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Location: Sainte-Anne-de-Bellevue, Canada 📍 Location: Sainte-Anne-de-Bellevue, QC, Canada 📄 Employment Type: Full-Time 🏢 Work Arrangement: Remote Opportunity for US and Canadian citizens and relocation assistance and bonus for UK and European citizens. 🚀 Start Date: TBD 💰 Compensation: TBD — based on skills and experience Important Screening Requirements Must have a B.Sc. in Electrical Engineering, Aerospace Engineering, Physics, or a related field . 10+ years of experience preferred , but candidates with slightly less experience may be considered based on the strength and relevance of their GNC/AOCS background Strong experience in orbital mechanics, attitude dynamics, and control-law design . Experience implementing real-time filtering and control algorithms in embedded code . Hands-on experience with navigation sensors and actuators . Strong aerospace systems engineering background. Experience supporting systems integration, testing, and troubleshooting . Experience with Confluence and JIRA . Ability to work independently with limited supervision. Excellent written and verbal communication skills in an English and French work environment . About the Role We are seeking a senior Guidance, Navigation & Control (GNC) Systems Engineer to support the design, integration, verification, and operational support of GNC subsystems for current and next-generation remote sensing and communications satellites . This role spans the complete GNC subsystem lifecycle—from algorithm development and hardware implementation through integration, verification, and on-orbit operational support. The ideal candidate brings deep expertise in orbital dynamics, attitude determination and control, navigation, and real-time flight software integration , while being comfortable working closely with subsystem and mission operations teams throughout the spacecraft lifecycle. What We're Looking For We’re looking for a senior GNC/AOCS systems engineer who can work across the full spacecraft lifecycle and bridge GNC algorithms, spacecraft hardware, flight software, systems engineering, integration, verification, and mission operations. The ideal candidate goes beyond analysis and simulation, with hands-on experience translating GNC concepts into integrated spacecraft systems and supporting those systems through testing and on-orbit operations. Key Responsibilities Architecture & Requirements Derive GNC functional, performance, and safety requirements from mission-level requirements. Define requirements related to pointing accuracy, slew rates, orbit-maintenance ΔV, and fault tolerance. Develop and maintain System Requirements, Interface Control Documents (ICDs), and system description documentation . Manage requirements and maintain bidirectional traceability throughout the project lifecycle. Modeling & Trade Studies Conduct trade studies involving navigation sensor suites and actuator families. Perform high-fidelity spacecraft dynamics simulations using MATLAB/Simulink and STK . Develop and maintain error budgets. Analyze control-law stability, fuel consumption, and survivability margins. Evaluate GNC architectures and design alternatives. Algorithm Development Design and implement attitude-determination algorithms. Develop orbit-determination solutions. Develop control laws for: 3-axis stabilization Slew maneuvers Station keeping Validate algorithms using Monte Carlo analysis and worst-case scenarios . Support Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) testing on avionics test benches. Cross-Functional & Systems Engineering Translate GNC algorithms into requirements for real-time flight software. Collaborate with C&DH and flight software teams on telemetry/telecommand, schedulers, and FDIR logic. Present technical status, risk analyses, and trade-study results to senior management. Negotiate and maintain ICDs with other spacecraft subsystems. Support Phase 0 and pre-Phase A activities. Hardware Selection & Design Specify GNC sensor and actuator hardware. Define electrical, mechanical, and thermal interfaces. Conduct trade studies involving redundancy architectures. Work with structures and thermal engineering teams to ensure appropriate mounting, thermal paths, and vibration survivability. Review harness routing and power distribution for GNC electronics. Integration & Verification Develop Verification Plans, Test Matrices, and Validation Procedures for unit, subsystem, and spacecraft-level GNC testing. Lead functional testing, including: Sensor calibration Actuator command-response testing Closed-loop attitude-control testing On-orbit checkout sequences Conduct FMEA, FTA, and reliability assessments . Support system integration and troubleshooting activities. Technical Leadership & Documentation Mentor and guide junior engineers and interns. Serve as a technical liaison with customers, partners, and external vendors. Contribute to continuous improvement initiatives, engineering standards, and organizational knowledge bases. Support technology road-mapping and innovation activities. Maintain GNC standards covering sensor/actuator interfaces, filter design, and control-law documentation. Ensure analyses, software, deliverables, and technical documentation are properly version controlled. Support major design reviews including SRR, PDR, CDR, MRR, and TRR . On-Orbit & Operations Support Support operational procedures for detumble, orbit maintenance, safe mode , and related GNC activities. Provide real-time GNC support during launch, early-orbit checkout, and contingency events . Analyze on-orbit telemetry and update filter parameters as required. Generate GNC performance reports. Support Satellite Operations Handbook (SOOH) development and operator training. Required Qualifications Bachelor’s degree in Electrical Engineering, Aerospace Engineering, Physics, or a related field . 10+ years of experience, but candidates with slightly less experience may be considered based on the strength and relevance of their GNC/AOCS background Strong knowledge of: Orbital mechanics Attitude dynamics Control-law design Classical and modern optimal control Experience with navigation sensors and actuators. Experience implementing real-time filtering and control algorithms in embedded code. Strong understanding of aerospace systems engineering fundamentals. Hands-on experience supporting systems integration, testing, and problem resolution. Experience with Confluence and JIRA . Ability to work efficiently with limited supervision. Strong analytical and problem-solving capabilities. Excellent written and verbal communication skills. Preferred Qualifications Master’s degree in Electrical Engineering, Aerospace Engineering, Physics, or a related field. Specialized academic or professional background in flight dynamics, control theory, or navigation . Experience across the complete spacecraft GNC lifecycle, from concept and algorithm development through integration and on-orbit operations. Experience with MATLAB/Simulink and STK . Experience with SIL/HIL testing and avionics test benches . Experience with spacecraft FDIR, telemetry/telecommand, and flight software integration . Experience supporting spacecraft design reviews and Phase 0/pre-Phase A activities. Experience with GNC hardware interfaces, redundancy architectures, and spacecraft environmental considerations. Experience conducting FMEA, FTA, and reliability analysis .

-Разрабатывать и выводить в промышленную эксплуатацию решения на базе ИИ, в том числе AI-агентов на базе LLM. -Проектировать архитектуру AI-агентов и подходы к их построению.

