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We are seeking an experienced Equity Derivatives and QIS Structurer to join our team in New York. As a key member of our investment banking, markets, and research department, you will be responsible for leveraging your knowledge and experience in the US Equity Derivatives and Quantitative Investment Strategies landscape to identify and evaluate commercial opportunities for our equity derivatives business.

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Your primary responsibilities will include working with our salesforce to design an innovative product roadmap, as well as the actual products, to meet new or future client demand. You will also be responsible for helping to design compelling marketing campaigns around these products to maximise engagement with our client base.

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In addition, you will oversee the full spectrum of product launch, including onboarding into our systems, to ensure risks generated by these products are well controlled by our organisation. You will liaise with our relevant trading teams to incorporate feedback, leverage our strengths, and plan out necessary future capabilities.

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Collaboration with our internal tech teams will be essential to streamlining the trade volumes resulting from our equity derivatives and QIS businesses.

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This is a full-time position, Monday to Friday, 40 hours per week, with a salary range of $250,000.00 to $260,000.00 per year.

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XML job scraping automation by YubHub

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About the role\\nEvery request that hits Claude , from claude.ai, the API, our cloud partners, or internal research , passes through a routing decision. Not a generic load balancer round-robin, but a decision that accounts for what's already cached where, which accelerator the request runs best on, and what else is in flight across the fleet.\\n\\nGet it right and you extract meaningfully more throughput from the same hardware. Get it wrong and you burn capacity, miss latency SLOs, or shed load that shouldn't have been shed.\\n\\nThe Inference Routing team owns this layer. We build the cluster-level routing and coordination plane for Anthropic's inference fleet , the system that sits between the API surface and the inference engines themselves, making fleet-wide efficiency decisions in real time.\\n\\nAs Anthropic moves from "many independent inference replicas" toward "a single warehouse-scale computer running a coordinated program," Dystro is the coordination layer. 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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.\\nAnnual Salary: $405,000-$485,000 USD

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XML job scraping automation by YubHub

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As a Technical Program Manager on the Compute team, you will help drive the planning, coordination, and execution of programs that keep Anthropic's compute infrastructure running efficiently at scale.

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Our compute fleet is the foundation on which every model training run, evaluation, and inference workload depends. You'll join a small, high-impact TPM team and take ownership of critical workstreams across the compute lifecycle, from how supply is procured and brought online, to how capacity is allocated and utilized across teams.

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You'll partner with Infrastructure, Systems, Research, Finance, and Capacity Engineering to shape the processes, tooling, and coordination mechanisms that allow Anthropic to move fast while managing an increasingly complex compute environment.

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Responsibilities:

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You may be a good fit if you:

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Strong candidates may also have:

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XML job scraping automation by YubHub

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As a Technical Program Manager on the Compute team, you will help drive the planning, coordination, and execution of programs that keep Anthropic's compute infrastructure running efficiently at scale.

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Our compute fleet is the foundation on which every model training run, evaluation, and inference workload depends. You'll join a small, high-impact TPM team and take ownership of critical workstreams across the compute lifecycle, from how supply is procured and brought online, to how capacity is allocated and utilized across teams.

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You'll partner with Infrastructure, Systems, Research, Finance, and Capacity Engineering to shape the processes, tooling, and coordination mechanisms that allow Anthropic to move fast while managing an increasingly complex compute environment.

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Responsibilities:

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Strong candidates may also have:

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XML job scraping automation by YubHub

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We're looking for a Senior Data Engineer to join our Payments team. As a critical part of our operations, you'll handle data related to compliance with Tax, Payments, and Legal regulations. You'll design, build, and maintain robust and efficient data pipelines that collect, process, and store data from various sources, including user interactions, listing details, and external data feeds.

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Your work will involve developing data models that enable the efficient analysis and manipulation of data for merchandising optimization, ensuring data quality, consistency, and accuracy. You'll also develop high-quality data assets for product use-cases by partnering with Product, AI/ML, and Data Science teams.

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As a Senior Data Engineer, you'll contribute to creating standards and best practices for Airbnb's Data Engineering and shape the tools, processes, and standards used by the broader data community. You'll collaborate with cross-functional teams to define data requirements and deliver data solutions that drive merchandising and sales improvements.

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To succeed in this role, you'll need 6+ years of relevant industry experience, a BE/B.Tech in Computer Science or a relevant technical degree, and hands-on experience in DSA coding, data structure, and algorithm. You'll also need extensive experience designing, building, and operating robust distributed data platforms and handling data at the petabyte scale.

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XML job scraping automation by YubHub

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You will be responsible for building and evolving the core backend systems and shared infrastructure that power our platform.

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A significant portion of backend logic is shared across services, and this role will help define, maintain, and scale that foundation.

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You will own and improve internal schema and code generation tooling that ensures consistency and correctness across services.

