Вакансии

Location: United Kingdom As a Pre-Sales Engineer, you will be responsible for securing the technical 'win' in Mid-Market and Enterprise client organizations, inspiring them to choose Action1. The ideal candidate is results-oriented and highly driven, with a strong focus on both personal and team success, aiming for exceptional professional growth. Key responsibilities: Drive deals to closure through pre-sales support and technical account management. Conduct technical qualifications of prospects by thoroughly understanding their business and technical needs. Deliver client-specific product demonstrations. Support our sales team and customers primarily in the EMEA regions Requirements Essential system administration experience, with the ability to assist IT administrators in recognizing Action1 as their preferred patching solution. 1-3 years of B2B pre-sales experience (preferred but not mandatory). Strong understanding of IT administration and security principles. Ability to work both independently and collaboratively as part of a team in a fast-paced, dynamic environment. Genuine interest in information technology and systems administration, with a passion for staying updated on industry trends. Benefits Fully remote work, giving you the flexibility you need in the modern world A multitude of interesting challenges and opportunities. Non-stop professional growth and learning of new technologies. Stable income and flexible working hours, opportunities for promotion. Friendly and professional peers.

• Ведёт задачу по workflow команды: от тестирования до закрытия или возврата с комментарием. • Возвращает задачу, если по ней недостаточно информации для тестирования.

Location: San Francisco, CA Workplace: OnSite ABOUT US Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Our co-founders are Bret Taylor https://www.linkedin.com/in/brettaylor/ and Clay Bavor https://www.linkedin.com/in/claybavor/. Bret currently serves as Board Chair of OpenAI. Previously, he was co-CEO of Salesforce (which had acquired the company he founded, Quip) and CTO of Facebook. Bret was also one of Google's earliest product managers and co-creator of Google Maps. Before founding Sierra, Clay spent 18 years at Google, where he most recently led Google Labs. Earlier, he started and led Google’s AR/VR effort, Project Starline, and Google Lens. Before that, Clay led the product and design teams for Google Workspace. Sierra’s Applied Research team advances the quality, speed, and cost efficiency of the models powering our agents across a wide range of tasks. Our work spans post-training open-weight models for specialized capabilities and pioneering new approaches to voice AI, turning research breakthroughs into reliable production systems. WHAT YOU’LL DO - Tackle unsolved applied AI and ML problems. Pushing the boundaries of post-training, voice AI, and long-horizon task execution, building agents that can reason, adapt, and reliably carry out complex tasks for customers. - Determine when to build specialized models and when to use externally provided frontier intelligence, balancing quality, speed, and cost to deliver customer value. - Turn ambiguous product and customer problems into well-framed machine learning problems. - Develop, fine-tune, evaluate, and deploy models that improve Sierra’s customer-facing AI agents. - Advance our post-training work, from supervised fine-tuning toward reinforcement learning and long-horizon optimization. - Build rigorous evaluations that connect model performance to real-world agent quality. - Work with large-scale, proprietary datasets spanning text and audio interactions. - Collaborate across Sierra while taking ideas from experimentation through production deployment and customer impact. WHAT YOU’LL BRING - Strong experience building and deploying production machine learning and AI systems. We welcome backgrounds across applied ML, including search, ranking, recommendations, advertising, language, and speech. - Excellent judgment in framing product problems as ML tasks and selecting the simplest & most effective approach. - Experience working deeply with data, experimentation, evaluation, and model iteration. - A practical, evidence-driven approach to ML, with strong judgement around when a problem calls for modeling and when a simpler solution will deliver the best outcome. - The ability to operate with high agency and work across the stack when needed to deliver an outcome. - Alignment with Sierra’s values, including Craftsmanship, Customer Obsession, and Competitive Intensity. - A degree in computer science, machine learning, or a related field, or equivalent professional experience. EVEN BETTER… - Experience with supervised fine-tuning or reinforcement learning. - Experience in search, ranking, recommendations, or advertising systems. - Experience training and deploying language, speech, or multimodal models. - Experience designing model evaluations tied to real-world product outcomes. - Experience working with large-scale consumer datasets or high-volume ML products. - Contributions to applied ML research or benchmarks. OUR VALUES - Trust: We build trust with our customers with our accountability, empathy, quality, and responsiveness. We build trust in AI by making it more accessible, safe, and useful. We build trust with each other by showing up for each other professionally and personally, creating an environment that enables all of us to do our best work. - Customer Obsession: We deeply understand our customers’ business goals and relentlessly focus on driving outcomes, not just technical milestones. Everyone at the company knows and spends time with our customers. When our customer is having an issue, we drop everything and fix it. - Craftsmanship: We get the details right, from the words on the page to the system architecture. We have good taste. When we notice something isn’t right, we take the time to fix it. We are proud of the products we produce. We continuously self-reflect to continuously self-improve. - Intensity: We know we don’t have the luxury of patience. We play to win. We care about our product being the best, and when it isn’t, we fix it. When we fail, we talk about it openly and without blame so we succeed the next time. - Family: We know that balance and intensity are compatible, and we model it in our actions and processes. We are the best technology company for parents. We support and respect each other and celebrate each other’s personal and professional achievements. WHAT WE OFFER We want our benefits to reflect our values and offer the following to full-time employees: - Flexible (unlimited) paid time off - Medical, dental, and vision benefits for you and your family - Life insurance and disability benefits - Retirement plan dependent on country of employment - Parental leave - Fertility and family building benefits through Carrot - Lunch, as well as delicious snacks and coffee to keep you energized  - Discretionary benefit stipend giving people the ability to spend where it matters most - Free alphorn lessons These benefits are further detailed in Sierra's policies, may vary by region, and are subject to change at any time, consistent with the terms of any applicable compensation or benefits plans. Eligible full-time employees can participate in Sierra's equity plans subject to the terms of the applicable plans and policies. BE YOU, WITH US We're working to bring the transformative power of AI to every organization in the world. To do so, it is important to us that the diversity of our employees represents the diversity of our customers. We believe that our work and culture are better when we encourage, support, and respect different skills and experiences represented within our team. We encourage you to apply even if your experience doesn't precisely match the job description. We strive to evaluate all applicants consistently without regard to race, color, religion, gender, national origin, age, disability, veteran status, pregnancy, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.

