of the hardest problems in applied AI — not demos or experimental research, but production systems supporting real-world missions. You’ll help defense, civilian, and intelligence customers design, deploy, and operate AI agents in secure, regulated, and high-stakes environments. The feedback loop is fast, the mission impact is tangible, and your work directly influences how AI agents are deployed across the federal landscape. You will operate in environments that demand security, reliability, compliance (FedRAMP, IL levels, etc.), and operational rigor. WHAT YOU’LL DO - Co-architect and co-build production AI agents with federal engineering teams and system integrators - Own the technical win in federal pre-sales engagements by designing secure POCs, supporting RFI/RFP technical responses, and guiding technical evaluations - Design architectures that meet federal security and compliance requirements (FedRAMP, DoD IL2–IL6, NIST, etc.) - Help agencies deploy and operate agent-based applications such as mission support copilots, intelligence analysis agents, research automation systems, and multi-step operational workflows - Advise customers post-award on architecture, scalability, observability, evaluation, and long-term roadmap decisions - Deliver technical demos, workshops, and enablement sessions tailored to government developer and operator audiences - Partner with security teams to navigate ATO processes and accreditation pathways - Surface field feedback from federal use cases and contribute reusable patterns, secure deployment guides, and example code that scale across agencies - Occasionally contribute code upstream when it meaningfully improves federal customer outcomes WHAT YOU’LL BRING - Must have zctive or prior U.S. security clearance (Secret, TS/SCI) - 3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, product engineering), ideally supporting federal or regulated customers - Experience working with DoD, Civilian Agencies, Intelligence Community, or federal system integrators - Strong Python and JavaScript fundamentals, with deep systems thinking - Experience designing agent-based or LLM-powered systems beyond simple API calls, including multi-step workflows, orchestration, guardrails, and failure handling - Familiarity with secure cloud environments (AWS GovCloud, Azure Government, etc.) - Comfort operating in regulated environments with security, compliance, and documentation requirements - Experience supporting technical evaluations, architecture reviews, and competitive down-select processes - Ability to clearly explain technical tradeoffs to government stakeholders and build trust across engineering and program leadership - Ownership mindset — you take responsibility for mission outcomes, not just recommendations - Bias toward action and comfort operating in ambiguity - Excitement about running AI agents in real-world, production federal environments — not just building demos NICE TO HAVE - Experience deploying AI systems in classified or air-gapped environments - Familiarity with FedRAMP authorization processes or DoD Impact Levels (IL2–IL6) - Experience with LLM evaluation, observability, red-teaming, or guardrails in high-assurance settings - Hands-on experience with AWS, Azure, containers, Kubernetes, and secure networking architectures - Experience working alongside federal system integrators (Booz Allen, SAIC, Leidos, etc.) - Production experience with LangChain, LangGraph, or similar agent frameworks COMPENSATION & BENEFITS We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. Benefits include things like medical, dental, and vision coverage, flexible vacation, a 401(k) plan, and life insurance. Actual compensation and offerings will vary based on role, level, and location. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. Annual OTE range: $185,000–$300,000 USD COMPENSATION PHILOSOPHY: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Location: Boston, MA Workplace: OnSite ABOUT US At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale. We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry. More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale. ABOUT THE TEAM The Deployed Engineering team works directly with companies building and running AI agents in production, helping turn ideas and prototypes into systems teams can rely on. This is a hands-on, highly technical team that partners closely with customer engineers across the full lifecycle, from pre-sales evaluations to post-deployment advisory work. The focus is on achieving the technical win, co-designing agent architectures, and helping customers operate agents reliably at scale using the LangChain suite. Deployed Engineers sit at the intersection of engineering, product, and go-to-market, shaping how LangChain is adopted in the field and feeding real-world insights back into the platform. ABOUT THE ROLE The Deployed Engineer…You’ll work on some of the hardest problems in applied AI — not demos, not research, but systems that real teams depend on in production. The feedback loop is fast, the impact is visible, and the work you do directly shapes how AI agents are built in the real world. WHAT YOU’LL DO - Co-architect and co-build production AI agents with customer engineering teams - Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations - Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows - Advise customers post-sale on architecture, best practices, and roadmap-level decisions - Run technical demos, trainings, and workshops for developer audiences - Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers - Occasionally contribute code upstream when it meaningfully improves customer outcomes WHAT YOU’LL BRING - 3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up - Strong Python, JavaScript and systems fundamentals - Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling - Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations - Can explain technical tradeoffs clearly and build trust with developer audiences - Take responsibility for outcomes, not just recommendations - Have a bias toward action and enjoy figuring things out as you go - Are excited about operating AI agents in production, not just building demos NICE TO HAVE’S: - You’ve deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks - Worked with LLM evaluation, observability, or guardrails - Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts - Have shipped and operated production software and are comfortable owning systems under real-world constraints COMPENSATION Annual OTE range: $185,000–$300,000 USD COMPENSATION PHILOSOPHY: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Location: New York, NY Workplace: OnSite ABOUT US At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale. We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry. More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale. ABOUT THE TEAM The Deployed Engineering team works directly with companies building and running AI agents in production, helping turn ideas and prototypes into systems teams can rely on. This is a hands-on, highly technical team that partners closely with customer engineers across the full lifecycle, from pre-sales evaluations to post-deployment advisory work. The focus is on achieving the technical win, co-designing agent architectures, and helping customers operate agents reliably at scale using the LangChain suite. Deployed Engineers sit at the intersection of engineering, product, and go-to-market, shaping how LangChain is adopted in the field and feeding real-world insights back into the platform. ABOUT THE ROLE The Deployed Engineer…You’ll work on some of the hardest problems in applied AI — not demos, not research, but systems that real teams depend on in production. The feedback loop is fast, the impact is visible, and the work you do directly shapes how AI agents are built in the real world. WHAT YOU’LL DO - Co-architect and co-build production AI agents with customer engineering teams - Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations - Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows - Advise customers post-sale on architecture, best practices, and roadmap-level decisions - Run technical demos, trainings, and workshops for developer audiences - Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers WHAT YOU’LL BRING - 3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up - Strong Python, JavaScript and systems fundamentals - Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling - Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations - Can explain technical tradeoffs clearly and build trust with developer audiences - Take responsibility for outcomes, not just recommendations - Have a bias toward action and enjoy figuring things out as you go - Are excited about operating AI agents in production, not just building demos NICE TO HAVE’S: - You’ve deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks - Worked with LLM evaluation, observability, or guardrails - Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts - Have shipped and operated production software and are comfortable owning systems under real-world constraints COMPENSATION Annual OTE range: $185,000-$315,000 USD COMPENSATION PHILOSOPHY: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Location: US, San Diego, CA Workplace: OnSite Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. About The Role As Director of Autonomy at Humanoid, you will lead the team that teaches our bipedal platform to perform locomanipulation tasks in homes and service environments: tidying a room, loading and unloading a dishwasher, putting groceries away, folding laundry, and similar tasks. The output of your team is capabilities that run reliably on real robots and pass our evals, not papers or one-off demos. This is a player-coach role: you own the roadmap, hiring, and delivery, while also training policies, curating data, and debugging failures on the robot yourself. That hands-on work is how you lead: it's how you find what slows the team down and fix it for everyone. This role sits at the intersection of applied deep learning, robotics, and people leadership, working closely with the Controls team on whole-body teleoperation and combining RL controllers with learned policies, and with the Core Technology team on foundation model pre-training for five-finger dexterity and RL methods. If you have hands-on experience with real robot hardware, a track record in applied deep learning, and have led engineers doing this kind of work, we want to hear from you. What You'll Do - Own The Capability Roadmap: Work with product to identify use-case priority, choose the execution strategy, define the evals, and deliver on the agreed timeline. - Lead The Team: Hire, develop, and manage a team of robot learning engineers; set engineering and experimental standards; run day-to-day prioritization. - Co-Own The System 0/1 Interface: Agree with the Controls team on what the policy commands (end-effector targets, root velocity, pelvis height, foot targets, hand joint targets), debug failures across that boundary, and push requirements into the whole-body control roadmap. - Partner On Data Strategy: Specify what good data looks like for bipedal locomanipulation with data collection and external acquisition organizations, ensure diversity and coverage, iterate on operator instructions, and supervise collection on the biped. - Drive Sim-To-Real Transfer: Work with the Simulation team to set up RL training on the digital twin for bipedal tasks, then iterate on reward and simulation quality to ensure transfer to the real world. - Shape Base Model Development: Feed bipedal