Location: Berlin Office; Bosnia; Romania; Norway; Estonia; Latvia; Slovenia; Italy; Netherlands; Hungary; Portugal; France; Poland; Serbia; Germany; Austria; Finland; Albania; Lithuania; Slovakia; Ireland; Bulgaria; Denmark; Belgium; Czech Republic; United Kingdom; Croatia; Spain; Greece Workplace: Remote The AI orchestration of your wildest imagination. n8n is the open workflow orchestration platform built for the new era of AI. We give technical teams the freedom of code with the speed of no-code, so they can automate faster, smarter, and without limits. Backed by a fiercely inventive community and 500+ builder-approved integrations, we’re changing the way people bring systems together and scale ideas for impact. Since our founding in 2019, we’ve grown into a diverse team of over 260 - working across Europe and the US, connected by a shared builder spirit and with our centre of gravity in Berlin. Along the way, we’ve: - Cultivated a community of more than 650,000 active developers and builders - Earned 190K+ GitHub stars, making us one of the world’s Top 40 most popular projects - Backed by top investors, from Sequoia’s first German seed to our SAP's recent strategic investment - bringing us to a $5.2bn valuation That’s the company we’ve built. Now we’d love to see what you can build. If you’re applying, try n8n out - whether you’re technical or not - and share a screenshot of your first workflow with us. The easiest place to start is here: app.n8n.cloud/register http://app.n8n.cloud/register. We’re in a defining moment of an incredible journey. Come and build with us. Your main goal will be to make it simple and reliable for customers to buy, pay for, and grow their use of n8n. You'll own the foundations of how n8n's commercial system works: licensing and entitlements, customer accounts, and the metering and billing platform for both Cloud and Enterprise. Today these live across several teams and two billing stacks; you'll bring them into one stable home that the rest of the company builds on. This is a foundations role. You build the system that pricing and packaging decisions run on, rather than running price experiments yourself. To do so, here are your core responsibilities: OWN THE LICENSING & BILLING STRATEGY & ROADMAP - Define and communicate the product strategy and roadmap for Licensing & Billing in partnership with Engineering, Product leadership, Finance, and go-to-market. - Prioritize foundational investments based on revenue integrity, enterprise needs, data correctness, and engineering effort, and make trade-offs explicit. - Lead discovery with enterprise customers, Sales, Customer Success, Finance, and RevOps to understand how n8n is bought, renewed, and grown today, and where that breaks. LICENSING, ENTITLEMENTS & ACCOUNTS - Own the license server and entitlement model: issuance, renewal, and validation across Cloud, self-hosted, air-gapped, and OEM deployments, with one source of truth for which plan or deal grants which features. - Introduce "account" as a first-class product primitive (accounts, purchases, license keys, activations) and expose it as the API contract our customer-facing console and multi-instance management are built on. - Productize the deal-to-entitlement flow so a closed deal in Salesforce creates or updates entitlements without manual steps. METERING & BILLING PLATFORM - Own usage metering and billing for Cloud and Enterprise, including subscription billing: what counts as a billable execution, quota consumption, and overage charging. - Lead the migration off Paddle Classic and unify Cloud and Enterprise billing into one platform. - Build the mechanics for usage-based enterprise billing and reliable overage charging. CROSS-ORG ALIGNMENT - Make the architecture and mechanism decisions (entitlement model, billing platform, enterprise invoicing path) and align closely with Finance and go-to-market on each of them. Every decision here touches money; you bring people along rather than inform them afterwards. - Hold clear boundaries with adjacent teams: the customer console, execution limits, and access control live elsewhere; you own the data and mechanics they rely on. - Establish clear success measures and keep the squad accountable for outcomes, not only delivery. REQUIREMENTS Must-haves - Billing or licensing product experience: You have 6+ years in Product, with a substantial part spent owning a billing, licensing, entitlement, or quote-to-cash system at a B2B SaaS company. - Shipped through Finance and RevOps: You've driven a cross-functional systems change where Finance, Sales Ops, and Engineering all had a say, and you got it live. - Systems and data-model depth: You can design or critique an entity model, reason about reconciliation and idempotency in a sync, read an API contract, and explain why two systems reading the same events end up with different numbers. - Attention to detail: You know that if the billing data is wrong, everything built on it inherits the error, and you sequence work accordingly. - Prioritization under ambiguity: You make trade-offs explicit and connect foundational or data-quality work to commercial outcomes. - Ownership and autonomy: You grab problems without being asked, fix what you see broken, and drive outcomes without waiting for direction. - Exceptional communication: You're equally at home with engineers, the Finance team, and Sales, and you can make billing trade-offs legible to all of them. - Enterprise reality: You understand what self-hosted, air-gapped, and OEM deployments mean for licensing and metering. Nice-to-haves - Usage-based billing: You've owned usage-based or hybrid (subscription plus usage) billing. - Vendor billing stacks: You've worked hands-on with Paddle, Stripe, Chargebee, Zuora or similar, and know where they break at enterprise contract volume. - Migration experience: You've run a billing-provider migration. - Hands-on ability: You can read code, query data, or prototype, even if you're not shipping it. - CRM integration: You've worked on Salesforce or CRM-to-billing integrations. - Domain familiarity: You know workflow automation, agent orchestration, or open-source / developer-community products. - Writing culture: You default to writing things down to create clarity and alignment. Why join us? At n8n, you'll be in the middle of the AI revolution. The shift from deterministic automation to agent autonomy is a genuinely open design space, and n8n has a credible claim to owning it. Every plan we sell, every enterprise contract, and every agent run that gets metered and billed runs on the foundation you own: licensing, entitlements, accounts, and the billing platform across Cloud and Enterprise. Your work directly shapes how n8n makes money and how easily customers can buy, grow, and trust what they pay for, from self-serve Cloud to air-gapped and OEM deployments. You'll join one of Europe's fastest growing startups at the sweet spot: small enough to have real impact and be empowered, big enough that your work already reaches massive scale and serious enterprise customers. You'll work with experienced engineers, Finance, and go-to-market on problems where the details are the product, in an AI-native, empowered culture of humble, kind, yet driven and direct people, where you're hired to think, not just to execute. And with our fair-code model, a passionate open-source community gives you a direct feedback loop on everything you ship. Sound like a challenge you're excited to take on? Apply now, and help us build the commercial foundation of the future of reliable AI automation. n8n is an equal opportunity employer and does not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, gender identity, age, marital status, veteran status, or disability status. We can sponsor visas to Germany; for any other country, you need to have existing right to work. Our company language is English. You care about diversity and inclusion? We do too! Check out our Diversity, Inclusion and Belonging initiatives at n8n https://www.notion.so/Diversity-inclusion-and-belonging-n8n-c1bec2fff536422d868b1a438d990e35?pvs=21 (https://www.notion.so/n8n/Diversity-inclusion-and-belonging-n8n-c1bec2fff536422d868b1a438d990e35). Location disclaimer: If you see multiple job postings for the same role, it is most likely because we're hiring remotely for this role and posting in different locations to make sure every potential candidate can see the role. Please apply to the location you're the most likely to work from in the future. BENEFITS - Competitive compensation 💸 – We offer fair and attractive pay. - Ownership 💪 – Our core value is to “empower others,” and we mean it—you’ll get a slice of n8n with equity. - Work/life balance 🏖️ – We work hard but ensure you have time to recharge: - Europe: 30 days of vacation, plus public holidays wherever you are. - US: 20 vacation days, 8 sick days, plus public holidays wherever you are. - Health & wellness 🩺 – - Europe: We provide benefits according to local country norms.* - US: Multiple low-premium, low-deductible medical plans with coverage for individuals and families—plus a no-cost premium HDHP option with a pre-seeded HSA—along with dental and vision coverage. - Future planning 💰 – - Europe: We provide pension contributions according to local country norms.* - US: 401(k) retirement plan with a 4% employer match. - Financial security 🛡️ – - Europe: We provide benefits according to local country norms.* - US: Company-paid short-term and long-term disability insurance, plus life insurance to support you and your loved ones. - Career growth 📈 – We hire rising stars who grow with us! You’ll get €1K (or equivalent) per year to spend on courses, books, events, or coaching to level up your skills. - A passionate team 🤩 – We love our product, and we prove it with regular hackathons where we see who can build the coolest thing with it! - Remote-first 🌏 – Our team works remotely across Europe, with regular off-sites for team bonding. Some roles, like sales in the US, are hybrid—please check the job description. - Giving back 🤝 – We're big fans of open source, and you'll get $100 per month to support projects you care about. - Transparency 🙏 – We all know what everyone’s working on, how the company is doing—the whole shebang. *Country and/or State specific details provided in your employment agreement.

