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Location: Remote - United States 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 is building next-generation data center infrastructure across the United States to support high-density, GPU-driven workloads. As a Mechanical Design Engineer, you will support the design, review, and technical development of mechanical systems for data centers across the US portfolio. This role is ideal for a mechanical engineer who can take broad ownership across data center mechanical design, evaluate technical options, and work closely with electrical, construction, commissioning, and operations teams. The right candidate can operate independently, review third-party designs, and help Nebius build scalable, maintainable, high-density data center environments. Location: Remote, US Responsibilities Review mechanical design packages for data center projects, including internal concepts and third-party partner designs. Support mechanical design development for high-density data center environments. Evaluate mechanical design options for air-cooled and liquid-cooled data center architectures. Coordinate closely with electrical design, construction, commissioning, and operations stakeholders. Review designs for maintainability, operational resilience, capacity, and alignment with Nebius standards. Help assess technical tradeoffs, alternative solutions, product options, and design risks. Support design reviews tied to power density, airflow, cooling capacity, and operational readiness. Requirements Data center mechanical design experience. Strong understanding of mechanical systems in mission-critical or data center environments. Experience with air-cooled data center design. Ability to evaluate mechanical capacity, flows, and design assumptions. Ability to review design documents, technical proposals, and partner deliverables. Ability to read and interpret contracts, design requirements, and technical standards. Strong communication skills with internal stakeholders, external design partners, and construction teams. Broad engineering ownership mindset; able to manage the full mechanical design scope rather than only a narrow specialty. Nice to Have Experience with liquid-cooled data center technologies. Experience with hyperscale, neocloud, GPU, or AI infrastructure data centers. Experience at companies such as Lambda, CoreWeave, hyperscalers, colocation providers, or mission-critical engineering firms. Familiarity with high-density rack environments, including 50kW to 200kW per rack. Experience supporting design reviews through commissioning or operational turnover. Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $185,000 — $225,000 USD 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: Amsterdam, Netherlands; Remote - Europe; Remote - United States 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 We are seeking a highly skilled ML/AI Engineer to join our team to lead and support benchmarking of GPU platforms benchmarking of GPU platforms for machine learning and AI workloads. You will play a critical role in evaluating the performance of GPU-based hardware for various deep learning and AI frameworks, enabling data-driven decisions for platform optimisation and next-generation hardware development. Your responsibilities will include: Work closely with hardware, development teams to profile and analyse GPU performance at the system and kernel level. Evaluate and compare GPU performance across different platforms, architectures, and software stacks (e.g.,CUDA, ROCm). Debug and optimise ML workloads to run efficiently on GPU hardware, identifying and resolving performance bottlenecks. Perform acceptance testing acceptance testing for new GPU clusters, ensuring hardware and software meet performance, stability, and compatibility requirements for AI workloads. Perform experiments across diverse GPU system configurations to assess the impact of varying interconnect strategies and system-level optimisations on performance and scalability. Develop tools and dashboards to visualise performance metrics visualise performance metrics, bottlenecks, and trends. Contribute to internal tooling, frameworks, and best practices We expect you to have: A profound understanding of theoretical foundations of machine learning Deep understanding of performance aspects of large neural networks training and inference (data/tensor/context/expert parallelism, offloading, custom kernels, hardware features, attention optimisations, dynamic batching etc.) Deep experience with modern deep learning frameworks (PyTorch, JAX, Megatron-LM, Tensort-LLM) Good understanding of the GPU stack: CUDA,NCCL, drivers, and relevant libraries Familiarity with containerized environments (e.g., Docker, Kubernetes). Strong communication and ability to work independently Ways to stand out from the crowd: Familiarity with modern LLM inference frameworks (vLLM, SGLang, TensorRT) Experience in Python and performance profiling tools (e.g., Nsight, nvprof, perf). Familiarity with cloud ML platforms like AWS, GCP, Azure ML Contributions to open-source ML benchmarking tools 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: North Macedonia; Georgia; Serbia; Croatia; Slovenia; Bosnia and Herzegovina About Huzzle At Huzzle, we connect exceptional talents with top opportunities at leading companies across the UK, US, Canada, Europe, and Australia. Our clients include startups, digital agencies, and tech platforms in industries such as SaaS, MarTech, FinTech, and AI. Unlike an outsourcing agency, we place you directly with a client where you’re hired in-house as a valued member of their team. Role Type: Full-time Engagement: Independent Contractor Job Summary As an AI Engineer, you will be responsible for developing and optimizing AI systems, integrating machine learning models into production environments, and building scalable AI-driven applications. You will contribute to everything from model experimentation to deployment and performance optimization. Key Responsibilities Design, build, and deploy AI and machine learning solutions for real-world business applications. Develop and maintain applications using Large Language Models (LLMs) such as GPT-based systems. Build AI-powered automations, copilots, chatbots, and workflow systems. Fine-tune, evaluate, and optimize machine learning models for performance and scalability. Integrate AI systems with APIs, databases, CRMs, and third-party platforms. Collaborate with product and engineering teams to translate business requirements into AI-driven solutions. Implement prompt engineering strategies and retrieval-augmented generation (RAG) systems. Monitor model performance, reliability, and cost efficiency in production environments. Maintain documentation, testing procedures, and deployment workflows. Stay updated on the latest AI trends, frameworks, and emerging technologies. Requirements 3+ years of experience in AI Engineering, Machine Learning, or Software Engineering. Strong experience with Python and AI/ML frameworks such as TensorFlow, PyTorch, LangChain, or Hugging Face. Experience working with LLMs, OpenAI APIs, Anthropic, Gemini, or similar AI platforms. Familiarity with vector databases, embeddings, and RAG architectures. Experience deploying AI applications using cloud platforms such as AWS, GCP, or Azure. Strong understanding of APIs, backend systems, and scalable application architecture. Experience with Docker, Git, CI/CD pipelines, and modern development practices. Excellent problem-solving and analytical thinking skills. Strong communication skills with the ability to work in remote, cross-functional teams. Bonus: Experience building AI agents, automation systems, or voice AI applications. Benefits 💰 Competitive salary based on experience and location 🌎 Fully remote role with flexible work environment 🚀 Work on cutting-edge AI automation projects with real business impact 📈 Strong growth opportunity into senior AI / systems roles 🧠 Exposure to LLMs, prompt engineering, and scalable AI system design 🤝 Direct collaboration with a fast-moving, high-performance team