requirements — embodiment coverage in the pretraining mixture, action representations, latency budgets — into the VLA Core Technology team's base model development, and adopt new base models as they land. - Inform Hardware Design: Interface with the Hardware Design team so findings about hands, arms, and legs on the current generation are reflected in future designs. - Report On Progress: Communicate progress to leadership in terms of capabilities working on the robot, eval results, and failure analysis. WHAT WE'RE LOOKING FOR - 5+ years working on robots (industry or research) with shipped artifacts to show for it, including learned manipulation or locomanipulation policies deployed on real hardware. - Experience leading a team of 5+ engineers doing applied robot learning, including hiring and people management, with capabilities delivered to a deadline. - Hands-on today: you've trained and debugged policies within the last year and intend to keep doing so, and can curate data, post-train a policy, and diagnose failures without delegating that work. - Experience post-training VLA-class policies (autoregressive, diffusion, or flow-matching based) via behavior cloning and/or RL. - Solid understanding of the modern teleoperation and low-level control stack — whole-body control, inverse kinematics, RL locomotion controllers — and the ability to tell whether a failure lives in the policy, the controller, or the data. - Familiarity with deep learning infrastructure: streaming datasets, checkpointing and state management, distributed training, PyTorch or JAX. - Strong grounding in modern software engineering practices, with the ability to document experiments clearly and communicate trade-offs crisply to engineers and leadership. NICE TO HAVE - Experience with multi-fingered dexterous hands: teleoperation and retargeting, tactile sensing, in-hand manipulation. - Experience with legged platforms: bipedal locomotion, whole-body control, humanoid teleoperation. - Experience applying RL to robotics problems in simulation or on real robots. - Familiarity with Physical Intelligence (π) models, NVIDIA GR00T, or similar open VLA frameworks. - Publications at top-tier robotics or deep learning conferences (CoRL, RSS, ICRA, NeurIPS) or equivalent open-source contributions. WHAT WE OFFER - Comprehensive health coverage for US‑based employees, including fully paid medical, dental, and vision insurance, with virtual care and employee assistance resources. - Meaningful time off to rest and recharge: 23 days of PTO (accrued), separate sick leave, and paid company holidays. - 401(k) retirement plan with 4% employer match. - Competitive equity: stock options with meaningful upside as we scale. - Free daily catered lunch, snacks, and drinks in‑office. - Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics. - Freedom to influence the product and own key initiatives. For this role in California, the expected base salary range is $300,000-$450,000 USD per year; your placement in that range depends on how experience maps to our internal leveling.

Location: United States (Remote) Workplace: Remote About the Role The Senior Analytics Engineer will help build the reusable analytical models, tools, and systems to power decisions across Temporal. This role reports to the Director of Data Engineering and Analytics. Ultimately, your goal is to make it possible for every Temporal employee to produce first-class analyses. You will work across multiple business domains, including Product, GTM, Finance, and Engineering to deeply understand their needs and build solutions that enable both people and agents to reliably answer their analytical questions. You will architect and implement the data layer that sits between our landed source data and our various user interfaces via canonical entities, shared dimensions, metric definitions, data marts, etc. That includes making core concepts such as accounts, namespaces, activation, billable usage, and revenue consistent wherever they are used. You will take direct ownership of work that is currently distributed across the Data Engineering and Analytics team, including data modeling, data mart and Omni Topic creation, and enablement. Along with helping to align our business logic across marts and dashboards, you will create clear technical governance and controls as you work to empower stakeholders across the business. You will work closely with Data Platform and Trust on orchestration, deployment, observability, access controls, performance, reliability, and cost. You will partner with Data Science and Applied AI on feature tables, model outputs, model consumption, and monitoring. You will work with business functions on source system hygiene, prioritization of tasks, and validating business meaning of shared metrics. The ideal candidate possesses strong SQL skills, sound business acumen, and deep expertise in building semantic products gained through years of experience across different tools, modeling frameworks, and subject areas. The candidate is comfortable with ambiguous business questions, knows when to build a reusable data product vs modify an existing one, and how to deliver practical solutions in keeping with an overall governance framework and strategy. You will be hands-on writing code, tests, documentation, and performing technical reviews. What You’ll Do - Build and maintain reusable analytical data products across Temporal's business domains. - Define canonical entities, shared dimensions, purpose-built marts, metric components, explicit grains, keys, lineage, and history behavior. - Design and maintain core parts of the Omni semantic layer and certified Topics, with clear definitions for measures, dimensions, joins, and time handling. - Establish and improve standards for modeling, metric implementation, documentation, and change