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You will work on and extend our custom job scheduler, improving reliability, observability, and execution guarantees for distributed workloads.

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You will contribute to safely execute large-scale concurrent and distributed operations.

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You will play a key role in defining and maintaining API standards across teams, ensuring performance, backward compatibility, and clear evolution strategies.

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You will collaborate closely with Product and various Engineering teams to design systems that are reliable, scalable, and maintainable over time.

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The Core Systems team is responsible for the foundational backend infrastructure that powers Weights & Biases within CoreWeave.

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Much of the platform's critical logic is shared across services, and this role sits at the center of that foundation.

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You will work on the systems that other engineers build upon , from execution frameworks and schedulers to schema tooling and API standards.

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This is a high-leverage role focused on durability, scalability, and long-term maintainability.

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The systems you design and evolve will directly impact reliability, developer velocity, and the ability of the platform to scale with growing workloads.

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You'll collaborate across teams to ensure that shared backend abstractions remain clean, performant, and consistent as we continue to expand our adoption of technologies like GraphQL and gRPC.

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If you enjoy owning deep technical infrastructure, shaping engineering standards, and building systems that other engineers depend on every day, this role offers meaningful scope and impact.

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You will be surrounded by some of the best talent in the industry, who will want to learn from you, too.

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Come join us!

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XML job scraping automation by YubHub

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About the role\\nEvery request that hits Claude , from claude.ai, the API, our cloud partners, or internal research , passes through a routing decision. Not a generic load balancer round-robin, but a decision that accounts for what's already cached where, which accelerator the request runs best on, and what else is in flight across the fleet.\\n\\nGet it right and you extract meaningfully more throughput from the same hardware. Get it wrong and you burn capacity, miss latency SLOs, or shed load that shouldn't have been shed.\\n\\nThe Inference Routing team owns this layer. We build the cluster-level routing and coordination plane for Anthropic's inference fleet , the system that sits between the API surface and the inference engines themselves, making fleet-wide efficiency decisions in real time.\\n\\nAs Anthropic moves from "many independent inference replicas" toward "a single warehouse-scale computer running a coordinated program," Dystro is the coordination layer. This is a deeply technical team.\\n\\nThe engineers here design custom load-balancing algorithms, build quantitative models of system performance, debug latency spikes that cross kernel, network, and framework boundaries, and reason carefully about cache placement across thousands of accelerators.\\n\\nThey work shoulder-to-shoulder with teams that write kernels and ML framework internals.\\n\\nThe EM for this team doesn't need to write kernels , but they do need the systems depth to make architectural calls, evaluate deeply technical candidates, and spot when a proposed optimization will have second-order effects on the fleet.\\n\\nYou'll inherit a strong team of distributed-systems engineers, and you'll be accountable for two things that pull in different directions: shipping system-level performance improvements that measurably increase fleet throughput and efficiency, and running the team operationally so that deploys are safe, incidents are rare, and the teams who depend on Dystro can plan around you with confidence.\\n\\nThe job is holding both.\\n\\n## Representative work:\\nThings the Inference Routing EM actually spends time on:\\n- Deciding whether a proposed routing algorithm change is worth the deploy risk, given the modeled throughput gain and the blast radius if it regresses\\n- Sequencing a quarter where KV-cache offload, a new coordination protocol, and two model launches all compete for the same engineers\\n- Working through a persistent tail-latency regression with the team , walking down from fleet-level metrics to per-replica behavior to a root cause in the networking stack\\n- Building the case (with numbers) to peer teams for why a cross-team protocol change unlocks the next efficiency win\\n- Running the post-incident review after a cache-eviction bug caused a capacity event, and turning it into process changes that stick\\n- Interviewing a candidate who has built schedulers at supercomputing scale, and deciding whether they'd be additive to a team that already goes deep\\n\\n## What you'll do:\\nDrive system-level performance\\n- Own the technical roadmap for cluster-level inference efficiency , routing decisions, cache placement and eviction, cross-replica coordination, and the protocols that keep routing and inference engines in sync\\n- Partner with the inference engine, kernels, and performance teams to identify fleet-level throughput and latency wins, then turn those into shipped improvements with measurable results\\n- Build the team's habit of quantitative performance modeling: claim a win only when you can measure it, and know before you ship what the expected effect is\\n\\nDeliver reliably and operate cleanly\\n- Set technical strategy for how routing evolves across heterogeneous hardware (GPUs, TPUs, Trainium) and across all our serving surfaces\\n- Run the team's operational backbone , on-call rotation, incident response, postmortem review, deploy safety , so the team can ship aggressively without the system becoming fragile\\n- Create clarity at a seam: Inference Routing sits between the API surface, the inference engines, and the cloud deployment teams. You'll make sure commitments are realistic, dependencies are understood, and nobody is surprised\\n\\nBuild and grow the team\\n- Develop and retain a strong existing team, and hire against the bar described above: people who can go to the OS and framework level when the problem demands it, and who care about production reliability\\n- Coach engineers through a roadmap where priorities shift with model launches, new hardware, and scaling demands. We pair a lot here , you'll help make that collaboration pattern productive\\n- Pick up slack when it matters. This is a small team in a critical path; sometimes the EM is the one unblocking a stuck deploy or synthesizing a design debate\\n\\n## You may be a good fit if you:\\n- Have 5+ years of engineering management experience, ideally with at least part of that leading teams on critical-path production infrastructure at scale\\n- Have a deep systems background , load balancing, scheduling, cache-coherent distributed state, high-performance networking, or similar. You need enough depth to make architectural calls about routing and efficiency, and to evaluate candidates who go to the kernel and framework level\\n- Have shipped performance improvements in large-scale systems and can explain, with numbers, what the impact was\\n- Have run production infrastructure with real operational stakes: on-call, incident response, capacity events, deploy discipline\\n- Are results-oriented with a bias toward impact, and comfortable working in a space where throughput, latency, stability, and feature velocity all pull in different directions\\n- Build strong relationships across team boundaries , this is a seam role, and much of the job is making sure other teams can rely on yours\\n- Are curious about machine learning systems. You don't need an ML research background, but you should want to learn how transformer inference actually works and how that shapes the systems problems\\n\\nStrong candidates may also have:\\n- Experience with LLM inference serving , KV caching, continuous batching, request scheduling, prefill/decode disaggregation\\n- Background in cluster schedulers, load balancers, service meshes, or coordination planes at scale\\n- Familiarity with heterogeneous accelerator fleets (GPU/TPU/Trainium) and how hardware differences affect workload placement\\n- Experience with GPU/accelerator programming, ML framework internals, or OS-level performance debugging , enough to follow and evaluate the technical work, not necessarily to do it daily\\n- Led teams at supercomputing or hyperscaler infrastructure scale\\n- Led teams through rapid-growth periods where hiring and onboarding competed with roadmap delivery\\n\\nThe 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.\\nAnnual Salary: $405,000-$485,000 USD