Location: South Africa; Pakistan; Philippines; United States; United Kingdom; Canada About Huzzle At Huzzle, we connect exceptional talents with top opportunities at leading companies across the UK, US, Canada, Europe, and Australia. Our clients include startups, digital agencies, and tech platforms in industries such as SaaS, MarTech, FinTech, and AI. Unlike an outsourcing agency, we place you directly with a client where you’re hired in-house as a valued member of their team. Role Type: Full-time Engagement: Independent Contractor Job Summary We are looking for a highly skilled AI Engineer to design, build, and deploy AI-driven applications and machine learning solutions. You’ll work closely with product, engineering, and data teams to create intelligent systems that improve automation, customer engagement, and operational efficiency. This role is ideal for someone passionate about artificial intelligence, large language models (LLMs), machine learning infrastructure, and production-grade AI systems. Key Responsibilities Design, develop, and deploy AI and machine learning models for real-world applications. Build and optimize AI-powered tools using technologies such as OpenAI, Claude, Gemini, or open-source LLMs. Develop scalable APIs and backend services for AI applications. Fine-tune machine learning models and improve model performance. Work with structured and unstructured datasets for training and inference. Implement prompt engineering strategies and retrieval-augmented generation (RAG) pipelines. Integrate AI solutions into web platforms, CRMs, and business systems. Monitor AI model performance, reliability, and scalability in production. Collaborate with cross-functional teams to define AI product requirements and technical roadmaps. Stay updated on emerging AI technologies, frameworks, and best practices. Requirements 3+ years of experience in AI engineering, machine learning, or software engineering. Strong experience with Python and AI/ML frameworks such as TensorFlow, PyTorch, LangChain, or Hugging Face. Experience working with LLMs, generative AI tools, and prompt engineering. Familiarity with vector databases, embeddings, and RAG architectures. Knowledge of cloud platforms such as AWS, GCP, or Azure. Experience building REST APIs and scalable backend systems. Understanding of MLOps, model deployment, and monitoring workflows. Strong problem-solving and analytical thinking skills. Excellent communication and collaboration abilities. Experience working in remote or distributed teams is a plus. Benefits 💰 Competitive salary based on experience and location 🌎 Fully remote role with flexible work environment 🚀 Work on cutting-edge AI automation projects with real business impact 📈 Strong growth opportunity into senior AI / systems roles 🧠 Exposure to LLMs, prompt engineering, and scalable AI system design 🤝 Direct collaboration with a fast-moving, high-performance team

Location: Mexico; Colombia; Brazil; Chile; Argentina; Costa Rica About Huzzle At Huzzle, we connect exceptional talents with top opportunities at leading companies across the UK, US, Canada, Europe, and Australia. Our clients include startups, digital agencies, and tech platforms in industries such as SaaS, MarTech, FinTech, and AI. Unlike an outsourcing agency, we place you directly with a client where you’re hired in-house as a valued member of their team. Role Type: Full-time Engagement: Independent Contractor Job Summary We are looking for a highly skilled AI Engineer to design, develop, and deploy scalable AI and machine learning solutions. You’ll work closely with product, engineering, and leadership teams to build intelligent systems that power automation, analytics, conversational AI, and predictive capabilities. This role is ideal for someone passionate about artificial intelligence, large language models (LLMs), machine learning infrastructure, and solving real-world business challenges with cutting-edge technology. Key Responsibilities Design, develop, and deploy AI/ML models for production environments. Build and optimize AI-powered applications using large language models (LLMs) and generative AI tools. Develop intelligent automation workflows, chatbots, recommendation systems, and predictive analytics solutions. Fine-tune, evaluate, and improve machine learning models for performance and scalability. Collaborate with software engineers and product teams to integrate AI features into existing platforms. Work with structured and unstructured datasets for training and inference pipelines. Implement prompt engineering strategies and retrieval-augmented generation (RAG) systems. Monitor AI systems, troubleshoot issues, and continuously improve model accuracy and efficiency. Stay updated with the latest advancements in AI, machine learning, and automation technologies. Maintain documentation for AI models, systems, and workflows. Requirements 3+ years of experience in AI engineering, machine learning, or related software engineering roles. Strong experience with Python and AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or LangChain. Hands-on experience working with OpenAI APIs, LLMs, generative AI, or NLP systems. Experience deploying machine learning models into production environments. Familiarity with vector databases, embeddings, and RAG architectures. Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform. Knowledge of Docker, Kubernetes, APIs, and CI/CD pipelines is a plus. Strong understanding of data structures, algorithms, and software engineering best practices. Excellent problem-solving and analytical skills. Strong communication skills and ability to work in a remote, collaborative environment. Benefits 💰 Competitive salary based on experience and location 🌎 Fully remote role with flexible work environment 🚀 Work on cutting-edge AI automation projects with real business impact 📈 Strong growth opportunity into senior AI / systems roles 🧠 Exposure to LLMs, prompt engineering, and scalable AI system design 🤝 Direct collaboration with a fast-moving, high-performance team

Location: New York City, NY; San Francisco, CA; Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As part of our growing Data Science and Analytics team, you will play an instrumental role in our company’s mission of building safe and beneficial artificial intelligence by driving data-informed decision making across our organization. You’ve worked in cultures of excellence in the past, and are eager to apply that experience to help shape the cultural norms and best practices of a growing data science team as Anthropic continues to scale. In this unique company, technology, and moment in history, your work will be critical to informing our strategy as we deploy safe, frontier AI at scale to the world. Responsibilities: Deep dive into product and user data to derive actionable insights and size opportunities to improve products, strategy and operations, influencing roadmaps through your insights and recommendations Develop hypotheses, apply rigorous causal inference methods – controlled experiments, synthetic controls –  and analyze the results in order make actionable recommendations Investigate anomalies, conduct root cause analyses, and provide data-driven insights to guide priorities and inform decisions Define core metrics, build measurement frameworks, and maintain core reporting to evaluate success Build statistical models, optimization frameworks, and simulations to automate decision-making and operational processes Present complex technical analyses and recommendations to both technical and non-technical stakeholders Establish foundational data practices and help scale our analytics infrastructure to support rapid iteration and decision-making as our products grow You may be a good fit if you have: 7+ years of experience in data science or analytics roles Deep expertise with Python, SQL, and data visualization tools Expertise with experimental design, causal inference, statistical modeling, and A/B testing frameworks, particularly in high-scale technical environments Highly effective written communication and presentation skills A track record of translating complex data into clear, actionable insights for both technical and business stakeholders A bias for action and ability to thrive in ambiguous, fast-moving environments where you must create clarity and drive forward progress A passion for the company’s mission of building helpful, honest, and harmless AI Some experience with AI/ML products, large language models, or developer tools in the AI/ML ecosystem 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: $285,000 — $380,000 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. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. 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: United Kingdom; Brazil; Ireland; Peru; Costa Rica; Latvia; Argentina; Romania; Cyprus; Portugal; Slovenia; Lithuania; South Africa; Czech Republic; Panama; Estonia; Canada; Hungary; Mexico; Poland; Spain; Bulgaria Workplace: Remote BUILDING THE FUTURE OF OPEN FINANCE Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system. THE TEAM Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients. Kraken is building a dedicated AI Compute and Infrastructure team to power the next generation of model training, inference, evaluation, and experimentation across the exchange. This team sits within engineering leadership and owns the infrastructure layer that lets Kraken run AI workloads with control, speed, reliability, and cost discipline. The team is responsible for GPU and accelerator infrastructure, cluster operations, scheduling, model serving, observability, capacity planning, and cost-efficient compute at scale. This is the backbone that allows Kraken to train, serve, evaluate, and iterate on AI systems in-house where it matters for privacy, latency, reliability, cost, or product differentiation. You will join a small, senior, high-impact team working directly with AI/ML researchers, platform engineers, security teams, and product teams. The mandate is simple: make Kraken's AI ambitions real by building compute infrastructure that is fast, dependable, efficient, and production-grade.   