management. Build automated checks for freshness, completeness, uniqueness, and relationships between datasets, and make failures visible to the teams that own them. - Reduce duplicated or conflicting logic across source tables, marts, Omni workbooks, dashboards, notebooks, and analytical agents. - Partner with Data Platform and Trust on deployment, orchestration, observability, access controls, performance, cost, and production reliability. - Partner with Data Science and Applied AI on feature tables, prediction outputs, model consumption, and monitoring. - Work with domain partners and senior stakeholders to turn ambiguous questions into precise definitions, reusable models, and trusted ways for teams to answer follow-up questions themselves. - Help qualified contributors outside Data Engineering and Analytics create tables, models, notebooks, and workbooks through controlled sandboxes, templates, automated checks, review, and promotion into shared-use environments. - Make important data products understandable and usable for people and analytical agents through documented contracts, metadata, ownership, quality status, and retrieval guidance. - Contribute to architecture decisions, design reviews, code reviews, and coaching while remaining hands on in SQL/code, modeling, testing, and documentation. What You’ll Focus on First - During your first several months, you will help the team: - Document ownership of the core Analytics Products portfolio and identify high-risk duplicated or conflicting metric logic. - Publish the initial canonical entity and mart architecture, including clear standards for grain, keys, history, lineage, and quality controls. - Launch governed contribution templates, automated checks, and a review and promotion process for qualified contributors outside DEA. - Create a practical certification and retirement process for metrics, Topics, dashboards, and shared data products. - Deliver an initial version of one agreed priority data product, such as a revenue, PLG, or account-level model. What You’ll Bring - Advanced SQL experience, including the ability to reason about correctness, performance, and maintainability. - Working proficiency in Python to build and maintain jobs that create and alter tables in the data lake. - Experience with data transformation frameworks such as DBT, SQLMesh, etc - Data mart design experience using modeling techniques such as dimensional and entity centric modeling. - Experience implementing and maintaining a semantic layer or governed metrics. - Advanced knowledge of grain, keys, history, lineage, contracts, and quality controls. - Git, review, automated tests, CI/CD, and production ownership. - Translating ambiguous questions into reusable products. - Improving self-service while maintaining shared definitions. - Working with senior business and technical stakeholders. Helpful Experience - S3, Athena, Iceberg, Hive, or another open lakehouse architecture. - Hands-on experience with Omni Analytics, Looker, or a comparable semantic layer. - Experience orchestration systems. - Usage-based B2B SaaS, cloud infrastructure, developer tools, or consumption-based revenue. - Building governed contribution programs. - Designing metadata or semantic products for analytics agents.   Temporal Technologies is an Equal Opportunity Employer. Temporal Technologies does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status, or any other basis covered by appropriate law. All employment is decided on the basis of qualifications, merit, and business need. We embrace and celebrate differences and diversity. Temporal is committed to providing access, equal opportunity, and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. If you need to request a reasonable accommodation, please let your Recruiter know so we can assist.

Location: Boston, MA Workplace: OnSite At WHOOP, we're on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. Our wearable technology collects rich physiological data, providing members with actionable insights into their recovery, training, and sleep. We are seeking a Senior Signal Processing Engineer to develop and deploy algorithms for physiological sensors including optical sensor and inertial measurement unit (IMU) data into accurate, reliable, and real-time insights on WHOOP devices. This hands-on role centers on digital and statistical signal processing, sensor fusion, and model-based state estimation, with a particular focus on accelerometer and gyroscope data. You will own algorithm development from sensor characterization, mathematical modeling, and Python prototyping through efficient C/C++ implementation, firmware integration, and production validation. Working closely with Firmware, Hardware, Data Science, and Product, you will deliver robust on-device algorithms that perform reliably across diverse members and real-world conditions while meeting the constraints of wearable hardware. RESPONSIBILITIES - Design and develop real-time signal processing and sensor fusion algorithms that combine Sensor measurements inputs to characterize motion and deliver reliable member-facing insights. - Develop and tune model-based estimators for orientation and motion, including sensor bias estimation, uncertainty propagation, and drift mitigation under changing movement and sensing conditions. - Build robust IMU processing pipelines, including filtering, calibration, time synchronization, resampling, coordinate transformations, and compensation for sensor noise and systematic errors. - Analyze large-scale, noisy wearable datasets to identify failure modes and improve robustness across motion patterns, device orientation, fit, and individual movement characteristics. - Translate algorithm prototypes into production-ready C/C++ implementations, balancing accuracy with power, memory, compute, numerical stability, and