Location: San Francisco, CA Workplace: OnSite ABOUT US Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Our co-founders are Bret Taylor https://www.linkedin.com/in/brettaylor/ and Clay Bavor https://www.linkedin.com/in/claybavor/. Bret currently serves as Board Chair of OpenAI. Previously, he was co-CEO of Salesforce (which had acquired the company he founded, Quip) and CTO of Facebook. Bret was also one of Google's earliest product managers and co-creator of Google Maps. Before founding Sierra, Clay spent 18 years at Google, where he most recently led Google Labs. Earlier, he started and led Google’s AR/VR effort, Project Starline, and Google Lens. Before that, Clay led the product and design teams for Google Workspace. ABOUT THE ROLE Sierra’s AI agents depend on foundation models to reason and act in real time. The Inference team builds the systems that make those models fast, reliable, and efficient at scale. As a Software Engineer on Inference, you’ll help define Sierra’s inference architecture across both self-hosted models and third-party inference providers. You’ll work on the systems responsible for serving and routing inference, managing capacity and quota, and optimizing for latency, reliability, and cost. This is a systems-first role at the intersection of distributed infrastructure and AI. You don’t need to be an ML researcher—we’re looking for engineers who love complex systems problems and are excited to apply that expertise to one of the fastest-moving areas of AI infrastructure. WHAT YOU'LL DO - Partner with frontier labs and providers. At our scale, we rely on frontier labs, and inference providers to supply capacity, training and inference infrastructure. - Shape Sierra’s inference architecture. Design how inference traffic flows across models, infrastructure, and providers, including new serving and proxy layers as Sierra scales. - Build for low latency and high reliability. Develop systems for routing, failover, capacity management, and quota that keep inference performant and available across large-scale production workloads. - Build and operate self-hosted inference. Run models on GPU infrastructure, from building containers and operating inference engines to managing the underlying compute capacity. - Optimize inference performance. Work with the Applied Research team on techniques such as speculative decoding and serving-engine optimizations that improve latency, throughput, and cost. - Build across a hybrid inference stack. Work with both Sierra-managed infrastructure and leading inference platforms, making architectural decisions about where and how workloads should run. - Push the serving stack forward. Work closely with inference providers to tune engines and infrastructure for Sierra’s workloads. - Support the broader model lifecycle. Contribute to infrastructure that enables post-training while partnering closely with our Models and Agent Runtime teams. WHAT YOU'LL BRING - Deep systems thinking and strong distributed systems fundamentals. - Experience designing, building, and operating large-scale production systems. - Strong judgment around tradeoffs involving latency, reliability, capacity, and cost. - Experience taking ownership of complex infrastructure from architecture through production operation. - Excitement about applying systems expertise to AI infrastructure and learning quickly as the underlying technology evolves. EVEN BETTER - Experience with ML infrastructure, MLOps, or production inference systems. - Experience serving LLMs or other large models at scale. - Experience operating self-hosted inference and GPU infrastructure. - Familiarity with inference frameworks such as vLLM or SGLang. - Experience with post-training infrastructure or inference-performance optimization. OUR VALUES - Trust: We build trust with our customers with our accountability, empathy, quality, and responsiveness. We build trust in AI by making it more accessible, safe, and useful. We build trust with each other by showing up for each other professionally and personally, creating an environment that enables all of us to do our best work. - Customer Obsession: We deeply understand our customers’ business goals and relentlessly focus on driving outcomes, not just technical milestones. Everyone at the company knows and spends time with our customers. When our customer is having an issue, we drop everything and fix it. - Craftsmanship: We get the details right, from the words on the page to the system architecture. We have good taste. When we notice something isn’t right, we take the time to fix it. We are proud of the products we produce. We continuously self-reflect to continuously self-improve. - Intensity: We know we don’t have the luxury of patience. We play to win. We care about our product being the best, and when it isn’t, we fix it. When we fail, we talk about it openly and without blame so we succeed the next time. - Family: We know that balance and intensity are compatible, and we model it in our actions and processes. We are the best technology company for parents. We support and respect each other and celebrate each other’s personal and professional achievements. WHAT WE OFFER We want our benefits to reflect our values and offer the following to full-time employees: - Flexible (unlimited) paid time off - Medical, dental, and vision benefits for you and your family - Life insurance and disability benefits - Retirement plan dependent on country of employment - Parental leave - Fertility and family building benefits through Carrot - Lunch, as well as delicious snacks and coffee to keep you energized  - Discretionary benefit stipend giving people the ability to spend where it matters most - Free alphorn lessons These benefits are further detailed in Sierra's policies, may vary by region, and are subject to change at any time, consistent with the terms of any applicable compensation or benefits plans. Eligible full-time employees can participate in Sierra's equity plans subject to the terms of the applicable plans and policies. BE YOU, WITH US We're working to bring the transformative power of AI to every organization in the world. To do so, it is important to us that the diversity of our employees represents the diversity of our customers. We believe that our work and culture are better when we encourage, support, and respect different skills and experiences represented within our team. We encourage you to apply even if your experience doesn't precisely match the job description. We strive to evaluate all applicants consistently without regard to race, color, religion, gender, national origin, age, disability, veteran status, pregnancy, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.