Location: Hybrid - San Francisco, New York City About Vercel: Vercel is the agentic infrastructure company, freeing people and agents to ship what's next. For more than a decade we've helped builders move from idea to production with speed, security, and exceptional developer experience. Now we're scaling our products for both agents and people to ship and run software, built in the open and trusted by OpenAI, PayPal, Ramp, Supreme, and millions of developers worldwide. About the role We are looking for a Machine Learning Engineer on our Trust & Safety Engineering team to build and operate the production systems that detect and stop abuse on the platform at internet scale. This is an engineering-first role, roughly 80% writing and shipping production code (services, pipelines, and infrastructure) and 20% modeling, taking detection and classification approaches from prototype to hardened systems. This is a hybrid role based in San Francisco or New York City, with three days a week in the office. What you’ll do Design, build, and operate production systems that detect and disrupt abusive behavior across the platform, with an emphasis on reliability, scalability, and observability Build and maintain machine learning (ML) infrastructure, including training pipelines, feature pipelines, serving infrastructure, and evaluation frameworks Ship large language model (LLM) and classical ML approaches for abuse detection and classification, turning prototypes into maintainable code Own systems end to end, from integrating a model into the platform through deployment, monitoring, and iteration Work with security, product, and infrastructure teams to turn abuse patterns and detection logic into production systems What you need 5+ years of software engineering experience building and operating production systems at scale Wrote production code in Python, plus JavaScript/TypeScript or Go Built and maintained ML infrastructure, such as training and serving pipelines, feature stores, or evaluation tooling Integrated LLMs into production systems, including prompt engineering and evaluation Bonus if you Built an open source ML tool Worked in the trust and safety, fraud, or abuse prevention domain Compensation & Benefits: Competitive compensation package, including equity. Inclusive Healthcare Package. Learn and Grow - we provide mentorship and send you to events that help you build your network and skills. Flexible Time Off. We will provide you the gear you need to do your role, and a WFH budget for you to outfit your space as needed. The San Francisco, CA base pay range for this role is $208,000.00 - $312,000.00. Actual salary will be based on job-related skills, experience, and location. Compensation outside of San Francisco may be adjusted based on employee location. The total compensation package may include benefits, equity-based compensation, and eligibility for a company bonus or variable pay program depending on the role. Your recruiter can share more details during the hiring process. Disclosures: Privacy: Please review our Job Applicant Privacy Policy for more information on how we handle your data. Equal Opportunity: Vercel is committed to fostering and empowering an inclusive community within our organization. We do not discriminate on the basis of race, religion, color, gender expression or identity, sexual orientation, national origin, citizenship, age, marital status, veteran status, disability status, or any other characteristic protected by law. Vercel encourages everyone to apply for our available positions, even if they don't necessarily check every box on the job description.