THE OPPORTUNITY - Own and operate GPU and accelerator clusters used for training, inference, evaluation, and experimentation, including drivers, runtimes, kernels, device plugins, node configuration, scheduling primitives, and workload isolation. - Design infrastructure that enables Kraken teams to run models locally on GPUs where it is strategically and economically preferable, reducing unnecessary dependency on external providers and containing compute costs. - Build and improve scheduling, orchestration, placement, quota management, and utilization systems across heterogeneous accelerator environments. - Optimize inference pipelines for latency, throughput, reliability, memory efficiency, and cost using frameworks such as vLLM, Triton Inference Server, TensorRT, or equivalent serving stacks. - Partner with ML engineers and researchers to remove bottlenecks in training, evaluation, batch inference, online inference, deployment, and production debugging workflows. - Build observability for GPU utilization, memory pressure, queue depth, saturation, token throughput, request latency, failed workloads, capacity pressure, and spend. - Drive reliability, incident response, alerting, runbooks, and post-incident improvements for always-on AI compute infrastructure. - Evaluate and integrate new hardware, cloud instance families, specialized accelerators, runtimes, schedulers, and serving frameworks as the AI infrastructure landscape evolves. - Build tooling that makes GPU usage visible, accountable, and easier for internal teams to consume without needing to become infrastructure experts. - Contribute to long-term architecture decisions that balance performance, cost efficiency, scalability, operational simplicity, and production safety.   WHAT YOU BRING - 5+ years of infrastructure engineering experience, with significant time spent on GPU compute, ML infrastructure, distributed systems, high-performance computing, or large-scale production platforms. - Hands-on experience operating GPU clusters or accelerator-backed infrastructure in production or production-like environments, including scheduling, orchestration, utilization monitoring, and cost optimization. - Strong systems engineering fundamentals across Linux, networking, storage, containers, Kubernetes, distributed runtimes, and production debugging. - Experience with ML serving frameworks such as vLLM, Triton Inference Server, TensorRT, TorchServe, KServe, Ray Serve, or equivalent systems. - Proficiency in Python for infrastructure automation, tooling, debugging, integration, and operational workflows. - Practical understanding of performance tradeoffs across batching, concurrency, memory usage, GPU utilization, model size, latency, throughput, availability, and cost. - Track record of optimizing compute costs while maintaining clear performance, reliability, and availability expectations. - Experience building observable systems with useful metrics, logs, traces, dashboards, alerts, and incident workflows. - Comfortable working in high-stakes, always-on environments where uptime, throughput, correctness, and operational discipline are critical. - Clear communicator who can translate infrastructure tradeoffs for researchers, product teams, platform engineers, security stakeholders, and engineering leadership.   NICE TO HAVES - Experience at a frontier AI lab, hyperscaler, high-frequency trading firm, research platform, or high-scale ML organization. - Familiarity with custom silicon or specialized accelerators such as TPUs, AWS Trainium, Gaudi, or similar platforms. - Background in capacity planning, procurement input, reserved capacity strategy, cloud accelerator economics, or GPU fleet cost management. - Experience with distributed training frameworks such as DeepSpeed, Megatron-LM, FSDP, Ray, or equivalent systems. - Experience debugging CUDA, NCCL, kernel, driver, runtime, memory, networking, or low-level performance issues. - Experience with Rust, C++, Go, CUDA, or other systems languages used for performance-critical infrastructure. - Crypto, financial services, trading infrastructure, or security-sensitive production infrastructure experience. Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis. Please note, applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution. We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance. OUR COMMITMENT Payward is powered by people from around the world and we celebrate the diverse talents, backgrounds, contributions, and unique perspectives that everyone brings to the table. We hire based on merit, seeking out people with the right abilities, knowledge, and skills for the job. We encourage you to apply for roles where you don't fully meet the listed requirements, especially if you're passionate or knowledgeable about crypto. We may ask candidates to complete job-related skills or work-style assessments as part of our hiring process. These assessments evaluate competencies relevant to the role and are applied consistently across candidates for similar positions. Results are considered alongside experience and interviews, and are not the sole basis for any employment decision. As an equal opportunity employer, we don't tolerate discrimination or harassment of any kind, whether based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status, or any other protected characteristic as outlined by federal, state, or local laws. Stay connected Follow us on X https://twitter.com/krakenfx Learn on the Kraken Blog https://blog.kraken.com/#:~:text=Enter%20your%20email%20address Connect on LinkedIn https://www.linkedin.com/company/kraken-exchange/ Candidate Privacy Notice https://www.kraken.com/legal/candidate-privacy-notice

Location: Mississauga Workplace: hybrid At PointClickCare our mission is simple: to help providers deliver exceptional care. And that starts with our people. As a leading health tech company that’s founder-led and privately held, we empower our employees to push boundaries, innovate, and shape the future of healthcare.   With the largest long-term and post-acute care dataset and a Marketplace of 400+ integrated partners, our platform serves over 30,000 provider organizations, making a real difference in millions of lives. We also reinvest a significant percentage of our revenue back into research and development, ensuring our employees have the resources to innovate and make a lasting impact. Recognized by Forbes as a top private cloud company and honored as one of Canada’s Most Admired Corporate Cultures, we offer flexibility, growth opportunities, and meaningful work.    At PointClickCare, we empower our people to be the architects of a smarter healthcare future; one that is human-first and accelerated by AI to create meaningful and lasting change. Employees harness AI as a catalyst for creativity, productivity, and thoughtful decision-making. By integrating AI tools into our daily workflows, collaboration is enhanced, outcomes are improved, and every team member has the proficiency to maximize their impact. It all starts with our hiring practices where we uncover AI expertise that complements our mission, and we continue to invest in training and development to nurture innovation throughout the employee journey.   Join us in redefining healthcare — so it doesn’t just survive, it thrives. To learn more about PointClickCare, check out Life at PointClickCare and connect with us on Glassdoor and LinkedIn.     **Travel to Office expectations** For Remote Roles: If this role is remote, there will be in-office events that will require travel to and from the Mississauga and/or Salt Lake City office. These will include, but not limited to, onboarding, team events, semi-annual and annual team meetings.   For Hybrid Roles: If this role is Hybrid, there will be an expectation to reside within commutable distance to the office/location specified in the job listing. This will include, but not limited to, weekly/bi-weekly/monthly events in the office with your specific team. This is a requirement for this role. Job Summary:   The Platform Engineering team is a close-knit group of highly senior engineers focused on building software products for developers — from internal APIs and services to developer tooling, CI/CD frameworks, and Kubernetes-based abstractions. We treat our platform as a product — prioritizing usability, adoption, and developer experience as first-class concerns. You will spend a significant portion of your time writing code — building platform services, APIs, and developer tooling used across engineering teams. This is a rare opportunity to work alongside highly experienced engineers on meaningful software problems where your ideas and expertise truly matter. The team operates in Hybrid, focusing office time for meaningful collaboration. Platform Engineering exists to empower product engineering teams to build, deploy, and operate healthcare software faster, more reliably, and more securely. We solve complex cross-functional problems through well-designed software systems — building APIs, services, and developer-facing applications that reduce cognitive load so development teams can focus on delivering value to clinicians and patients. We may not own every part of the stack, but we engineer the APIs, abstractions, and interfaces that piece it together meaningfully — and we own the design, implementation, testing, and lifecycle of what we build through sound software engineering practices and SRE principles   Key