Location: San Francisco; Toronto; New York; Montreal; Seattle Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We're looking for distributed systems engineers and product-minded generalists to build the distributed runtime that powers large-scale LLM inference on Baseten. Our inference platform empowers customers to deploy and operate cutting-edge models with industry-leading performance, scalability, and reliability. It also powers Model APIs, our hosted endpoints for the latest open-source models. You'll work across the stack, from the developer experience customers use to deploy models, through the libraries behind features like tool calling and reasoning, down to the systems that orchestrate deployments on Kubernetes and route traffic efficiently. Your job is to make sure every model on our platform is fast, reliable, and cost-efficient. You'll join a small, high-impact team at the intersection of distributed systems, model performance, infrastructure, and product, helping define how developers use AI models at scale. This role is ideal for engineers who enjoy owning systems in production, solving hard integration problems, and making complex infrastructure simple and reliable for users.   EXAMPLE INITIATIVES You'll get to work on these types of projects on our Inference Platform: - The Baseten Inference Stack at NVIDIA Dynamo Day https://www.baseten.co/blog/nvidia-dynamo-day-baseten-inference-stack/ - 2x faster inference with KV cache-aware routing https://www.baseten.co/blog/how-baseten-achieved-2x-faster-inference-with-nvidia-dynamo/ - How Baseten multi-cloud capacity management (MCM) unifies deployments https://www.baseten.co/blog/how-baseten-multi-cloud-capacity-management-mcm-powers-cloud-self-hosted-and-hybr/#comparing-deployment-options-cloud-vs-self-hosted-vs-hybrid RESPONSIBILITIES - Build the infrastructure and orchestration systems that deploy and run large-scale distributed LLM inference, including routing, autoscaling, scheduling, and runtime management. - Design, build, and operate Model APIs, with a focus on advanced inference capabilities: structured outputs (JSON mode, grammar-constrained generation), tool/function calling, and multimodal serving. - Implement platform fundamentals such as API versioning, validation, usage metering, quotas, and authentication. - Instrument deep observability (metrics, traces, logs) and build repeatable benchmarks for speed, reliability, and quality. Help set best practices for testing, release automation, and operational excellence. - Debug and harden complex production systems spanning Kubernetes, distributed runtimes, networking, and GPU workloads to improve reliability and scalability. - Partner with Inference Performance engineers and other teams to make new optimizations broadly available to customers and easy to configure. - Own projects end to end, from architecture through deployment, monitoring, and iteration on customer feedback. Along the way, make thoughtful tradeoffs between performance, reliability, operational simplicity, and developer experience.   REQUIREMENTS - Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, or a related field, or equivalent practical experience. - 3+ years building and operating distributed systems, backend infrastructure, or large-scale APIs where reliability, latency, and scale are first-class concerns. - A proven track record of owning low-latency, reliable backend services, including rate limiting, auth, quotas, metering, and migrations. - Infrastructure instincts with a feel for performance: profiling, tracing, capacity planning, and SLO management. - Comfort debugging performance and reliability issues across multiple layers of the stack, from application behavior down to runtime and infrastructure internals. - A strong sense of developer experience. You think about how systems are used, not just how they work. - Eagerness to learn new languages, frameworks, and systems, and a real interest in inference engineering. Prior ML or LLM experience isn't required, though experience with model serving or inference systems is a plus. - Excellent written communication and collaboration skills, including writing clear design docs and working across functions.   NICE TO HAVE - Experience with or contributions to LLM inference engines and frameworks such as vLLM, SGLang, TensorRT-LLM, TGI, or Dynamo. - Deep Kubernetes experience, including operators and custom resources, plus familiarity with service meshes or API gateways. - Experience with distributed scheduling, autoscaling, or service orchestration. - Experience operating GPU workloads in production. - A background in developer-facing infrastructure or APIs, or contributions to open-source infrastructure or ML systems. - Familiarity with observability tooling, CI/CD systems, or release automation. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: San Francisco; Toronto; New York; Montreal; Seattle Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We're looking for inference performance engineers who want to make the world's most demanding AI workloads run faster and more efficiently. You'll work across the stack, from the inference engine and runtime through scheduling, serving, and routing. Along the way you'll apply techniques like prefill/decode disaggregation, speculative decoding, and KV-cache management. You'll reason from first principles about where time and memory go, find what's holding performance back, and close the gap. Your work directly impacts how fast our customers' models run and how efficiently we serve them. This role is ideal for someone who thrives in a fast-paced startup environment and is eager to make significant contributions to the exciting field of LLM inference.   EXAMPLE INITIATIVES You'll get to work on these types of projects as an Inference Performance engineer: - Agentic Kernels in Production https://www.baseten.co/blog/agentic-kernels-in-production/ - How we built the new fastest API for GLM-5.2 https://www.baseten.co/blog/how-we-built-the-new-fastest-api-for-glm-52/ - Live draft model training for speculative decoding https://www.baseten.co/blog/live-draft-model-training-for-speculative-decoding/#eliminating-traditional-bottlenecks - Making Kimi K3 Tokenization 18x faster https://www.baseten.co/blog/making-kimi-k3-tokenization-18x-faster-for-million-token-agentic-workloads/ - The Baseten Inference Stack https://www.baseten.co/resources/guide/the-baseten-inference-stack/ - Driving model performance optimization https://www.baseten.co/blog/driving-model-performance-optimization-2024-highlights/ RESPONSIBILITIES - Implement and productionize cutting-edge inference techniques, working deep in runtime internals. That includes quantization, speculative decoding, KV-cache reuse, chunked prefill, LoRA, guided generation for structured outputs, and custom scheduling and routing algorithms. - Profile and optimize inference end to end, from kernel launch overhead and memory layout up to request scheduling, prefill/decode disaggregation, and cache-aware routing. Run cross-layer investigations, such as tracing a tail-latency regression from request timing through routing and batching down to a kernel. - Turn performance into cost savings. Improve tokens per GPU-hour, raise utilization, and give customers and internal teams clear latency/throughput/cost tradeoffs. - Bring up and tune new model architectures on new hardware quickly, often in the same week they're released. - Build benchmarking frameworks that measure real-world performance across model architectures, batch sizes, sequence lengths, and hardware configurations. - Contribute upstream to open-source inference engines (vLLM, SGLang, TensorRT-LLM), and partner closely with model, infrastructure, and customer-facing teams to ship wins.   