Location: San Francisco, CA Workplace: Hybrid About Niantic Spatial AI has learned from almost everything online, but it still struggles with the physical world, where 99.5% of economic activity happens. It doesn’t know where things are, how spaces fit together, or what changes over time. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. About the Team Localization is where the map meets the moment. Our localization team builds the living geospatial world model and the centimeter-level Visual Positioning System (VPS) that lets people and robots know exactly where they are, from any camera, almost anywhere on Earth. That system is learned, not hand-built. Our localization stack has moved from classical geometry to visual models trained on billions of posed images — models that have to be accurate to the centimeter, robust to lighting and seasons and clutter, and fast enough to run in production for every query. This team sits at the junction of research and production: we take what works in a paper, make it work on our data, and make it work for customers. About the Role We're hiring an AI Engineer, Computer Vision, to contribute to the deep learning models at the heart of our localization system. Working alongside experienced researchers and engineers, you'll help train and iterate on the visual models — feature extractors, matchers, pose regressors, and retrieval models — that turn a single image into a precise position in the world. This is a full-loop environment. You'll get exposure to the entire pipeline: the data the model learns on, training, evaluation, and the service that runs it in production. You'll be surrounded by people who care deeply about correctness — where a loss curve going down is not the same as a system getting better — and you'll grow into that rigor quickly. What You'll Do - Train and Evaluate Models — Implement, train, and evaluate visual localization models in PyTorch: local feature extraction and matching, image retrieval, pose regression, and scene-coordinate regression. Iterate on architectures, loss functions, and training loops to improve accuracy on real production captures. - Build Data Pipelines — Contribute to high-throughput ingestion and preprocessing pipelines that turn raw imagery, poses, and 3D point clouds into training-ready datasets, including curation, sampling, and augmentation. - Run Experiments at Scale — Run training jobs across GPU environments on our 30-billion-image corpus, working with senior engineers and ML Infrastructure to keep experiments reproducible and results interpretable. - Extend the Benchmark — Help build and maintain the evaluation suite for localization — pose accuracy, recall across lighting and season changes, and latency under production load — so results are comparable across model versions. - Support Production Deployment — Work with senior engineers to take models from checkpoint toward serving: export, inference optimization, and monitoring once live. Learn what it means to own a model end-to-end, not just hand off a checkpoint. - Bridge Research and Product — Work with the Research team and Real-World Test Lab to understand how field failures translate into training data and benchmark improvements. What You'll Bring - Experience training deep learning models for computer vision tasks in PyTorch — through coursework, research, internships, or early industry work. - Foundational understanding of at least one of: visual localization, learned feature matching, image retrieval, pose estimation, SLAM, or structure-from-motion, and the 3D geometry underneath them (camera models, epipolar geometry, pose parameterizations). - Comfort working with large image or 3D datasets — preprocessing, curation, and understanding how data quality affects model behavior. - Familiarity with GPU-based training and the ability to read and reason about training runs — identifying whether a bottleneck is in data loading, compute, or model architecture. - Strong Python skills and a habit of writing code that others can rerun and build on. - A bachelor's or master's degree in computer science, computer vision, robotics, or a related field, or equivalent practical experience. Nice to Have - Published or contributed to work on learned localization, visual place recognition, scene-coordinate regression, or vision foundation models (CVPR, ECCV, ICCV, NeurIPS, 3DV, or equivalent). - Experience with large-scale self-supervised or multi-view pretraining, or fine-tuning vision foundation models for geometric tasks. - Exposure to inference optimization for edge or mobile hardware (TensorRT, ONNX, CoreML, or similar). - Experience with petabyte-scale spatial data: point clouds, meshes, posed image collections, or geospatial indexing. - Background in robotics, augmented reality, 3D reconstruction, digital twins, or autonomous systems. - Prior industry or research internship experience shipping or contributing to a production CV system. Compensation & Benefits The expected salary range for this role is $165,600 - $221,000 per year. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more. Location & Work Model This role is based in our San Francisco, CA office on a hybrid schedule with a minimum of 3 days per week in office. Inclusive Application We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed — you may be exactly who we're looking for. Equal Opportunity Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. Candidate Privacy I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Location: 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 Staff Applied Machine Learning Scientist to develop and continuously improve production-ready edge algorithms that transform sensor data into accurate, reliable, and real-time physiological insights. In this role, you will develop innovative approaches that combine deep learning, machine learning, and signal processing to deliver meaningful value to WHOOP members while optimizing for accuracy, latency, power, and scalability across wearable platforms. You will work closely with a cross-functional team of scientists, engineers, physiologists, and hardware experts to solve challenging problems at the intersection of wearable sensing, physiological modeling, and applied AI. You will develop novel modeling approaches, and own complex algorithmic problems from early development through production deployment and continuous improvement. Your work will directly shape the future of WHOOP’s sensing capabilities and our ability to provide members with accurate, personalized, and actionable insights into their health and performance. RESPONSIBILITIES: - Design advanced algorithms that combine signal processing, physiological modeling, machine learning, and deep learning for physiological time-series and multimodal sensor data, with a focus on accuracy, robustness, and generalization across diverse members and real-world conditions. - Own the development and continuous improvement of production-ready edge algorithms that transform multimodal sensor data into accurate, reliable, and real-time physiological insights. - Analyze large-scale wearable sensor datasets to identify performance gaps, characterize challenging conditions, and drive data-informed algorithm improvements. - Define rigorous evaluation methodologies, validation frameworks, and performance metrics to assess algorithms throughout development and deployment. - Optimize algorithms for embedded deployment, balancing accuracy with power, memory, latency, and compute constraints across current and future wearable platforms. - Set technical direction and establish best practices for modeling, experimentation, validation, and algorithm development across complex sensing problems. - Pursue ambiguous, high-impact technical initiatives from research and prototyping through validation, production deployment, monitoring, and continuous improvement. - Collaborate closely with Data Science, Firmware, Software, Hardware, Product, and domain experts to translate algorithmic innovations into production-ready capabilities and member-facing features. - Stay at the forefront of advances in deep learning, machine learning, signal processing, edge AI, and physiological sensing, and translate relevant innovations into differentiated WHOOP capabilities. QUALIFICATIONS: - MS or PhD in Electrical Engineering, Biomedical Engineering, Computer Science, Machine Learning, Applied Mathematics, or a related quantitative field. - 7+ years of experience developing and deploying machine learning, deep learning, and/or signal processing algorithms for complex real-world applications. - Deep technical expertise in modern machine learning and deep learning methods, particularly for time-series and multimodal sensor data. - Strong foundation in digital and statistical signal processing, with the ability to combine classical signal processing techniques with modern learning-based approaches. - Strong proficiency in Python for algorithm development, experimentation, and large-scale data analysis; experience with C/C++ and embedded algorithm development is highly desirable. - Demonstrated ability to develop robust models using large, noisy, real-world datasets and achieve strong generalization across diverse conditions. - Experience designing rigorous experiments, defining performance metrics, performing error analysis, and translating findings into algorithm improvements. - Experience taking algorithms through the full development lifecycle, from research and prototyping through validation, deployment, monitoring, and continuous improvement. - Strong analytical and problem-solving skills, with the ability to navigate ambiguity and make sound technical decisions. - Experience with physiological signals, wearable sensors, or biomedical sensing is highly desirable. - Experience optimizing algorithms for resource-constrained embedded or edge environments, including tradeoffs across accuracy, power, memory, compute, and latency, is a plus. - Strong commitment to embracing and leveraging AI tools in day-to-day work while maintaining high standards for quality and rigor. Join us in pushing the boundaries of wearable sensing, applied AI, and physiological intelligence and positively impacting people’s lives! 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.

What you will do Build new strategic partnerships with travel companies, agency networks, airlines, OTAs, travel platforms, loyalty programs, fintech companies, travel-tech players, and other distribution channels. Build a specific partnership hypothesis for each account rather than sending a generic company presentation.

Creating new and improving existing AI solutions Involvement in the whole process of data-driven development from analyzing product requirements to launching the feature or system to production (with the help of MLOps specialists) and improving it based on results of monitoring and feedback.

Location: Moldova Company Background Coherent Solutions is a global digital product engineering company founded in 1995 and headquartered in Minneapolis, Minnesota. With a presence in nine countries and a team of more than 1,700 engineers, the company provides custom software development and engineering services across web and mobile applications, cloud, DevOps, and data. Coherent Solutions works with clients worldwide to build and scale digital products while fostering a culture of collaboration, transparency, and continuous professional growth. Project Description You will work with senior engineers on internal classical machine learning initiatives. The role combines structured learning, practical delivery, and gradual ownership of increasingly complex tasks. Technologies Python, pandas, NumPy, scikit-learn, Jupyter SQL XGBoost, LightGBM MLflow FastAPI Git, Docker, pytest What You'll Do Prepare and validate structured datasets for machine learning tasks, including cleaning, transformation, encoding, scaling, and missing-value handling; Conduct exploratory data analysis to identify patterns, data quality issues, and opportunities for modeling; Build, test, and compare ML models for tasks such as regression, classification, clustering, dimensionality reduction, and anomaly detection; Develop and select features with guidance from senior engineers; Run experiments, tune model parameters, and evaluate results using appropriate validation approaches and metrics; Help maintain reproducible ML pipelines, notebooks, experiment tracking, and technical documentation; Write and maintain automated tests for data and model-related code; Support the delivery of models through REST APIs or batch-processing workflows; Document model assumptions, results, limitations, and technical decisions; Work closely with senior engineers, incorporate feedback, and gradually take ownership of more complex tasks; Job Requirements 1+ year of hands-on Python development experience, including experience with pandas, NumPy, scikit-learn, and Jupyter; A strong foundation in statistics, probability, and linear algebra, plus exploratory data analysis skills; An understanding of supervised and unsupervised learning, and practical experience with regression, classification, clustering, dimensionality reduction, and anomaly detection; Experience preparing structured datasets, including cleaning, transformation, encoding, scaling, and missing-value handling, plus practical feature engineering and feature-selection skills; An understanding of train/validation/test splits, cross-validation, data leakage, overfitting, and regularization; the ability to select appropriate evaluation metrics, compare models, and perform error analysis; Familiarity with hyperparameter tuning and reproducible ML pipelines; SQL skills for data extraction and analysis; The ability to expose models through REST APIs or batch-processing workflows; Familiarity with Git, automated testing, Docker, and basic model monitoring; The ability to explain model behavior, assumptions, limitations, and results; English at B2 level or higher; Nice To Have XGBoost, time-series analysis, recommendation systems, MLflow, model interpretability, cloud services, production ML monitoring, and GenAI, LLM, RAG, prompt-engineering, or agent-development experience; What Do We Offer The global benefits package includes: Technical and non-technical training for professional and personal growth; Internal conferences and meetups to learn from industry experts; Support and mentorship from an experienced employee to help you professional grow and development; Health insurance; Sports activities to promote a healthy lifestyle; Flexible work options, including remote and hybrid opportunities; Referral program for bringing in new talent; Work anniversary program and additional vacation days.