Responsibilities  1 · Design, build, and maintain platform services, APIs, SDKs, and internal tools used across engineering teams 2 · Own platform services end-to-end — from design and implementation through production operation and continuous improvement, writing high-quality, testable code throughout 3 · Design and improve CI/CD workflows and developer automation with a focus on usability and efficiency 4 · Apply distributed systems and backend engineering principles to design scalable, resilient services and APIs 5 · Production ownership of the services and tools you build — including observability, debugging, and incident response   Qualifications & Skills   Minimum Expert-level proficiency in TypeScript/Node.js building production-grade APIs, services, developer tools, and developer-facing UIs (React or similar), with strong automated testing practices (unit, integration, E2E)  Strong software engineering fundamentals — distributed systems, API design, and production backend services, with a proven ability to make pragmatic trade-offs across scalability, reliability, and developer experience Working proficiency with observability (metrics, logging, distributed tracing, SLI/SLO frameworks) and production ownership of services you build — including incident response and post-mortems Strong proficiency with Kubernetes — building software abstractions and APIs, extending capabilities through custom controllers and operators Strong proficiency designing and building CI/CD pipelines optimized for security, compliance, and developer experience Working proficiency with Infrastructure as Code (Terraform, Pulumi), GitOps practices, and cloud-native environments  Strong communication, influence, and documentation skills — able to drive adoption of platform patterns, translate technical complexity for diverse audiences, and produce ADRs, runbooks, and developer- facing documentation Preferred: Experience building internal developer platforms or platform-as-a-product initiatives, including developer portals and self-service tooling  Experience with AI/ML integration: LLM APIs, intelligent automation, AI-assisted developer tooling, and agentic patterns Working proficiency with cloud security practices (IAM, secret handling, encryption), compliance tooling (HIPAA, SOC 2, HITRUST), or cloud cost optimization (FinOps) #LI-AV1 #LI-Hybrid PointClickCare Benefits & Perks:   Benefits starting from Day 1! Retirement Plan Matching Flexible Paid Time Off Wellness Support Programs and Resources Parental & Caregiver Leaves Fertility & Adoption Support Continuous Development Support Program Employee Assistance Program Allyship and Inclusion Communities Employee Recognition … and more!   It is the policy of PointClickCare to ensure equal employment opportunity without discrimination or harassment on the basis of race, religion, national origin, status, age, sex, sexual orientation, gender identity or expression, marital or domestic/civil partnership status, disability, veteran status, genetic information, or any other basis protected by law. PointClickCare welcomes and encourages applications from people with disabilities. Accommodations are available upon request for candidates taking part in all aspects of the selection process. Please contact recruitment@pointclickcare.com should you require any accommodations. As part of our commitment to a streamlined and equitable hiring experience, PointClickCare uses AI tools to assist with candidate screening and assessment.   When you apply for a position, your information is processed and stored with Lever, in accordance with Lever’s Privacy Policy. We use this information to evaluate your candidacy for the posted position. We also store this information, and may use it in relation to future positions to which you apply, or which we believe may be relevant to you given your background. When we have no ongoing legitimate business need to process your information, we will either delete or anonymize it.  If you have any questions about how PointClickCare uses or processes your information, or if you would like to ask to access, correct, or delete your information, please contact PointClickCare’s human resources team: recruitment@pointclickcare.com    PointClickCare is committed to Information Security. By applying to this position, if hired, you commit to following our information security policies and procedures and making every effort to secure confidential and/or sensitive information. At PointClickCare, base salary is one of the many components that make up our total rewards package. The CAD base salary range for this position is $130,000 $162,000 + bonus + benefits. (overtime eligible). Our salary ranges are determined by job and level. The range displayed on each job posting reflects the target for new hire salaries for the position across all US locations. Within the range, individual compensation is determined by job-related skills and knowledge, relevant experience including professional and lived experience, and/or work location. Your recruiter can share more information about our total rewards package during the hiring process.

Location: MinIO Bay Area MinIO is the data and memory foundation for enterprise AI. Built for the speed, scale, and economics that AI and analytics demand, AIStor and MemKV unify every layer of the data stack, from agentic and inference context memory to tables and objects across core, edge, and cloud. Trusted by 77% of the Fortune 100, MinIO is redefining how AI factories, intelligent applications, and autonomous agents secure, persist, and unlock the full value of their data. We are seeking a highly technical, visionary, and customer-centric Field CTO to act as our premier technical ambassador. In this role, you will bridge the gap between MinIO’s engineering innovation and the strategic business needs of our largest, most complex enterprise customers. As a Field CTO, you will engage in peer-to-peer conversations with enterprise C-suites (CIOs, CTOs, CDOs) and lead architectural discussions with principal engineers. A deep, hands-on understanding of modern AI architectures (LLMs, RAG, deep learning pipelines) and modern analytics data systems (Iceberg, Delta Lake, Trino, Spark) is mandatory. You will not only evangelize MinIO’s capabilities but also act as a trusted advisor, helping organizations architect their data infrastructure to support next-generation workloads. This role is Bay Area, CA based What You Will Do Executive Advisory & Evangelism: Act as a trusted technical advisor to C-level executives at Fortune 500 companies. Articulate the strategic value of high-performance object storage in the context of AI readiness and data democratization. Architectural Leadership: Design and validate complex, large-scale architectures for enterprise customers. Guide them in integrating MinIO seamlessly into their AI/ML pipelines and modern data lakehouse ecosystems. Industry Thought Leadership: Represent MinIO at major industry conferences, webinars, and meetups. Write technical whitepapers, reference architectures, and blog posts focusing on the intersection of object storage, generative AI, and modern analytics. Pre-Sales & Post-Sales Strategic Support: Partner closely with enterprise sales and customer success teams on high-stakes deals. Unblock technical objections and provide strategic guidance during Proof of Concepts (PoCs) and large-scale deployments. Product Strategy Feedback Loop: Serve as the "voice of the customer" to MinIO’s product and engineering teams. Distill market trends, customer pain points, and emerging AI/Analytics patterns into actionable product requirements. Required Qualifications 10+ years of experience in technical leadership roles (e.g., Enterprise Architect, Chief Architect, VP of Engineering, or previous Field CTO). Demonstrated experience in a customer-facing or evangelist capacity, successfully presenting to both highly technical audiences and C-level business executives. AI & Machine Learning Architectures: Deep architectural understanding of Generative AI and Large Language Models (LLMs) , including Retrieval-Augmented Generation (RAG) workflows and vector databases (e.g., Milvus, Qdrant, Pinecone). Strong knowledge of AI/ML data pipelines and frameworks (PyTorch, TensorFlow, Ray, Hugging Face). Understanding of hardware bottlenecks in AI, specifically the challenge of feeding data to GPUs (GPU data starvation) and how high-throughput storage solves this. Modern Analytics & Data Systems: Expertise in Open Table Formats (Apache Iceberg, Apache Hudi, Delta Lake) and their integration with object storage. Proficiency with modern query engines and compute frameworks (Trino, Presto, Apache Spark, Flink, StarRocks). Solid grasp of the evolution from traditional data warehouses to modern Data Lakehouse architectures. Infrastructure & Cloud-Native Tech: Strong command of Kubernetes, containerization, and cloud-native orchestration. Deep understanding of the AWS S3 API, RESTful APIs, and distributed systems design. What We Offer Health Care Plan (Medical, Dental & Vision) 401K with 3% Contribution Pre-IPO Stock Options At least 12 Public Holidays Flexible Time Off Equal Opportunity Policy (EEO) MinIO is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law.