REQUIREMENTS - Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or related field. - Experience with one or more general-purpose programming languages, such as Python or C++. - Familiarity with LLM optimization techniques (e.g., quantization, speculative decoding, continuous batching). - Strong familiarity with ML libraries, especially PyTorch, TensorRT, or TensorRT-LLM. - Demonstrated interest and experience in LLMs. - Deep understanding of GPU architecture.   NICE TO HAVE - Proficiency in enhancing the performance of software systems, particularly in the context of large language models (LLMs) - Contributed to vLLM, SGLang, TensorRT-LLM, or another inference engine. - Worked on large-scale distributed serving: autoscaling, load balancing, multi-region or multi-cloud capacity. - Written or optimized GPU kernels (CUDA, Triton, CUTLASS, or similar) - Worked on quantization (FP8/FP4, AWQ, GPTQ) or speculative decoding in production. - Deep understanding of software engineering principles and a proven track record of developing and deploying AI/ML inference solutions. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: Paris Workplace: OnSite ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. THE MISSION You will be the reference point for the most complex hardware interventions across our sites. Mistral's compute is our largest investment and the foundation of frontier AI in Europe. As a Senior Data Center Hardware Engineer, you own the interventions that require the highest level of technical expertise: multi-node failures in production, full-rack deployments of GPU-dense systems, and vendor escalations where you speak as the technical authority for our sites. You'll work end-to-end — compute, storage, interconnect, power and cooling interfaces — across our data center footprint, raising the bar on reliability as our capacity grows. This is a hands-on senior IC role: deep technical leadership and mentorship, no direct line management. Location: Based at one of our data centers in the Paris region, with occasional visits to our other sites as needed.   WHAT YOU'LL DO - Lead complex interventions — vendor-level and multi-node operations in production: full rack builds and bring-up, GPU-dense platform deployment (H100/H200-class and next-gen, including liquid-cooled racks), intricate recabling, post-restart diagnosis. You own the plan, the risk assessment, and the rollback. - Own vendor management — coordinate repairs, part replacements and escalations directly with hardware vendors; hold them to their SLAs; drive proactive fixes from failure trends. - Diagnose at depth — correlate symptoms across compute, storage, interconnect and cooling; read BMC/IPMI consoles, OS logs and crash dumps; methodical root-cause analysis that ends in concrete corrective actions. - Deploy and validate — structured bring-up and validation of new racks and platforms; work with networking and infrastructure teams to take installations from delivery to production-ready. - Lift the team — mentor and pair with L1–L3 engineers on live operations; turn your knowledge into SOPs, checklists and training material; propose and build small automation (Python/Bash) to shorten MTTR. - Work the facility interface — partner with critical facilities/engineering ops on power, cooling (air and liquid), PDU and monitoring (BMS/EPMS) boundaries; enforce ESD/LOTO/PPE and audit-ready change discipline everywhere. - Own spares & reliability — plan spares strategy, track fleet failure trends, and run proactive vendor actions before incidents happen.   ABOUT YOU - 6+ years in data center / server hardware or L2/L3 hardware support, including complex hands-on work in production environments (HPC/AI/cloud at scale strongly preferred). - End-to-end hardware expertise: CPU/memory/PCIe (incl. accelerators), NICs, PSUs, drives, network, power and cooling interfaces (DLC included); strong judgment on when and how to escalate. - Diagnostics depth: confident with BMC/IPMI logs, Linux logs and CLI checks; structured root-cause analysis. - Deployment experience: you've taken racks from delivery to production, with concrete examples to show for it. - Vendor fluency: you've managed hardware vendors — RMAs, field replacements, escalations — to concrete outcomes. - Impeccable safety and discipline: ESD/LOTO/PPE without exception; clean, labeled, auditable work. - Communication & mentoring: clear status updates and handovers; able to coach engineers during live incidents with composure. - Mobility: willing to visit our other sites in the Paris region as needed.   NICE TO HAVE - GPU-dense platform experience: NVIDIA H-series (H100/H200) or GB-series rack deployments. - High-speed interconnects (InfiniBand/RoCE), RAID/storage (NVMe/SAS/SATA), vendor tooling (iDRAC/iLO/IPMI). - Automation (Python/Bash) for ops tooling and reporting; ticketing (Jira/ServiceNow), inventory/RMA flows. - Hardware qualification, burn-in or factory validation background. WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: San Francisco, CA | New York City, NY | 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 Frontier AI is on track to be among the most consequential and most adversarially-targeted technology in the world. The capability curve is steep, the adversaries who want these systems are extremely well-resourced, and the security bar this will eventually require is well beyond where the industry operates today. Incremental hardening alone is not going to close that gap, so we need breakthroughs and a group of people to go find them. Security Labs is that team. We run a portfolio of high-risk, high-expected-value security projects: the work that seems impractical until someone optimistic and stubborn enough actually tries it. Projects run on the order of weeks rather than quarters, and each one is either handed off to the Anthropic team that will own it in production or wound down with a writeup of what we learned. We expect a meaningful fraction of our bets not to land. This team is intentionally scrappy and ambitious. We're intentionally iterating fast on high-risk research bets and we expect a meaningful fraction not to land. If a 30% project success rate sounds uncomfortable, or uncharted and chaotic territory isn’t both frightening and exciting, this probably isn't the right fit. There are other places in Anthropic Security doing important work with more structure, less risk, and more certain paths to positive outcomes. The questions we're trying to answer include: Can our core research workflows survive extreme isolation? Can we replace trust with cryptographic guarantees? Can