2D Artist. to help shape the visual world of a mobile poker game set in a.

Location: Limassol Cyprus Workplace: OnSite ABOUT US Scorewarrior is a game developer and publisher based in Limassol, Cyprus. We are working on Total Battle — a cross-platform MMO 4X strategy game with a deep social system and extensive player interaction. We have 2 million monthly active players and continue to grow with the goal of becoming the #1 game in the genre. Our team consists of 200+ passionate, talented, self-driven, and continually developing professionals who love making high-quality games. ABOUT THE POSITION We are looking for a Senior Product Analyst to join our General Analytics team: the analytics lead, an analytics engineer and you. Product teams have their own analysts; we take the research questions they cannot answer on their own, and we own how analytics is done across the company. The core of the role is research: high-uncertainty product questions where the answer is the estimated effect of a specific factor, not a dashboard. Whether to extend the game's lifecycle, how to tell that a new giant feature works, how to run experiments on veteran players spread across hundreds of kingdoms. Alongside it you own the methodology: how experiments and effect estimates are done at Scorewarrior, and whether a number is good enough for a decision and useful to other analysts afterwards. It is a senior individual-contributor role: close work with the analytics lead, short cycles with game designers and product managers. Pilots of the infra components (experimentation platform, visualization system, AI analyst assistant, ELT) are driven by the analytics lead and the infrastructure itself by the data tech lead; you join as the customer, not the builder. Game industry experience is not required. WHAT YOU WILL BE DOING - Take high-uncertainty questions from product teams (Old Players, New Players, Quality of Life, Platform) from a vague question to a decision the team acts on. - Design and analyze experiments in non-standard setups: delayed treatment for veteran players, kingdom-level (cluster) tests, small samples, heavy-tailed revenue. Own variance reduction, outlier handling, permutation tests, MDE and power, reporting rules. - Estimate effects where a classic A/B test is impossible, using causal inference and quasi-experimental designs, and turn one-off solutions into methods other analysts reuse: reference documents, dbt data marts, Tableau dashboards. - Be the last check before the business acts on a number: the assumptions behind a metric, a test read-out or a pilot's output. - Use Python inside research when SQL is not enough: a graph algorithm over clan and kingdom networks, an optimization problem, a simulation. - Specify logging for features shipped by teams without their own analyst, and cover their ad-hoc questions. REQUIRED QUALIFICATIONS - Experience running research with high uncertainty end-to-end, from problem framing to a decision the team acted on. - Hands-on causal inference and quasi-experimental methods (difference-in-differences, synthetic control, matching or similar) on product questions: you can say what breaks an estimate and how you checked it. - Hands-on A/B test design and analysis, and the applied statistics behind it: distributions, power and MDE, variance reduction, heavy tails and outliers. - Strong SQL on an analytical data warehouse; ClickHouse is a plus. - Working Python for research: you can implement an algorithm or a statistical procedure that has no ready-made library call, and you understand what your code, or your AI assistant's code, does and whether it can work. No production services expected. - Clear, concise communication in writing and in person: you fix agreements, and you say so when a result cannot be trusted, including to the people who asked for it. - English at B1 or above. PREFERRED QUALIFICATIONS - Data science background alongside analytics: modeling, segmentation, validating assumptions with statistical tests. - Experience building or improving an experimentation platform, or setting methodology that other analysts followed. - Hands-on experience with dbt and Tableau. - Understanding of F2P economics, network effects, social or competitive systems; you play games and are ready to play Total Battle. WHAT WE OFFER - A team of professionals with a passion for gamedev and a drive to make Total Battle the #1 MMO 4X strategy game. - An excellent remuneration package, including a 13th salary. - Full relocation coverage for you and your family to Cyprus (Limassol). - A modern, sunny office close to the sea. - Private health insurance for you and your family members. - A monthly pre-school, school, and university allowance for employees' children. - Lunch compensation. - A sports and fitness allowance. - Company-funded personal development opportunities (professional and language courses, books, etc.). - Bonuses for outstanding results, years of service, marriage, and childbirth. - Friendly monthly office parties and epic annual offsite company birthday and Christmas celebrations.

Location: Limassol, Cyprus Workplace: on-site Mayflower is a technology company building high-load products used by millions of people worldwide. Operating at the scale of one of the world's top-50 websites, we solve complex engineering challenges and create solutions that power real-time entertainment for a global audience. We are looking for a Lead ML Engineer to own a new ML stream focused on fast delivery of applied machine learning solutions across different product and business domains. The stream will work with a broad range of ML challenges. Some initiatives may be relatively small and delivered within a few weeks, while others may prove their value and grow into larger dedicated projects. This role combines hands-on ML engineering with end-to-end technical delivery ownership. You will receive product problems and expected outcomes from Product or internal stakeholders, clarify the technical requirements and constraints, define the implementation approach, coordinate execution within the stream, and bring solutions to production readiness and launch. This is a highly hands-on role. The stream will not have a dedicated software engineer for every initiative, so we expect Data Scientists and ML Engineers to be comfortable working beyond experimentation and contributing directly to production code. The role is not tied to a single ML domain. We value strong ML fundamentals, engineering skills, pragmatism, and the ability to quickly understand new problem areas more than deep specialization in one particular class of models. Job Responsibilities Technical Delivery Ownership Turn product and business problems into concrete ML implementation plans. Clarify requirements, constraints, available data, integrations, and success criteria together with Product and relevant stakeholders. Define technical scope, milestones, dependencies, risks, and delivery estimates. Select appropriate ML approaches and determine the fastest reliable way to validate and implement them. Drive technical delivery through experimentation, implementation, integration, deployment, and launch readiness. Keep delivery on track, proactively identify blockers, and coordinate dependencies with other teams. Provide Product with clear technical options, trade-offs, estimates, risks, and experiment results required for product decisions. Support production rollout and iteration based on observed results. Hands-on ML & Engineering Design, train, evaluate, and deploy ML models across different domains and problem types. Write production-quality Python and contribute directly to implementation. Build APIs, batch jobs, data-processing pipelines, and ML services required to deliver solutions where appropriate. Work with classical ML, deep learning, and foundation-model-based approaches depending on the problem. Process and transform large production datasets using Python and SQL. Integrate models into existing production systems. Implement appropriate testing, monitoring, logging, and observability for delivered ML solutions. Work within the shared ML infrastructure, architecture, and engineering practices used across the company. Collaborate with Data Science, Backend, Data Engineering, and MLOps specialists when deeper domain expertise or infrastructure changes are required. Stream Execution Break initiatives down into concrete technical tasks and coordinate execution within the stream. Coordinate the work of Data Scientists and ML Engineers contributing to stream initiatives. Review technical approaches, experiments, and implementation. Keep the team focused on the agreed scope, priorities, and delivery timeline. Identify technical risks and dependencies early and drive them to resolution. Escalate architectural, infrastructure, or methodological questions when broader alignment is required. Help prepare successful initiatives for scaling or transition into longer-term ownership if they grow beyond the scope of the stream. Cross-functional Collaboration Work closely with Product throughout the delivery lifecycle. Independently gather the technical details and constraints required to execute on a product request. Communicate estimates, dependencies, technical trade-offs, and delivery status clearly. Work directly with Engineering and other internal teams to unblock implementation. Challenge technically unclear, contradictory, or infeasible requirements and propose practical alternatives. You’ll thrive here if you have 5+ years of commercial experience in Machine Learning, Data Science, or ML Engineering. Strong hands-on Python programming and software engineering skills. Experience taking ML solutions from a product requirement through experimentation, implementation, integration, and production. Strong understanding of machine learning methods, statistics, experimentation, and model evaluation. Experience writing maintainable production code rather than working exclusively in notebooks. Experience building APIs, services, batch processing, or data pipelines. Strong SQL skills and experience working with large production datasets. Practical experience with Docker and production deployment environments. Ability to turn partially defined problems into concrete technical plans. Ability to estimate work, identify dependencies and risks, and drive technical execution against a timeline. Experience owning technical delivery involving several contributors and coordinating work across dependencies. Experience reviewing code and technical approaches. Ability to work effectively within established engineering and ML practices while independently owning delivery within a stream. Strong communication skills and the ability to work directly with Product and technical stakeholders. Ability to balance speed and engineering quality: validate ideas quickly when uncertainty is high and build robust solutions when moving towards production. Nice to Have Previous experience as a Tech Lead, Stream Lead, or technical owner of ML initiatives. Experience with Kubernetes and CI/CD. Experience with Kafka or other streaming platforms. Experience with Airflow, MLflow, experiment tracking, model monitoring, or similar tooling. Experience building real-time or high-load ML services. Experience with FastAPI or similar Python service frameworks. Experience across several ML domains, such as recommendation systems, ranking, NLP/LLMs, Computer Vision, anomaly detection, forecasting, or classification. Experience with LLM inference, fine-tuning or other GenAI systems. Experience in teams where Data Scientists and ML Engineers own a substantial part of production implementation themselves.