Location: Chicago, IL, USA; New York, NY, USA; San Francisco, CA, USA About the role We are looking for a Product Security Manager to lead a team focused on cloud, application, and AI security within our Product Security organization. This role is responsible for securing our cloud infrastructure, protecting applications, and enabling safe adoption of AI across our products and internal systems. You will work closely with engineering, product, and platform teams to ensure that Chime applications and systems are designed and operated securely. While cloud and application security are core pillars of this role, AI security is an increasingly critical area of focus. You will help define how we safely leverage AI technologies while managing risks such as data exposure, misuse, and insecure integrations. This role operates within a flexible POD model, requiring you and your team to move fluidly across security domains based on business priorities. In this role, you can expect to Own the security posture of cloud infrastructure, application systems, and AI-enabled applications across product surfaces Influence platform teams' roadmaps and drive alignment on security priorities without direct authority Partner with engineering and product teams to embed security into system design and development workflows Lead and grow a team of security engineers, setting a high bar for technical execution, ownership, and impact Define and execute strategy for securing cloud-native architectures and applications at scale Establish guardrails and secure design patterns for AI adoption, including safe integration of third-party and internal AI systems Identify and mitigate risks related to AI systems, including prompt injection, data leakage, and misuse of models or APIs Build scalable controls and automation around identity, access, encryption, and application security, including secure SDLC and vulnerability management processes Lead and support response efforts for security incidents related to cloud, application, and AI risks To begin in this role, you have 5+ years of experience in security engineering, with significant experience in cloud and application security 2+ years of experience managing or leading security engineers Strong understanding of cloud platforms (AWS, GCP), Infrastructure as Code (IaC), and modern infrastructure patterns Experience with industry tooling for SAST, DAST, SCA, WAF, and CNAPP Experience designing or securing applications that handle sensitive or regulated data Experience securing or evaluating AI/ML systems, or demonstrated ability to operate effectively in ambiguous and emerging security domains Strong technical judgment and the ability to go deep in architecture and security discussions when needed Experience operating in fast-paced, product-driven environments with shifting priorities Strong communication skills with the ability to translate security risks into business impact #LI-SL1 #LI-Onsite Below is the base salary offered for this role and level of experience. Full-time employees may also be eligible for bonus(es), competitive equity, and benefits. For commissioned roles, the base salary listed in this job description does not include incentive/variable pay. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience. Wage Notice $152,000 — $210,000 USD A little about us At Chime, we believe that everyone can achieve financial progress. We created Chime—a financial technology company, not a bank*—on the premise that core banking services should be helpful, easy, and free. Through our user-friendly tools and intuitive platforms, we empower our members to take control of their finances and work towards their goals. Whether it's starting a savings account, purchasing a first car or home, launching a business, or pursuing higher education, we're proud to have helped millions unlock their financial potential. We're a team of problem solvers, dreamers, and builders with one shared obsession: our members. From day one, Chimers have worked tirelessly to out-hustle and out-execute competitors to bring our mission to life. Their grit and determination inspire us to work harder every day to deliver the very best experience possible. We each bring an owner's mindset to our work, refusing to be outdone and holding ourselves accountable to meet and exceed the highest bars for our teams, our company, and our members. We believe in being bold, dreaming big, and taking risks, while also working together, embracing our diverse perspectives, and giving each other honest feedback. Our culture remains deeply entrepreneurial, encouraging every Chimer to see themselves as stewards of our mission to help everyday Americans unlock their financial progress. We know that to achieve our mission, we must earn and keep people's trust—so we hold ourselves to the highest standards of integrity in everything we do. These aren't just words on a wall—our values are embedded in every aspect of our business, serving as a north star that guides us as we work to help millions achieve their financial potential. Because if we don't—who will? *Chime is a financial technology company, not a bank. Banking services provided by The Bancorp Bank, N.A. or Stride Bank, N.A., Members FDIC. What we offer for our full-time, regular employees 🏢 Our in-office work policy is designed to keep you connected - with four days a week in the office and Fridays from home for those near one of our offices, plus team and company-wide events depending on location. 💻 Benefits that support your work and life, including backup child, elder, and pet care and subsidized commuter benefits for eligible employees. ✨ Comprehensive health, financial, and wellbeing benefits designed to support you at every stage of life. 🏝 Generous vacation policy and company-wide paid days off 🫂 1% of your time off to support local community organizations of your choice 👟 Annual wellness stipend to use towards eligible wellness related expenses 👶 Up to 22 weeks of paid parental leave for birthing parents and 12 weeks of paid parental leave for non-birthing parents 👪 Access to family planning reimbursement 💚 A challenging and fulfilling opportunity to join one of the most experienced teams in FinTech and help millions unlock financial progress We know that great work can’t be done without a diverse team and inclusive environment. That’s why we specifically look for individuals of varying strengths, skills, backgrounds, and ideas to join our team. We believe this gives us a competitive advantage to better serve our members and helps us all grow as Chimers and individuals. Chime is proud to be an Equal Opportunity Employer. We consider qualified applicants without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, gender identity, age, marital or family status, disability, genetic information, veteran status, or any other legally protected basis under provincial, federal, state, and local laws, regulations, or ordinances. We will also consider qualified applicants with criminal histories in a manner consistent with the requirements of state and local laws, including the San Francisco Fair Chance Ordinance, Cook County Ordinance, NYC Fair Chance Act, and the LA City Fair Chance Ordinance, and consistent with Canadian provincial and federal laws. If you have a disability or special need that requires accommodation during any stage of the application process, please contact: accommodations@chime.com . To learn more about how Chime collects and uses your personal information during the application process, please see the Chime Applicant Privacy Notice .

Location: New York, New York, United States; San Francisco, California, United States; Seattle, Washington, United States About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . About the role Turing builds the training and evaluation data powering the world’s leading frontier AI labs. As these labs push the frontier, they bring us their newest models and ask the critical question: how does our model perform against your benchmark datasets, where does it fail, and what data will make it climb the hill? As a Field Engineer, AI Labs , you are the technical face of Turing to frontier research teams. You sit at the exact intersection of client-facing solutions engineering and hands-on AI evaluation. When a frontier lab provides a new model endpoint, you wire it up, run comprehensive evals across Turing’s datasets, synthesize raw performance metrics into actionable intelligence, and present those findings directly to client researchers and technical leaders. This is not a pure research engineering seat and not a traditional forward-deployed IT role. It is a high-velocity, high-agency solutions engineering role for a technically fluent, dependable builder who thrives on talking to customers, running benchmarks across model APIs, and packaging last-mile data deliveries with extreme precision. Why this role matters You will work directly with research scientists and technical executives at top AI labs (such as xAI, Anthropic, OpenAI, and emergent frontier labs). Every eval you run and every benchmark report you write directly influences how frontier labs evaluate model capabilities, uncover failure modes, and procure the pre-training and post-training data required for next-generation intelligence. What you’ll own Evaluation & Benchmarking Connect frontier model APIs and inference endpoints (OpenAI, Anthropic, AWS Bedrock, vLLM, private client gateways) into evaluation harnesses. Execute large-scale benchmark runs across proprietary Turing datasets (reasoning, coding, multimodal, agentic environments, and GDP Val). Analyze eval results, isolate failure cases, detect model