the models themselves become our most effective security control? What would it actually take to defend against a tier-1 state adversary, and how much of that can we build now? Who we're looking for. We're hiring generalists with rare depth. You're a strong software engineer as a baseline, and on top of that you've gone deep in at least one area most engineers don't get near: firmware or hardware security, applied cryptography, OS / kernel / hypervisor internals, formal methods, reverse engineering, or high-assurance and cross-domain systems. You've built things under your own direction, you're comfortable jumping layers when the problem demands it, and you'd rather take a swing at something that might not work than ship the safe incremental thing. You think the trajectory of AI matters a great deal, you're not comfortable with how the security side of it is going by default, and you'd rather be on the inside building the answer than watching from outside. Current Project Areas The portfolio changes as we learn. The kinds of bets currently in flight or queued: Standing up a prototype high-assurance research cluster: running real Anthropic training and research workloads under extreme isolation and physical security controls, and finding out exactly where productivity breaks and what we'd need to invent to get it back Provable inference: cryptographic verification (zero-knowledge proofs, attestation chains) that a given output came from a specific model running specific code, replacing "trust us" with math Build hypervisor-isolated microVM workload isolation across the fleet Compiling an ML kernel through a formally verified pipeline where every lowering step carries a machine-checked proof of equivalence, making compilation-layer sabotage mathematically detectable Regenerating clusters: automation that spins up a purpose-built cell, runs a workload, and tears the whole thing down on a TTL measured in hours, so attacker persistence has an expiry date Using Claude itself to drive security work end to end: threat modeling new compute platforms, rewriting critical services to zero external dependencies, running the test equipment that validates what hardware datasheets claim Part of your job is deciding what comes next. We hire people we trust to pick good bets, and project selection is owned by the engineers doing the work. Key responsibilities Own Security Labs projects end to end. You'll scope the bet, build the prototype, run it against real workloads, and bring it to either a hand-off or a documented exit Stand up novel security infrastructure fast (isolated clusters, attestation chains, hypervisor and runtime work, verification tooling) optimizing for what we learn rather than for permanence Find the receiving team early, bring them along while you build, and hand them something they actually want to own Work embedded with research and infrastructure teams (Pretraining, RL, Inference, Compute) to test whether their workflows survive what you're proposing, and document precisely where they don't Turn experimental results into short writeups that shape Anthropic's long-term security architecture, and into costed contingency plans we could execute on short notice Help pick the next round of bets and influence the industry to get better along the way Minimum qualifications Genuinely care about where AI is heading and want to work on the security problems that determine whether it goes well. This is the most important thing on this list Have real depth in at least one area most software engineers don't touch (e.g. firmware or hardware security, applied cryptography, OS / kernel / hypervisor internals, formal methods and verification, reverse engineering and exploit development, or high-assurance / cross-domain systems) Have built and shipped things under your own direction. Maybe you founded a company or research group, maintained an open-source project other people depend on, or shipped research that changed how people in your field work. We weight this far more than where you've worked or for how long Have a track record of choosing the problem yourself and seeing it through, rather than only executing a plan someone else handed you Are comfortable jumping between domains and layers of the stack when the problem calls for it, and have actually done so Have run prototypes or experiments where the goal was answering a hard question rather than shipping a permanent system, including ones that didn't pan out Write clearly enough to turn weeks of work into a couple of pages someone can act on Change your mind when the evidence says to, and are fine being the least-expert person in a room full of specialists Care about defense. Plenty of folks here come from offense and that background is valuable, but what you actually want to spend your time on now is making systems hold up Are a strong programmer (Python plus at least one of Rust, Go, or C/C++) and can stand up real infrastructure without that being the interesting part of your week Preferred qualifications Experience inside airgapped or high-side environments (classified networks, cross-domain solutions, ICS/SCADA, financial trading infrastructure) and the operational realities of working in them A background in offensive security, red teaming, or vulnerability research, with calibrated intuitions for which threats actually matter Familiarity with ML infrastructure (training pipelines, distributed schedulers, inference serving, accelerator hardware) sufficient for grounded conversations with researchers about what their workloads actually need A history of working in environments built around rapid iteration rather than rigid change control: startups, applied research groups, independent consulting, small security shops The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Boston, MA Workplace: OnSite At WHOOP, we're on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. By combining advanced sensing, data science, and technology designed for continuous wear, WHOOP delivers insights that help members better understand and improve their health and performance. WHOOP is looking for a Senior Sensor Intelligence Engineer to join our Embedded Controls team within Sensor Intelligence and help shape the sensing capabilities across current and future WHOOP devices. Sitting at the intersection of signal processing, machine learning, embedded systems, and sensor technology, you will develop algorithms that interpret real-world sensor signals and intelligently control how sensors operate on-device. You will work across the sensing stack from understanding sensor physics and signal characteristics through algorithm development and embedded deployment to enable increasingly sophisticated sensing capabilities while meeting the power, compute, memory, and reliability constraints of a continuously worn device. RESPONSIBILITIES: - Develop signal-processing and machine-learning algorithms that interpret physiological, motion, and other wearable sensor signals, including applications such as activity and state detection, wear detection, signal-quality assessment, and device-state estimation - Design intelligent embedded control strategies that dynamically configure