разработка классических LLM агентов. разработка tools для агентов, а также skills-инструкций для LLM.

Location: San Francisco, CA | New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The Account Abuse team is tasked with ensuring Anthropic's computing capacity is allocated fairly, minimizing resources available to bad actors and preventing them from coming back. As a software engineer on this team, you will build the machine learning systems that help us detect and stop abuse at scale. The ideal candidate can see things from opponents' perspectives, understand their means and motives, and anticipate their responses to countermeasures. We're looking for full stack machine learning engineers with experience across model training, productionization, and evaluation. You'll also look for ways to use Claude to speed up how these models get built and maintained. This is classical ML on structured and behavioral data. You do not need a deep learning background or knowledge of LLM internals. What matters is that you have trained and shipped models where the stakes are real, and that you care about building robust production systems as much as the model itself. A false positive here is a legitimate customer locked out, so measurement, precision, and safe rollout are part of the job. Key responsibilities Build and operate a feature computation platform that serves both model training and real-time scoring, with point-in-time correct training data and low-latency online retrieval Train, evaluate, and deploy models that detect account-level abuse and fraud, running them both offline and online Build tooling that automates more of the model development lifecycle, including using Claude to speed up feature development, training, and evaluation Make backtesting, shadow deployment, and staged rollout the default path to production, with monitoring for training / serving skew, drift, and adversarial adaptation Work with our data scientists and our Policy & Enforcement team to improve label coverage and quality Partner with product and platform teams to gather signals and integrate model decisions with minimal impact on their systems' latency, stability, or overall architecture Minimum qualifications Proficiency in Python and SQL Experience training machine learning models and deploying them to production Experience building data pipelines with a batch processing engine (e.g., Spark, Beam) and a workflow scheduler (e.g., Airflow) Working understanding of point-in-time correctness and training / serving skew, and how to prevent both Strong communication skills and ability to explain technical tradeoffs to non-technical stakeholders Preferred qualifications Experience building or operating a feature platform such as Chronon, Feast, or Tecton Experience with stream processing engines such as Flink, Beam / Dataflow, or Kafka Streams Experience training ML models in a production setting with demanding serving requirements, such as fraud, risk, or ranking Experience with tree-based models on tabular data Experience building unsupervised, clustering-based or graph-based detection systems to surface coordinated account abuse Experience in integrity, spam, fraud, or abuse detection Experience working with scarce, delayed, or noisy labels Experience with AutoML or other approaches to automating the ML workflow Care about the societal impacts of AI and want your work to make powerful systems safer The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Canada (Remote) Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are a 100% remote company with team members across 40+ countries, backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Learn more at grafana.com and follow us on LinkedIn and X . We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do. You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity. This is a remote opportunity, and we would be interested in applicants located in the US or Canada. The Opportunity: At Grafana Labs, we build observability tools that help users understand, respond to, and improve their systems – regardless of scale, complexity, or tech stack. We recently started a skunkworks initiative with a mission to bring observability to the rest of the business. Our goal is to make Grafana the single best place where humans and AI agents understand and act on real-time data from across the enterprise. As part of this initiative, we are building an AI-native data intelligence system that gives agents reliable, governed access to enterprise context. This system helps agents retrieve the right data, metadata, definitions, lineage, quality signals, and institutional knowledge without every agent builder having to maintain their own brittle context files. The principle is simple: the context system retrieves, agents decide, and data teams maintain the intelligence. The new team is a mix of seasoned Grafanistas and new hires. We operate with a high degree of autonomy and ownership, both as individuals and as a team. Engineers are empowered to make decisions, move quickly, and validate ideas early – while being supported by a deeply collaborative culture that values curiosity, feedback, and cross-functional partnership. We’re looking for a Senior Software (Database) Engineer to help build the underlying storage system for our general purpose data platform. The database layer has both OLAP and OTLP capabilities to support classic analytics queries while also serving up freshly stored data. It embraces the separation of compute and storage and is designed to run on multiple cloud providers. This is an early-stage role on a high-autonomy team, so you should be comfortable working through ambiguity, making pragmatic architectural decisions, and building systems that can evolve from internal dogfooding to production-grade SaaS. As the team matures, there’s a broad opportunity to expand or redefine this role based on impact and initiative. What You'll Be Doing: Take ownership over and build one or multiple parts of the database: Design, implement, test, and operate ingestion, query planning, distributed query execution, data formats, storage formats. Create a scalable SaaS foundation: Help define and build the architecture for a multi-tenant database service, including tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries. Power agent-facing retrieval workflows: Build APIs and database interfaces that allow AI agents, MCP tools, CLIs, and internal applications to retrieve data quickly. Work across product and infrastructure: Partner with the team to make practical tradeoffs between fast experimentation and long-term reliability, especially as the project moves from prototype to production. Operate what you build: Instrument database services with metrics, logs, traces, alerts, and dashboards. Use observability tools to understand system behavior and improve reliability. Contribute to technical direction: Help shape the architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices for a new product area. Effective communication: You’ll be working in a highly dynamic and collaborative environment, so we need someone who can communicate effectively and contribute across teams. Ownership and impact: Take full ownership of the database solutions you develop, ensuring they are not only innovative but also scalable, maintainable, and aligned with real user workflows. What Makes You a Great Fit: Strong engineering skills: Solid experience building production-grade, user-facing software systems. You’re a self-starter, capable of tackling complex engineering problems and making design decisions with minimal supervision. AI experience with a practical mindset: You’re familiar with AI technologies and frameworks, and you focus on delivering high-quality solutions that work in the real world, not just in theory. Quick iteration and experimentation: You’re comfortable releasing prototypes, collecting feedback, and iterating with a pragmatic mindset. Proven initiative: You take ownership and drive projects forward, pushing boundaries to find the most impactful solutions. You can deal with ambiguity and are able to define scope where things are loosely defined. Collaborative attitude: You communicate effectively with your peers. You’re open to feedback, and you bring a solutions-oriented mindset to the table. Experience with distributed systems, catalogs and table formats, query engines. Mastery of a programming language like golang, C++, or Rust Proven track record of delivering software that made it into production and is actively used by users. Exposure to working in cloud-native environments (e.g., AWS, GCP, Azure). Experience using observability tools to understand and troubleshoot system behavior. Bonus Points for: Experience building distributed query engines. Experience building data warehouses and/or data lakes. Experience building tools for data engineering. How we work: We are a remote-first team that meets regularly over video and does most of our work asynchronously, in writing. We value creativity, diverse perspectives, and clear communication. Tempo is relied upon by prominent global organizations to monitor critical applications and infrastructure, and we expect everyone on the team, including our Staff engineers, to contribute ideas that make it a more reliable, more useful, and more loved product. Compensation & Rewards: In Canada, the Base compensation range for this role is $ 164,490 - $197,389. Actual compensation may vary based on level, experience, and skillset as assessed throughout the interview process. All of our roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success. We believe in shared outcomes—RSUs help us stay aligned and invested as we scale globally. *Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process. Why You’ll Thrive at Grafana Labs: 100% Remote, Global Culture - As a remote-only company, we bring together talent from around the world, united by a culture of collaboration and shared purpose. Scaling Organization – Tackle meaningful work in a high-growth, ever-evolving environment. Transparent Communication – Expect open decision-making and regular company-wide updates. Innovation-Driven – Autonomy and support to ship great work and try new things. Open Source Roots – Built on community-driven values that shape how we work. Empowered Teams – High trust, low ego culture that values outcomes over optics. Career Growth Pathways – Defined opportunities to grow and develop your career. Approachable Leadership – Transparent execs who are involved, visible, and human. Passionate People – Join a team of smart, supportive folks who care deeply about what they do. In-Person onboarding - We want you to thrive from day 1 with your fellow new ‘Grafanistas’ to learn all about what we do and how we do it. Balance is Key - We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect. *We will comply with local legislation where applicable. Equal Opportunity Employer: Grafana Labs is an equal opportunities employer. We welcome applications from everyone regardless of race, colour, nationality, origin, caste, sex, gender reassignment identity or expression, sexual orientation, age, religion or belief, disability, veteran status, genetic information, pregnancy, maternity, marital, family or carer status, or any other characteristic which is protected by local law. We believe that equality and diversity build a strong organisation, and we work hard to ensure that is the foundation of our organisation as we grow. Grafana Labs may utilize AI tools in its recruitment process to assist in matching information provided in CVs to job postings. The recruitment team will continue to review inbound CVs manually to identify alignment with current openings. #LI-Remote For information about how your personal data is used once you’ve applied to a job, check out our privacy policy .