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XML job scraping automation by YubHub

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About Mistral AI

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Mistral AI is an AI technology company that develops high-performance, optimized, open-source and cutting-edge models, products and solutions.

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Role Summary – Software Engineering track

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As a Research Engineer on the software side, you will design and harden the codebase, tools and distributed services that let our scientists train and ship frontier-scale models. You do not need prior ML experience; what matters is writing clean, reliable code that scales. You will join our Platform REs team to build and maintain shared dev-tools, evaluation & data pipelines, training framework, cluster tooling and CI/CD.

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Responsibilities

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• Accelerate researchers by owning the complex parts of large-scale pipelines and delivering robust internal tooling.\n• Interface research with product: expose clean APIs, automate model pushes, surface live metrics.\n• Write efficient, well-tested Python and systems code; enforce code review, CI, and observability.\n• Design and optimise distributed services (Kubernetes / SLURM, thousands-of-GPU jobs).\n• Prototype utilities (CLI, dashboards) and carry them through to stable, shared libraries.

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About the Research Engineering team

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Based in Paris and London, our REs move fluidly along the research ↔ production spectrum. Engineers can rotate between Platform and Embedded tracks as their interests evolve.

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About you

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• Master’s in Computer Science (or equivalent experience).\n• 4 + years building and operating large-scale or distributed systems.\n• Strong software-design instincts: modular code, tests, CI/CD, observability.\n• Fluency in Python plus one systems language (C++, Rust, Go or Java).\n• Hands-on with container orchestration and schedulers (Kubernetes / K8s, SLURM, or similar).\n• Comfortable profiling performance, optimising I/O, and automating workflows.\n• Self-starter, low-ego, collaborative, high-energy.

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Benefits

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France:\n• Competitive cash salary and equity\n• Food: Daily lunch vouchers\n• Sport: Monthly contribution to a Gympass subscription\n• Transportation: Monthly contribution to a mobility pass\n• Health: Full health insurance for you and your family\n• Parental: Generous parental leave policy

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UK:\n• Competitive cash salary and equity\n• Insurance\n• Transportation: Reimburse office parking charges, or £90 per month for public transport\n• Sport: £90 per month reimbursement for gym membership\n• Meal voucher: £200 monthly allowance for meals\n• Pension plan: SmartPension (percentages are 5% Employee & 3% Employer)

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XML job scraping automation by YubHub

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As Microsoft continues to push the boundaries of AI, we are on the lookout for experienced individuals to work with us on the most interesting and challenging AI questions of our time. Our vision is to build systems that have true artificial intelligence across agents, applications, services, and infrastructure. We're looking for an experienced HPC Site Reliability Engineer (SRE) to join our High Performance Computing (HPC) infrastructure team. In this role, you'll blend software engineering and systems engineering to keep our large-scale distributed AI infrastructure reliable and efficient. You'll ensure that AI systems stay efficient and reliable with very high uptimes.