sensor behavior based on signal quality, device state, user context, system requirements, and power constraints - Characterize new and existing sensors by understanding signal behavior, noise sources, artifacts, dynamic range, sampling requirements, calibration, operating modes, and the impact of analog and acquisition architectures - Prototype algorithms and sensing strategies using tools such as Python or MATLAB, then partner with Firmware Engineering to translate them into robust, computationally efficient implementations suitable for resource-constrained embedded systems - Partner closely with Firmware, Electrical Engineering, Data Science, Hardware, and broader Sensor Intelligence teams to bring up new sensing modalities, debug issues across the sensing stack, and inform sensing architectures for future WHOOP products - Develop experiments, analysis frameworks, and validation methodologies to evaluate sensor and algorithm performance across users and real-world conditions, optimizing solutions for accuracy, robustness, latency, memory, compute, and power QUALIFICATIONS: - BS, MS, or PhD in Electrical Engineering, Computer Engineering, Biomedical Engineering, Computer Science, Applied Physics, or a related technical field, or equivalent practical experience - Strong foundation in digital signal processing and time-series analysis, with experience applying techniques such as filtering, spectral analysis, sampling theory, noise reduction, and feature extraction to real-world sensor data - Experience developing signal-processing and/or machine-learning algorithms for noisy, artifact-prone sensor data and taking algorithms beyond offline analysis toward real-time, embedded, or production implementation - Strong proficiency in Python, MATLAB, or similar algorithm-development environments, with working knowledge of C/C++ and embedded-system considerations such as timing, memory, compute, and hardware interfaces - Working knowledge of sensor and electrical systems, including concepts such as ADCs, analog front ends, sampling, digital interfaces, noise, calibration, and signal acquisition - Ability to investigate and solve ambiguous engineering problems across algorithm, firmware, sensor, and electrical boundaries using strong experimental and analytical methods - Experience with wearable, physiological, optical, impedance, motion, multimodal, or related sensor systems is valued; experience with embedded inference, sensor fusion, fixed-point processing, quantization, low-power sensing, or microcontroller deployment is a plus - Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions. Interested in the role, but don't meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply. WHOOP is an Equal Opportunity Employer and participates in E-Verify to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Location: San Francisco, CA | New York City, NY | 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 team Secure Frameworks is the team in Anthropic's Security Engineering org that builds shared security libraries and frameworks the rest of the company builds on. We collaborate closely with teams and leaders across Anthropic to own critical security foundations, including workload identity and authorization, cryptographic frameworks, and the centralized access proxy that  secures traffic to internal applications. Our mission is to make the secure thing easy and the easy thing secure by helping Anthropic engineers get secure behaviour by default and preventing entire classes of vulnerabilities through careful design. We own what we ship, help customers adopt better security practices and at times we work embedded directly in research, infrastructure or product teams. About the role You will design, build and run security foundations used across Anthropic's fleet, from threat model through production, on-call and adoption. Much of the work is greenfield: our infrastructure is growing quickly and the timelines are compressed, so you will help define the architecture rather than inherit it. Depending on your strengths you will focus on one or two of the areas below and contribute across the rest. Build critical security foundations including cryptographic frameworks, mTLS infrastructure, secure serialization, and authorization systems, designed to prevent entire classes of vulnerabilities Partner with product, research, infrastructure, and other security teams to ensure frameworks integrate smoothly with lower-layer security controls Scope, design and build security services and libraries end-to-end, maintain them in production, and drive through ambiguous technical problems with minimal oversight Build and operate the zero-trust access gateway that fronts Anthropic's internal services Design and roll out authorization frameworks so services enforce least privilege consistently instead of each inventing its own access controls Own cryptographic frameworks and libraries Mentor engineers, contribute to hiring, and grow security engineering culture across Anthropic Minimum qualifications 8+ years of software engineering experience building security-relevant systems in production, including leading complex initiatives independently Strong programming skills in Go, Rust or Python Deep understanding of authentication and authorization systems: OAuth 2.0 / OIDC, JWTs, service-to-service auth, policy-based access control Hands-on experience with PKI, X.509, mTLS and workload identity Working knowledge of applied cryptography: choosing and composing primitives correctly, key management, and the ways libraries fail in practice Experience shipping and operating high-reliability services on Kubernetes across cloud and on-prem environments, including being on call for them A track record of bringing clarity and ownership to ambiguous technical problems and driving them to resolution across teams A strong sense of developer experience Clear communication across all levels of the organization Passion for AI safety and the role security engineering plays in building trustworthy AI systems Preferred qualifications Built security frameworks or libraries adopted across an engineering organization, and done the work to drive that adoption Built or operated a zero-trust / BeyondCorp-style access gateway or identity-aware proxy Migrated a fleet to mTLS at scale, including certificate issuance and staged enforcement Maintained or contributed to open-source cryptographic libraries Designed authorization systems using policy languages or relationship-based models Familiarity with ML infrastructure (training, inference serving) 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: $32,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: San Francisco, CA | New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The Security Risk team is responsible for how Anthropic identifies, prioritizes, and drives treatment of its most important security risks. We are rebuilding risk management to operate as an engineering function, using automation, quantitative risk and AI-native platforms to enable decision making. The risks we assess span Anthropic's full security landscape, so the team needs breadth across domains and the judgment to