Location: United States (Remote) Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are a 100% remote company with team members across 40+ countries, backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Learn more at grafana.com and follow us on LinkedIn and X . We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do. You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity. This is a remote opportunity, and we would be interested in applicants located in the US or Canada. The Opportunity: At Grafana Labs, we build observability tools that help users understand, respond to, and improve their systems – regardless of scale, complexity, or tech stack. We recently started a skunkworks initiative with a mission to bring observability to the rest of the business. Our goal is to make Grafana the single best place where humans and AI agents understand and act on real-time data from across the enterprise. As part of this initiative, we are building an AI-native data intelligence system that gives agents reliable, governed access to enterprise context. This system helps agents retrieve the right data, metadata, definitions, lineage, quality signals, and institutional knowledge without every agent builder having to maintain their own brittle context files. The principle is simple: the context system retrieves, agents decide, and data teams maintain the intelligence. The new team is a mix of seasoned Grafanistas and new hires. We operate with a high degree of autonomy and ownership, both as individuals and as a team. Engineers are empowered to make decisions, move quickly, and validate ideas early – while being supported by a deeply collaborative culture that values curiosity, feedback, and cross-functional partnership. We’re looking for a Senior Software (Database) Engineer to help build the underlying storage system for our general purpose data platform. The database layer has both OLAP and OTLP capabilities to support classic analytics queries while also serving up freshly stored data. It embraces the separation of compute and storage and is designed to run on multiple cloud providers. This is an early-stage role on a high-autonomy team, so you should be comfortable working through ambiguity, making pragmatic architectural decisions, and building systems that can evolve from internal dogfooding to production-grade SaaS. As the team matures, there’s a broad opportunity to expand or redefine this role based on impact and initiative. What You'll Be Doing: Take ownership over and build one or multiple parts of the database: Design, implement, test, and operate ingestion, query planning, distributed query execution, data formats, storage formats. Create a scalable SaaS foundation: Help define and build the architecture for a multi-tenant database service, including tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries. Power agent-facing retrieval workflows: Build APIs and database interfaces that allow AI agents, MCP tools, CLIs, and internal applications to retrieve data quickly. Work across product and infrastructure: Partner with the team to make practical tradeoffs between fast experimentation and long-term reliability, especially as the project moves from prototype to production. Operate what you build: Instrument database services with metrics, logs, traces, alerts, and dashboards. Use observability tools to understand system behavior and improve reliability. Contribute to technical direction: Help shape the architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices for a new product area. Effective communication: You’ll be working in a highly dynamic and collaborative environment, so we need someone who can communicate effectively and contribute across teams. Ownership and impact: Take full ownership of the database solutions you develop, ensuring they are not only innovative but also scalable, maintainable, and aligned with real user workflows. What Makes You a Great Fit: Strong engineering skills: Solid experience building production-grade, user-facing software systems. You’re a self-starter, capable of tackling complex engineering problems and making design decisions with minimal supervision. AI experience with a practical mindset: You’re familiar with AI technologies and frameworks, and you focus on delivering high-quality solutions that work in the real world, not just in theory. Quick iteration and experimentation: You’re comfortable releasing prototypes, collecting feedback, and iterating with a pragmatic mindset. Proven initiative: You take ownership and drive projects forward, pushing boundaries to find the most impactful solutions. You can deal with ambiguity and are able to define scope where things are loosely defined. Collaborative attitude: You communicate effectively with your peers. You’re open to feedback, and you bring a solutions-oriented mindset to the table. Experience with distributed systems, catalogs and table formats, query engines. Mastery of a programming language like golang, C++, or Rust Proven track record of delivering software that made it into production and is actively used by users. Exposure to working in cloud-native environments (e.g., AWS, GCP, Azure). Experience using observability tools to understand and troubleshoot system behavior. Bonus Points for: Experience building distributed query engines. Experience building data warehouses and/or data lakes. Experience building tools for data engineering. How we work: We are a remote-first team that meets regularly over video and does most of our work asynchronously, in writing. We value creativity, diverse perspectives, and clear communication. Tempo is relied upon by prominent global organizations to monitor critical applications and infrastructure, and we expect everyone on the team, including our Staff engineers, to contribute ideas that make it a more reliable, more useful, and more loved product. Compensation & Rewards: In the US, the Base compensation range for this role is $ 154,445 - $185,334. Actual compensation may vary based on level, experience, and skillset as assessed throughout the interview process. All of our roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success. We believe in shared outcomes—RSUs help us stay aligned and invested as we scale globally. *Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process. Why You’ll Thrive at Grafana Labs: 100% Remote, Global Culture - As a remote-only company, we bring together talent from around the world, united by a culture of collaboration and shared purpose. Scaling Organization – Tackle meaningful work in a high-growth, ever-evolving environment. Transparent Communication – Expect open decision-making and regular company-wide updates. Innovation-Driven – Autonomy and support to ship great work and try new things. Open Source Roots – Built on community-driven values that shape how we work. Empowered Teams – High trust, low ego culture that values outcomes over optics. Career Growth Pathways – Defined opportunities to grow and develop your career. Approachable Leadership – Transparent execs who are involved, visible, and human. Passionate People – Join a team of smart, supportive folks who care deeply about what they do. In-Person onboarding - We want you to thrive from day 1 with your fellow new ‘Grafanistas’ to learn all about what we do and how we do it. Balance is Key - We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect. *We will comply with local legislation where applicable. Equal Opportunity Employer: Grafana Labs is an equal opportunities employer. We welcome applications from everyone regardless of race, colour, nationality, origin, caste, sex, gender reassignment identity or expression, sexual orientation, age, religion or belief, disability, veteran status, genetic information, pregnancy, maternity, marital, family or carer status, or any other characteristic which is protected by local law. We believe that equality and diversity build a strong organisation, and we work hard to ensure that is the foundation of our organisation as we grow. Grafana Labs may utilize AI tools in its recruitment process to assist in matching information provided in CVs to job postings. The recruitment team will continue to review inbound CVs manually to identify alignment with current openings. #LI-Remote For information about how your personal data is used once you’ve applied to a job, check out our privacy policy .