behavioral drift, and identify the exact data distributions needed to address capability gaps. Technical Pre-Sales & Client Engagement Serve as the technical counterpart to sales leads on calls with frontier AI labs and enterprise AI teams. Translate complex evaluation metrics into compelling, high-signal reports, client presentations, and technical collateral. Answer technical questionnaires and RFIs from lab researchers regarding data design, task curation, difficulty stratification, and rubrics. Surface lab feedback and emerging benchmark requirements back to internal R&D and dataset design teams. Internal Data Systems & Catalog Stewardship Navigate and operate Turing’s internal data infrastructure (DataOS, Data Hub, and task catalogs) to query, manipulate, and move data pipelines. Slice and curate custom data subsets based on specific difficulty thresholds, benchmark rubrics, or top-performing data points. Audit data provenance and maintain up-to-date specifications across Turing's public catalog and internal dataset inventories. Packaging & Last-Mile Delivery Own last-mile delivery, schema alignment, and packaging so that data delivered matches client technical specifications flawlessly. Work closely with engineering and quality leads to verify that all data deliverables adhere to rigorous validation standards before client handoff. What you bring Required 1 to 5 years of professional experience in data science, AI/ML engineering, solutions engineering, or data analytics with a demonstrated transition into generative AI. Strong hands-on Python proficiency: working with REST/gRPC model APIs, inference endpoints (OpenAI, Anthropic, Bedrock, vLLM), pandas, and SQL. Practical experience with model evaluation, benchmark design, or LLM-as-a-judge pipelines. You know how benchmarks are structured, scored, and debugged. High agency and a scrappy, startup mindset: you have owned projects end-to-end, delivered under tight deadlines, and thrive in fast-paced environments where playbooks are being built in real time. Exceptional written and verbal communication: ability to walk research scientists through technical evaluation metrics and command confidence on commercial calls. Comfort working with command-line tools, internal tooling, and data manipulation workflows. Preferred Prior experience at an early-stage AI startup or high-growth technology company where you wore multiple hats across engineering and customer support. Hands-on experience with evaluation harnesses and agent benchmarks (e.g., Harbor, Terminal-Bench, SWE-bench, Inspect) or agentic workflows. Experience fine-tuning models (e.g., LoRA/QLoRA), working with open-weights models, or conducting multimodal evaluations. Familiarity with data quality control, task verification, or synthetic data generation pipelines. How we work High ownership, low ego: You own the last mile from intake to client handoff. The outcome matters more than who gets credit. Technical fluency with commercial empathy: We don't hide behind jargon. We present clear, honest numbers to lab researchers and act as their trusted technical partners. Rigorous data integrity: Provenance, versioning, and reproducibility come first. What we benchmark is what the client gets. Location & Work Model Location: United States. Strong preference for candidates based in the San Francisco Bay Area (for regular interaction with local AI labs and team hubs); remote within the US considered for exceptional candidates. Employment Type: Full-time standard employee. Compensation & Benefits The annual base salary range for this full-time position is $175,000 – $232,000 USD . In addition to base salary, this role is eligible for equity, performance incentives, and comprehensive company benefits. Individual compensation is determined by job-related skills, experience, qualifications, and relevant business considerations. Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .

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 team The Hardware Automation team builds the internal platforms and tooling that power how Nebius operates its data center infrastructure at scale. Our mission is to eliminate manual effort, reduce human error, and give every team in the Hardware Infrastructure department real-time visibility and control over the systems they own. We operate as a product engineering team embedded within hardware infrastructure — meaning we don't just write requirements and hand them off. We own the full stack: from requirements gathering with data center operations and hardware engineering, through design and build, all the way to rollout and ongoing reliability. The role We are looking for a Technical Project Manager to drive the programs that turn manual hardware operations into automated, scalable workflows. You will own the delivery of automation initiatives across the full hardware lifecycle — from automated server provisioning and firmware update orchestration through intelligent fault detection, self-healing remediation, and data-centre-scale operational tooling — coordinating engineers, operations teams, and infrastructure stakeholders to ship systems that meaningfully reduce human toil at fleet scale. The responsibilities Initiating and driving cross-team projects with clear goals and timelines Aligning conflicting priorities and stakeholder needs, including C-level Facilitating technical and procedural decision-making Establishing and scaling repeatable processes Collaborating closely with engineering, infrastructure, and business teams The profile : Engineering Manager wanting to move from vertical to horizontal impact, from team to org level, designing and carrying a unified roadmap and strategy across multiple engineering teams. In this case, we are talking about 6 teams for more than 30 engineers. Technical Project/Program Manager with an Engineering Management background. Strong operational risk awareness: track record of structuring staged rollouts, defining rollback criteria, and insisting on safety gates before automation runs unsupervised on production infrastructure. Experience coordinating across engineering and operations stakeholders with different risk tolerances — bridging teams that move fast and teams that prioritise stability without letting that tension stall delivery. Preferred / Nice to Have Experience with specific hardware automation domains: zero-touch provisioning, firmware orchestration, automated hardware testing frameworks, or self-healing remediation systems. Familiarity with infrastructure-as-code and configuration management tooling (Terraform, Ansible, Kubernetes) from a delivery and rollout management perspective. Exposure to data centre operations — rack and stack, cabling, power and cooling management — sufficient to understand what automation means to a technician on the floor. Experience with site reliability engineering (SRE) practices: toil measurement, error budgets, and the discipline of treating operational automation as a first-class engineering investment. Knowledge of observability platforms (Prometheus, Grafana, Datadog, or equivalent) used to monitor automation system health and measure operational outcomes. 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.

Workplace: remote Location : Quantori is an international team: we have colleagues who work not only from office but also remotely from all over the world. Responsibilities : Design, build, and maintain scalable data pipelines for acquiring, integrating, and managing data from diverse data generation sources and systems (e.g., lab systems, MES, clinical supply, quality systems, external partners). Create and optimize data flows for structured and unstructured data using Python (PySpark), R, SQL, Databricks, Snowflake, and other modern engineering tools. Develop and maintain specific data repositories, implementing enterprise‑level data models, and creating new models as needed. Enable AI/ML readiness by ensuring data is well‑structured, versioned, traceable, and semantically aligned with enterprise data standards. What we expect : Bachelor’s degree in Engineering, Data Science, Life Sciences, Computer Science, or related field; advanced degree preferred. 6+ years of experience in data engineering, including data modeling and database design, preferably in a scientific, manufacturing, or healthcare environment. Proficiency with Python, R, SQL, and cloud-based architectures (AWS services, Snowflake, Databricks, Redshift) along with Spark and dbt. Familiarity with Databricks AI/BI or Tableau or other BI tools. Expertise in ETL and DWH. Experience with NoSQL and graph databases. English language proficiency of B2+ Strong analytical, problem‑solving, and stakeholder‑management skills, with the ability to translate discussions into actionable requirements. Ability to drive multiple exciting projects simultaneously with strong organizational skills and adaptability. Nice to have : Experience with regulated or standards‑driven data environments, such as CDISC, HL7, FHIR, OMOP, DICOM, or manufacturing/quality data standards. Familiarity with high‑dimensional data (e.g., imaging, sensor data, etc). Experience with principles connecting to or feeding MLOps and model deployment workflows. Knowledge of manufacturing systems (MES), laboratory information systems, or industrial data systems. Exposure to knowledge graph or ontology‑driven architectures. We offer : Competitive compensation Remote work Flexible working hours Continuous education, mentoring, and professional development programs A team with an excellent tech expertise