go deep wherever a decision demands it. Security Risk, in deep partnership with Security Engineering, helps define the security program and shapes how investment and treatment decisions get made. The conventional GRC playbook was not built for a company shipping frontier AI. You will help define what replaces it, with a direct line to CISO-level decisions. As a Security Risk Analyst you will take risk questions from leadership and partner teams and drive them to a decision. Sometimes that is a fast, structured qualitative assessment and other times it is a deep FAIR-based quantitative analysis. Either way you bring leadership a clear position, the tradeoffs, and honest uncertainty, and you defend it under challenge. In parallel, you will help turn quantitative risk into a product partner teams can leverage, and define the standards a growing risk function will run on. Key responsibilities Enable leadership and partner teams to make risk-informed decisions. Take ambiguous risk questions, drive them to a documented decision, and communicate the quantitative and qualitative tradeoffs clearly Lead quantitative analysis of the company's top security risk scenarios using FAIR, calibrated estimation, and simulation, working with the engineers who own the systems and presenting results in terms leadership can act on Partner with Security Engineering to assess threat scenarios and control effectiveness so security investment is right-sized to actual risk and remediation is sequenced where it buys down the most risk Frame escalations and risk treatment decisions with a clear recommendation, an honest statement of uncertainty, and re-evaluation triggers, and pressure test those decisions with risk owners Shape the analysis and narrative behind leadership risk reviews, and help define risk appetite and the risk metrics the organization should measure and why Use AI and automation to scale risk analysis, and own the calibration and quality review that keep the outputs trustworthy Turn one-off analyses into reusable methods, templates, and training so risk analysis becomes increasingly self service for partner teams, and raise the analytical quality of the risk register You may be a good fit if you Have owned security or technology risk analysis end to end, qualitative and quantitative, and can point to a decision your output changed, whether a funding call, a launch call, a remediation sequence, or a documented acceptance Have hands-on FAIR-style quantification experience, including scenario decomposition, calibrated estimation, and Monte Carlo simulation in Python, R, or spreadsheet tooling, briefed to decision makers Thrive in ambiguity. You frame a moving question into something analyzable, pick the depth that fits, and drive to a decision rather than a report Put defensible severity and likelihood on ambiguous problems, state uncertainty honestly, and change your position when the evidence changes Have enough technical security depth to decompose an attack path with security engineers and be credible in the room Can compress a complex risk position into one paragraph for the CISO, make the tradeoff explicit, and defend it under pointed questions Use Claude or other LLMs as daily working tools and review model output critically Are low ego and collaborative, build credibility through the work, and care about AI safety and the role security risk plays in it Strong candidates may also Have applied FAIR-CAM or another structured approach to control effectiveness and attack path modeling Have built or operated a cyber risk quantification program, or turned quantitative analysis into tooling, templates, or training that others ran Have helped define risk appetite or tolerance thresholds an organization actually used to make decisions Bring a background in security engineering, detection, threat intelligence, actuarial science, or decision science that grounds the numbers in real systems Are familiar with security risks specific to AI systems, such as goal or intent modification, and how they change traditional threat models 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: $270,000 — $345,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Foster City, CA Workplace: Hybrid Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation. ABOUT THE ROLE We're looking for a Security Engineer with deep incident response experience to help defend Replit, a fast-moving, cloud-native AI vibe-coding platform. You'll be the person who takes charge when something goes wrong. You'll lead investigations from first signal to root cause, coordinate containment across Security, SRE, and Engineering, and keep stakeholders informed while the incident is moving fast. This isn't a pure responder role. We want a well-rounded security engineer who has run real incidents and wants to make the next one faster. You'll write the scripts, automations, and tooling that take manual work out of triage, evidence collection, and containment. You'll also turn lessons from each incident into better detections, playbooks, and platform hardening.   RESPONSIBILITIES Incident Response - Serve as incident commander or technical lead for security incidents, from detection and triage through containment, eradication, recovery, and post-incident review. - Coordinate response across Security, SRE, Engineering, Legal, and leadership. Drive decisions under pressure and keep a clear record of actions taken. - Communicate incident status, impact, and risk clearly to technical and executive audiences. - Participate in and help shape the security on-call rotation. Investigation & Forensics - Investigate suspicious activity across cloud infrastructure, containers, identity systems, and application layers using SIEM, Cloud Logging, telemetry, and host and container artifacts. - Determine scope, root cause, attacker behavior, and blast radius for confirmed incidents. - Quickly assess whether emerging threats (0-days, active exploitation campaigns, bug bounty findings, customer reports) apply to Replit, and whether we're already affected. IR Automation & Tooling - Build scripts, automations, and tools (Python, Go, Bash, or directly on Replit) that speed up response, such as automated enrichment, evidence collection, credential and session revocation, workload isolation, and alert triage. - Develop and maintain response playbooks and runbooks, and automate them where possible. - Integrate response workflows with SIEM, SOAR, ticketing, and chat tooling to cut time-to-contain. Detection & Continuous Improvement - Turn incident findings into new or improved detections, logging coverage, and visibility. - Lead blameless post-incident reviews and drive remediation items to completion. - Run tabletop exercises and simulations to test readiness and find gaps.   