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Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

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This role is part of Microsoft AI's Superintelligence Team. The MAIST is a startup-like team inside Microsoft AI, created to push the boundaries of AI toward Humanist Superintelligence—ultra-capable systems that remain controllable, safety-aligned, and anchored to human values. Our mission is to create AI that amplifies human potential while ensuring humanity remains firmly in control. We aim to deliver breakthroughs that benefit society—advancing science, education, and global well-being.

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Responsibilities\nReliability & Availability : Ensure uptime, resiliency, and fault tolerance of HPC clusters powering MAI model training and inference.\nObservability : Design and maintain monitoring, alerting, and logging systems to provide real-time visibility into all aspects of HPC systems including GPU, clusters, storage and networking.\nAutomation & Tooling : Build automation for deployments, incident response, scaling, and failover in CPU+GPU environments.\nIncident Management : Lead on-call rotations, troubleshoot production issues, conduct blameless postmortems, and drive continuous improvements.\nSecurity & Compliance : Ensure data privacy, compliance, and secure operations across model training and serving environments.\nCollaboration : Partner with ML engineers and platform teams to improve developer experience and accelerate research-to-production workflows.

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Qualifications\nRequired Qualifications\nMaster’s Degree in Computer Science, Information Technology, or related field AND 2+ years technical experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering OR Bachelor’s Degree in Computer Science, Information Technology, or related field AND 4+ years technical experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering OR equivalent experience

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Preferred Qualifications\nStrong proficiency in Kubernetes, Docker, and container orchestration.\nKnowledge of CI/CD pipelines for Inference and ML model deployment.\nHands-on experience with public cloud platforms like Azure/AWS/GCP and infrastructure-as-code.\nExpertise in monitoring & observability tools (Grafana, Datadog, OpenTelemetry, etc.).\nStrong programming/scripting skills in Python, Go, or Bash.\nSolid knowledge of distributed systems, networking, and storage.\nExperience running large-scale GPU clusters for ML/AI workloads (preferred).\nFamiliarity with ML training/inference pipelines.\nExperience with high-performance computing (HPC) and workload schedulers (Kubernetes operators).\nBackground in capacity planning & cost optimization for GPU-heavy environments.

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Work on cutting-edge infrastructure that powers the future of Generative AI. Collaborate with world-class researchers and engineers. Impact millions of users through reliable and responsible AI deployments. Competitive compensation, equity options, and comprehensive benefits.

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XML job scraping automation by YubHub

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Opening. This role is open to hiring in Mississauga (Preferred) as well as Ottawa. We Are: At Synopsys, we drive the innovations that shape the way we live and connect. Our technology is central to the Era of Pervasive Intelligence, from self-driving cars to learning machines. We lead in chip design, verification, and IP integration, empowering the creation of high-performance silicon chips and software content. Join us to transform the future through continuous technological innovation. You Are: You possess strong analytical and problem-solving skills, with a proven track record of driving innovation and implementing solutions that improve efficiency and performance. You are detail-oriented and can work independently with minimal supervision. Your communication skills are excellent, enabling you to present complex technical information clearly and effectively to diverse audiences, including senior management and cross-functional teams. ## What you'll do \\ Manage and optimize compute and disk resources to support large-scale simulation workloads. \\ Monitor and troubleshoot compute infrastructure to ensure high availability and performance. \\ Collaborate with IT and infrastructure teams to scale resources as needed for complex simulation tasks. \\ Develop and implement strategies for efficient resource utilization, including job scheduling, load balancing, and storage optimization. \\ Identify opportunities for automation in simulation workflows and implement solutions to reduce manual effort and improve efficiency. \\ Develop custom scripts and tools using Python, Tcl, or other programming languages to automate repetitive tasks and enhance simulation processes. \\ Integrate automation solutions into existing workflows, ensuring seamless operation and scalability. \\ Stay updated on emerging technologies and methodologies to continuously improve automation capabilities. ## What you need \\ In-depth knowledge of compute infrastructure, including high-performance computing (HPC) environments, job schedulers (e.g., LSF), and disk storage systems. \\ Proficiency in simulation methodologies, including corner analysis, Monte Carlo simulations, and parasitic extraction. \\ Experience with automation scripting using Python, Tcl, or similar languages. \\ Exceptional analytical thinking skills with the ability to diagnose and resolve complex simulation and infrastructure issues. \\ Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, or a related field. \\ 3 or more years of experience in a relevant area.

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XML job scraping automation by YubHub

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