Location: New York WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW As a Staff Software Engineer on the Core Infrastructure team at Harvey, you'll play a critical role in designing and building new infrastructure systems while equally scaling and strengthening our existing infrastructure. Our infrastructure is the foundation that powers every user interaction with Harvey — processing billions of prompt tokens and millions of daily requests across our global legal AI platform. You'll work in an environment balanced between innovation — building new systems — and operational excellence, ensuring that Harvey remains resilient and efficient as it scales products, regions, customers, and usage. Your contributions will directly impact the reliability, scalability, and security of our platform as we serve the world's leading law firms and professional service providers. This role is based in New York City, New York. We use an in-person work model and offer relocation assistance to new employees. WHAT YOU’LL DO - Design and build scalable, fault-tolerant infrastructure systems that power Harvey's AI platform across multiple cloud regions - Own and evolve our multi-cloud infrastructure (Azure, GCP), including Kubernetes orchestration, networking, and container management - Lead technical initiatives around observability, incident response, and operational excellence — building systems that enable rapid detection and resolution of issues - Architect and optimize our distributed systems for reliability, including load balancing, quota management, and failover mechanisms - Partner with Product Engineering and Security teams to ensure our infrastructure is an accelerant, not a constraint - Drive infrastructure-as-code practices using tools like Terraform and Pulumi to enable reproducible, auditable deployments - Mentor engineers and raise the technical bar across the organization through code reviews, design reviews, and technical leadership REPRESENTATIVE PROJECTS - Design and implement a next-generation model proxy architecture that routes millions of daily inference requests while maintaining model API compatibility and enabling seamless model integration - Build distributed rate limiting and quota management systems using Redis-backed algorithms to handle bursty traffic patterns without degrading user experience - Architect multi-region deployment strategies that meet strict data residency requirements for global enterprise customers - Develop comprehensive observability infrastructure with granular SLA monitoring, burn rate alerts, and detailed token attribution for cost tracking - Lead the evolution of our CI/CD pipelines to improve developer velocity while maintaining production stability WHAT YOU HAVE - 10+ years of experience in Infrastructure Engineering or Platform Engineering in a production environment - Long track record building and scaling complex, large-scale distributed systems - Deep proficiency with cloud infrastructure platforms (Azure preferred; GCP or AWS experience transfers well) - Strong fluency in Infrastructure as Code (IaC) tools — Terraform, Pulumi, or CloudFormation - Solid understanding of Kubernetes, container orchestration, networking, and cloud security at scale - Experience with observability tools (Datadog, Sentry) and incident response practices (PagerDuty, Incident.io http://Incident.io) - Strong programming skills in Python, Go, or similar languages - Excellent problem-solving skills, a "spidey sense" of where things could go wrong, and a commitment to operational excellence NICE TO HAVE - Experience building infrastructure for AI/ML workloads or high-throughput inference systems - Background with distributed rate limiting, load balancing, or quota management systems - Experience operating multi-tenant platforms with strict security and compliance requirements - Track record of leading complex cross-functional projects and delivering measurable impact COMPENSATION RANGE $201,000 - $264,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-AN2 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: San Francisco WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW As a Staff Software Engineer on the Core Infrastructure team at Harvey, you'll play a critical role in designing and building new infrastructure systems while equally scaling and strengthening our existing infrastructure. Our infrastructure is the foundation that powers every user interaction with Harvey — processing billions of prompt tokens and millions of daily requests across our global legal AI platform. You'll work in an environment balanced between innovation — building new systems — and operational excellence, ensuring that Harvey remains resilient and efficient as it scales products, regions, customers, and usage. Your contributions will directly impact the reliability, scalability, and security of our platform as we serve the world's leading law firms and professional service providers. This role is based in San Francisco, CA. We use an in-person work model and offer relocation assistance to new employees. WHAT YOU’LL DO - Design and build scalable, fault-tolerant infrastructure systems that power Harvey's AI platform across multiple cloud regions - Own and evolve our multi-cloud infrastructure (Azure, GCP), including Kubernetes orchestration, networking, and container management - Lead technical initiatives around observability, incident response, and operational excellence — building systems that enable rapid detection and resolution of issues - Architect and optimize our distributed systems for reliability, including load balancing, quota management, and failover mechanisms - Partner with Product Engineering and Security teams to ensure our infrastructure is an accelerant, not a constraint - Drive infrastructure-as-code practices using tools like Terraform and Pulumi to enable reproducible, auditable deployments - Mentor engineers and raise the technical bar across the organization through code reviews, design reviews, and technical leadership REPRESENTATIVE PROJECTS - Design and implement a next-generation model proxy architecture that routes millions of daily inference requests while maintaining model API compatibility and enabling seamless model integration - Build distributed rate limiting and quota management systems using Redis-backed algorithms to handle bursty traffic patterns without degrading user experience - Architect multi-region deployment strategies that meet strict data residency requirements for global enterprise customers - Develop comprehensive observability infrastructure with granular SLA monitoring, burn rate alerts, and detailed token attribution for cost tracking - Lead the evolution of our CI/CD pipelines to improve developer velocity while maintaining production stability WHAT YOU HAVE - 10+ years of experience in Infrastructure Engineering or Platform Engineering in a production environment - Long track record building and scaling complex, large-scale distributed systems - Deep proficiency with cloud infrastructure platforms (Azure preferred; GCP or AWS experience transfers well) - Strong fluency in Infrastructure as Code (IaC) tools — Terraform, Pulumi, or CloudFormation - Solid understanding of Kubernetes, container orchestration, networking, and cloud security at scale - Experience with observability tools (Datadog, Sentry) and incident response practices (PagerDuty, Incident.io http://Incident.io) - Strong programming skills in Python, Go, or similar languages - Excellent problem-solving skills, a "spidey sense" of where things could go wrong, and a commitment to operational excellence NICE TO HAVE - Experience building infrastructure for AI/ML workloads or high-throughput inference systems - Background with distributed rate limiting, load balancing, or quota management systems - Experience operating multi-tenant platforms with strict security and compliance requirements - Track record of leading complex cross-functional projects and delivering measurable impact COMPENSATION RANGE $236,000 - $290,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE. https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74 #LI-AN2 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: Athens Workplace: Hybrid About the company: KIEFER is building Greece's integrated AI ecosystem. From renewable energy infrastructure and AI systems to robotics and enterprise applications, we connect the technologies that power Europe's intelligent future. Founded in 2014, KIEFER has delivered 600MW+ of energy projects and is now developing sovereign AI infrastructure, enterprise AI products and physical AI systems for Greece and Southeast Europe. About the role: We are looking for an ML Team Lead to join Kiefer Tech and lead our ML Engineering team. In this role, you will lead the development and continuous improvement of Sophea AI, our Greek-focused Large Language Model. You will work across LLM training, fine-tuning, ASR, inference optimization, model validation, and production-grade ML systems. Beyond the model itself, you will help shape the ML Engineering function at Kiefer Tech: define clear ownership, strengthen team workflows, raise technical standards, and ensure complex ML initiatives are delivered with accountability, clarity, and strong execution. What you will do: - Lead the ML Engineering team working on Sophea AI and set a clear technical direction for its development - Own delivery across core ML workstreams, including LLM training, fine-tuning, ASR, inference optimization, model validation, and production-grade ML systems - Build a stronger operating model for the ML function: improve workflows, clarify ownership, strengthen decision-making, and remove execution blockers - Turn the ML roadmap into clear priorities, accountable