latency. - Partner with Firmware and Hardware teams on sensor configuration, sampling strategies, and on-device integration; investigate differences between prototype behavior and performance on target hardware. - Define performance metrics and rigorous validation plans using reference measurements, controlled experiments, real-world data, and on-device testing to assess accuracy, robustness, and long-duration stability. - Own technical decisions for complex algorithm components, contribute to design and code reviews, mentor teammates, and collaborate across functions to investigate post-deployment issues and drive continuous improvement. QUALIFICATIONS - 5+ years of experience developing and deploying signal processing, sensor fusion, or state-estimation algorithms for real-world sensor applications, including substantial hands-on work with IMU data. - MS or PhD in Electrical Engineering, Biomedical Engineering, Computer Science, Applied Mathematics, or a related quantitative field, or equivalent practical experience. - Strong foundation in digital and statistical signal processing, including filtering, spectral analysis, stochastic processes, and sensor noise characterization. - Demonstrated experience with accelerometer and gyroscope processing, IMU calibration, sensor error modeling, bias estimation, and drift management. - Strong experience with Kalman filtering, including extended or error-state formulations, and model-based sensor fusion; practical understanding of nonlinear estimation, uncertainty propagation, and estimator failure modes. - Practical knowledge of 3D motion and orientation representations, including coordinate frames, rotation matrices, quaternions, and rigid-body kinematics. - Proficiency in Python for algorithm development, data analysis, and experimentation, and C/C++ for efficient embedded implementation; demonstrated experience deploying algorithms to resource-constrained hardware. - Ability to independently investigate complex algorithmic problems, design rigorous validation experiments, and communicate technical tradeoffs with firmware, hardware, and cross-functional partners. - Experience with body-worn sensors, human-motion modeling, motion segmentation, or periodicity analysis is highly desirable. This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office. Interested in the role, but don't meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply. WHOOP is an Equal Opportunity Employer and participates in E-Verify to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Location: Boston, MA Workplace: OnSite At WHOOP, we’re on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. WHOOP is hiring a Sensor Intelligence Engineer II to join the Sensor Intelligence team and help advance the next generation of biomedical optical sensing technologies. In this role, you will translate optical sensing concepts into validated systems and algorithms that enable meaningful physiological insights for WHOOP members. Working at the intersection of AD, hardware, firmware, data science, and human physiology, you will combine theoretical modeling, hands-on experimentation, and data analysis to inform future sensing capabilities and product development. RESPONSIBILITIES: - Drive the evaluation and development of next-generation optical sensing concepts, including the definition of technical requirements, design tradeoffs, and validation criteria - Analyze raw time-series and multidimensional optical sensor data and construct signal processing work flows or platform for new physiological sensing etc. - Develop, validate, and improve physiological sensing principle using physics modeling and/or experimentation. - Analyze time-series and multidimensional biomedical sensor data to assess performance, identify artifacts and failure modes, and generate actionable recommendations for system and algorithm improvements - Develop or improve the multiphysics numerical simulation platform to optimize sensor performance or evaluate potential sensing concepts. - Collaborate with partners across Hardware, Firmware, Data Science, Research, and Product to communicate technical findings, influence design decisions, and translate research into scalable WHOOP capabilities QUALIFICATIONS: - Master’s degree or PhD in Electrical Engineering, Biomedical Engineering, Applied Physics, Optics, or a related technical field, or an equivalent combination of education and practical experience - 3+ years of relevant industry or research experience developing biomedical optical sensing systems, signal processing methods, or algorithms - Demonstrated depth in: Analyzing raw time-series and multidimensional optical sensor data, including but not limited to: PPG signal AC, DC, Perfusion Index, Systolic Foot, Systolic Peak, Dicrotic Notch, Diastolic Peak, Crest Time, Diastolic Time, etc. - Experience analyzing time-series or multidimensional data from biomedical sensors, such as PPG, ECG, IMU, EMG, acoustic sensors, or medical imaging systems - Proficiency in Python, C, or C++ for data analysis, algorithm development, prototyping, or system implementation, with the ability to communicate technical findings clearly and deliver high-quality work as requirements evolve - Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions. PREFERRED QUALIFICATIONS: - Familiarity with biomedical optics or biophotonics, with expertise in one or more areas such as diffuse optical spectroscopy, Raman spectroscopy, tissue optics, experimental optics, or a related field. - Experience with physiological sensing principles, understanding of the human circulation system and respiration system. - Hands-on experience designing, building, and characterizing optical sensing systems or laboratory experiments using components such as LEDs, lasers, photodetectors, lenses, collimators, and optical filters.