Сопровождение ML-решений (мониторинг сервисов и моделей, отслеживание дрейфа данных, работа над развитием); Оценка влияния на бизнес предложенных решений;

Location: Paris, France Workplace: fully ⛵ About our Crew Data is at a strategic turning point for us. Our ambition is clear: we want to turn Data into a real machine for product and business impact, rather than just a support team responding to requests. You will join a Product organization of 6 PMs leading feature squads. As our first dedicated Data Product Manager , you will partner with 4 Data Analysts to build and run our Data Squad , structured and operating just like a classic product squad. Together, your mission is to move away from a "request factory" mindset. The goal? Ensuring the majority of the team's time is dedicated to high-impact initiatives with clear objectives, measurable outcomes (ROI, time saved, KPI uplift), and defined timelines. 🚤 The Role This is a newly created position. Reporting directly to Camille (VP Product) , you will be embedded in the Data team as the dedicated Product partner for Louis (Head of Data). You will work daily with our Data Analysts to run their roadmap like a true product squad. This role is at the crossroads: you are neither a Data Analyst nor a classic PM who merely writes tickets. You are the person who will bring Product thinking into Data. 🛟 What floats your boat Strategy & Roadmap: You lead the Data team's backlog. You receive requests, challenge them, extract the real business stakes, and make clear trade-offs so the Analysts work on the highest-value topics. Product Methodology: You drive topics in the right direction. We start with a problem (not a request): for whom, why now, how do we measure success? You systematically seek to evaluate post-delivery impact (Did this analysis change a decision?). Hands-on Data Product (NAO): You will be the Product Manager for NAO, our company-wide AI and Data assistant. NAO is a game-changer designed to democratize data access, enabling anyone at Click&Boat to query company data and make data-backed decisions on their scope. As PM, you will drive NAO's company-wide rollout and continuous improvement hands-on: analyzing chat replays, understanding usage, testing and refining prompts, gathering BU feedback, and shipping new features. Enablement: Please note, you are not here to tell the Analysts how to do their job. Your role is to empower them to do their best work by providing: business context, the problem to solve, prioritization, and success criteria. 🧭 What you bring aboard Data & Product Lover: You are very comfortable with Data (without necessarily being an Analyst yourself) and have a true Product DNA. Hands-on & Tech-savvy: You possess genuine technical curiosity and a desire to dive deeper into AI. You're not afraid to get your hands dirty (prompting, pushing your first MRs, testing things yourself). Business Partner: You have that rare blend of sharp business understanding, product vision, and technical agility. Assertiveness & Leadership: This is a highly exposed role. You will need to challenge stakeholders, know how to say "no", push the Analysts, and confidently stand by your decisions. Pioneer Spirit: This is a brand new role for us. We expect you to build the position from scratch. You are comfortable with trial and error, navigating ambiguity, and you are eager to solve this organizational challenge that is so important to us. 🗺 The hiring journey HR call with Talent Acquisition. In-depth interview with Louis (Head of data) to discuss vision, team, and stakes. Meeting with the team including a practical case to test your approach. Final interview with Camille (VP Product) and Nicolas De Nayer (CTPO) to confirm overall fit. 🙌 Perks & Benefits At Click&Boat, we believe that enjoying your work is essential. This is why Click&Boaters are provided with a stimulating environment and perks ensuring they are happy and efficient, and spend only high-quality time with co-workers. Therefore, we offer: Remote policy: As a developer, you'll have the opportunity to embrace remote work with our remote-friendly policy. To foster team synergy and maintain strong connections, we request that developers come to our Paris houseboat for two consecutive days each month. Competitive Salaries: We believe in recognizing and rewarding the talent and dedication of our team members. Professional Growth : Beyond technical skills, we'll support your growth as a strategic thinker and decision-maker Boating Perks: We offer a free boat license. Benefits: Enjoy lunch vouchers (Swile), private insurance (Alan), and 5 extra days off. 🪝 Ready to come aboard? If this opportunity sounds like your next adventure, hit the “apply to this job” button 🚀 This is a role for someone who wants to build, challenge and make an impact . You don’t need to have followed a traditional Data Product path, what matters is your Product mindset, your curiosity for Data and AI, your ability to navigate ambiguity, and your drive to turn problems into measurable outcomes. Even if you don’t meet every requirement, you might still be a great match for us. We’d love to hear from you! At Click&Boat, every application is reviewed with equal care and attention, regardless of age, gender, origin, disability, religion, sexual orientation, or any other characteristic. We hire for talent, drive, and mindset, the sea is open to all. 🌊