workstreams, and measurable outcomes aligned with product and business goals - Mentor ML engineers, develop team capabilities, and bring stronger engineering practices, tools, and research-driven approaches into the team’s daily work What you will need: - Proven experience leading ML Engineering teams or complex ML workstreams in high-performance, fast-moving environments - Deep technical background in LLMs, ASR, and production-grade ML systems, including pre-training, training from scratch, fine-tuning, reinforcement learning, model validation, and performance improvement - Strong understanding of inference optimization, model quantization, and serving frameworks such as vLLM, SGLang, NVIDIA Triton, NVIDIA Dynamo, or similar tools - Ability to bring structure into ambiguous environments: clarify ownership, improve workflows, and drive execution across several ML workstreams - High autonomy, strong communication skills, and the ability to follow current AI research and translate relevant tools, methods, and trends into team practice Nice to have: - Experience with MLOps infrastructure, model serving pipelines, GPU workload management, and experiment tracking - Contributions to open-source ML projects or published research in AI/ML - Familiarity with real-time inference systems or event-driven ML architectures What is there for you: - Compensation: highly competitive package aligned with AI talent benchmarks across Europe and the US - Ownership: high-impact leadership role with real influence on the ML Engineering function at Kiefer Tech - Work format: remote work option, with relocation support available for candidates open to working from our Athens office - AI-native environment: real challenges across LLMs, ASR, GPU workloads, and advanced AI product development - NVIDIA ecosystem: access to related conferences, certifications, internal knowledge sharing, and advanced AI infrastructure through Kiefer’s strategic collaboration - Culture: engineering-first, high autonomy, low bureaucracy, and space to shape meaningful AI products

Location: Dallas, USA; New York, USA; Orlando, USA As a Principal AI Solutions Architect within DataArt’s Healthcare & Life Sciences practice, you will bridge the gap between advanced AI tools and real-world business impact. You’ll operate at the intersection of hands-on prototyping, cross-functional collaboration, and stakeholder education. Your focus: drive measurable client outcomes through AI-enabled efficiencies and new digital experiences. You will shape how AI is adopted across the healthcare and life sciences industries — from intelligent copilots and personalization engines to developer acceleration and operational optimization — by prototyping real solutions, articulating business value, and driving adoption from C-suite to delivery teams. You will work closely with client account teams (50+ active clients) and DataArt’s AI + Data Lab to identify high-value use cases, prototype rapidly, validate with stakeholders, and showcase scalable solutions that clients can adopt. The estimated OTE for this position is 220,000 to 260,000 USD per year. Lead ideation and scoping of AI/ML proof of concepts (PoCs) in coordination with client account leaders Rapidly prototype and iterate AI features (chatbots, NLP services, recommender systems, etc.) Direct internal DataArt teams to execute PoCs with clarity, velocity, and alignment Own the "show-and-tell" lifecycle: internal demo → stakeholder validation → external client presentation Develop GenAI applications based on RAG variations, Agentic, hybrid models. Leverage Agentic SDLC and advocate for adoption. Develop with hyperscaler AI tools (Azure, GCP, AWS), LLM APIs, vector search, GenAI, Agentic SDKs and Agentic Runtimes (Agent Core, Agent Engine, etc.) Define reusable AI building blocks and Healthcare-specific agent scaffolds that feed into the Connect AI ecosystem Build working solutions integrated with cloud data platforms like Snowflake and Databricks Embed AI into client-facing products or operations (e.g., personalization, crew ops, service automation) Shape internal best practices for GenAI use in SDLC and Healthcare product lifecycle — from opportunity discovery to ops telemetry Create AI demo assets: notebooks, dashboards, blog content, or walkthrough videos Run workshops and working sessions with client teams and business leaders Act as a translator and evangelist, helping stakeholders understand responsible AI adoption and potential ROI Hands-on experience with AI services from at least one major cloud provider (Azure, AWS, GCP) Demonstrated ability to operate at Director level or above in client engagements, influencing both technical and business stakeholders Experience with designing and implementing RAG based applications Strong background with Snowflake and/or Databricks for data engineering and AI pipelines Proven success leading client-facing PoCs or MVPs from inception to delivery Prior exposure to Healthcare or Life Sciences sector Strong understanding of Healthcare and/or Life Sciences workflows, regulatory frameworks (HIPAA, CMS-0057-F, GDPR, ISO 13485, ISO 42001, etc.), and industry challenges. Ability to translate fuzzy business goals into AI-enabled outcomes, structuring initiatives from ambiguous needs into scoped, testable, and scalable components Fluency in evaluating tradeoffs between open-source LLMs and commercial APIs, and designing cost-efficient, scalable architectures Strong communicator comfortable with both technical teams and business stakeholders Ability to travel to client site (US, EU) Exposure to healthcare data standards (FHIR, HL7) and integrations (eRx, Clearinghouses, TEFCA). Knowledge of GenAI fine-tuning, vector DBs, or orchestration frameworks (LangChain, Semantic Kernel, etc.) Experience developing internal enablement materials or technical content Awareness of responsible AI practices (e.g., model fairness, explainability, usage boundaries)

Location: London, UK As a Principal AI Solutions Architect within DataArt’s Healthcare & Life Sciences practice, you will bridge the gap between advanced AI tools and real-world business impact. You’ll operate at the intersection of hands-on prototyping, cross-functional collaboration, and stakeholder education. Your focus: drive measurable client outcomes through AI-enabled efficiencies and new digital experiences. You will shape how AI is adopted across the healthcare and life sciences industries — from intelligent copilots and personalization engines to developer acceleration and operational optimization — by prototyping real solutions, articulating business value, and driving adoption from C-suite to delivery teams. You will work closely with client account teams (50+ active clients) and DataArt’s AI + Data Lab to identify high-value use cases, prototype rapidly, validate with stakeholders, and showcase scalable solutions that clients can adopt. Lead ideation and scoping of AI/ML proof of concepts (PoCs) in coordination with client account leaders Rapidly prototype and iterate AI features (chatbots, NLP services, recommender systems, etc.) Direct internal DataArt teams to execute PoCs with clarity, velocity, and alignment Own the "show-and-tell" lifecycle: internal demo → stakeholder validation → external client presentation Develop GenAI applications based on RAG variations, Agentic, hybrid models. Leverage Agentic SDLC and advocate for adoption. Develop with hyperscaler AI tools (Azure, GCP, AWS), LLM APIs, vector search, GenAI, Agentic SDKs and Agentic Runtimes (Agent Core, Agent Engine, etc.) Define reusable AI building blocks and Healthcare-specific agent scaffolds that feed into the Connect AI ecosystem Build working solutions integrated with cloud data platforms like Snowflake and Databricks Embed AI into client-facing products or operations (e.g., personalization, crew ops, service automation) Shape internal best practices for GenAI use in SDLC and Healthcare product lifecycle — from opportunity discovery to ops telemetry Create AI demo assets: notebooks, dashboards, blog content, or walkthrough videos Run workshops and working sessions with client teams and business leaders Act as a translator and evangelist, helping stakeholders understand responsible AI adoption and potential ROI Hands-on experience with AI services from at least one major cloud provider (Azure, AWS, GCP) Demonstrated ability to operate at Director level or above in client engagements, influencing both technical and business stakeholders Experience with designing and implementing RAG based applications Strong background with Snowflake and/or Databricks for data engineering and AI pipelines Proven success leading client-facing PoCs or MVPs from inception to delivery Prior exposure to Healthcare or Life Sciences sector Strong understanding of Healthcare and/or Life Sciences workflows, regulatory frameworks (HIPAA, CMS-0057-F, GDPR, ISO 13485, ISO 42001, etc.), and industry challenges. Ability to translate fuzzy business goals into AI-enabled outcomes, structuring initiatives from ambiguous needs into scoped, testable, and scalable components Fluency in evaluating tradeoffs between open-source LLMs and commercial APIs, and designing cost-efficient, scalable architectures Strong communicator comfortable with both technical teams and business stakeholders Ability to travel to client site (US, EU) Exposure to healthcare data standards (FHIR, HL7) and integrations (eRx, Clearinghouses, TEFCA). Knowledge of GenAI fine-tuning, vector DBs, or orchestration frameworks (LangChain, Semantic Kernel, etc.) Experience developing internal enablement materials or technical content Awareness of responsible AI practices