REQUIRED SKILLS & EXPERIENCE - Proven experience leading or serving as technical lead on security incidents in a cloud or SaaS environment. - Strong hands-on investigation skills with SIEM, cloud audit logs, and log-based analysis. Comfortable working through large datasets under time pressure. - Proficiency writing production-quality scripts or tools in Python, Go, or Bash for investigation and automation. - Solid knowledge of cloud architecture and security, especially Google Cloud Platform (IAM, audit logging, GKE, networking). - Working knowledge of Kubernetes and containers, including how to investigate and contain compromised workloads. - Understanding of identity systems, SaaS architectures, and common cloud attack paths (credential theft, privilege escalation, supply chain, token abuse). - Familiarity with software engineering fundamentals, CI/CD pipelines, and package ecosystems, so you can work effectively with Engineering on code-level fixes. - Understanding of IR frameworks and lifecycle (for example NIST 800-61), plus the vulnerability lifecycle and exploitability analysis.   PREFERRED QUALIFICATIONS - Experience building or operating SOAR or other response automation platforms. - Digital forensics experience with cloud, Linux hosts, or containers. - Experience with threat intelligence, threat hunting, or security research. - Experience with bug bounty programs or coordinated vulnerability disclosure. - Familiarity with detection-as-code practices and writing detection rules. - Experience in fast-paced, cloud-native, or AI/ML-driven environments. - Relevant certifications (such as GCIH, GCFA, GCFR), or equivalent hands-on experience. WHAT WE VALUE - Curiosity & initiative: Strong desire to understand attacker behaviors, emerging threats, and how they apply to real-world systems. - Speed & analytical rigor: Ability to quickly assess high-risk vulnerabilities with clear, evidence-based reasoning. - Collaboration: Comfort working across cross-functional teams spanning Security, SRE, Engineering, and Infrastructure. - Clear communication: Ability to explain findings, risks, and mitigation strategies to stakeholders at all levels. - Ownership mindset: Takes initiative to drive investigations, improvements, and remediations to completion. - Continuous learning: Passion for staying up to date on new vulnerabilities, exploit trends, and cloud-native security best practices. Full-Time Employee Benefits Include: 💰 Competitive Salary & Equity 💹 401(k) Program with a 4% match (US Only) ⚕️ Health, Dental, Vision and Life Insurance 🩼 Short Term and Long Term Disability 🚼 Paid Parental, Medical, Caregiver Leave 🏝 Flexible Time Off (FTO) + Holidays 🚗 Commuter Benefits (In-Office & US Only) 📱 Monthly Wellness Stipend 🧑‍💻 Autonomous Work Environment 🖥 In Office Set-Up Reimbursement (In-Office Only) 🚀 Quarterly Team Gatherings ☕ In Office Amenities (In-Office Only) Want to learn more about what we are up to? - Self-driving Company https://replit.com/blog/self-driving-company - Replit Agent at Scale https://replit.com/blog/evaluating-and-improving-agent-at-scale - AI Adoption https://replit.com/blog/ai-adoption - Build Open-Source Apps https://replit.com/build/open-source-app-builder   Interviewing + Culture at Replit - Operating Principles https://blog.replit.com/operating-principles - Reasons not to work at Replit https://blog.replit.com/reasons-not-to-join-replit To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds.

Location: San Francisco, CA | New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role As an AV Engineer focused on Platform & Automation, you'll build and own the software layer of Anthropic's collaboration systems — the tools, integrations, and engineering practices that let a small AV team support a fast-growing global office portfolio. Our rooms, event spaces, and broadcast systems are increasingly managed through APIs and code: device management platforms, DSP and control systems, conferencing administration, ticketing, and paging. Your job is to turn that surface into reliable, team-owned tooling, and to bring software engineering discipline to how we build and maintain it. You can expect a lot of your time writing, shipping, and operating code — fleet health monitoring, automated room testing and ticketing, integrations between our AV platforms and IT systems, and the internal tools that AV Engineering, AV Operations, and Events rely on every day. You'll also help own the administration of the software platforms we already run, and set the standards for how the team version-controls, reviews, tests, releases, and maintains everything from DSP designs to firmware to internal services. Responsibilities Design, build, and ship internal tools and integrations that monitor, test, and manage AV and collaboration systems across every office Develop fleet health monitoring and automated room testing that catch failures before users report them and open actionable tickets automatically Build integrations across device and platform APIs — Q-SYS (including plugins and Lua control scripts), Logitech Sync, Google Meet hardware, digital signage; and IT systems such as Jira, Slack, PagerDuty, and Google Workspace Automate repetitive operational work: provisioning, configuration baselines, drift detection, certificate renewal, and asset data — with code others can read, test, and maintain Own administration and lifecycle of the software platforms behind the AV estate, including firmware and software release management with staged rollouts and rollback plans Operate internal tools as production services, with monitoring, alerting, service metrics, and runbooks, on team-owned repositories and service identities so nothing depends on one person Establish the team's engineering practices: source control, code review, testing, CI/CD, and release — and bring AV configuration under version control, with standards set for integrator deliverables and acceptance verified before changes land Enable AV Engineers, Operations Specialists, and Production staff to contribute safely through templates, guardrails, and review paths built for non-developers Partner with Security, IT Engineering, and Network on least-privilege API access, secrets management, and integration with enterprise standards Gather requirements from AV Operations, Events, and AV Engineering, prioritize the tooling roadmap by operational impact, and decide where to build, buy, or push a vendor to close a gap You May Be a Good Fit If You: Have 10+ years of experience across AV, collaboration, or IT systems engineering, including 4+ years writing, shipping, and maintaining production code that other people depend on Are fluent in at least one general-purpose language: Python, Lua, Go, TypeScript, or similar — and comfortable with shell scripting Practice engineering fundamentals by default: Git-based workflows, code review, automated testing, CI/CD, logging, and observability Understand how AV and collaboration systems work: DSP and control, conferencing platforms, room devices, signal flow, AV-over-IP — well enough to build reliable tools on top of them Have built integrations against REST APIs, webhooks, and vendor SDKs, and know how to handle auth, rate limits, pagination, retries, and partial failure Have owned a tool or service in production over time, not just written it, including fixing it when it broke and handing it off cleanly Have defined engineering practices for a team, especially one where most contributors aren't developers Turn operational pain into well-scoped tooling, and can say no to work that shouldn't be built Thrive in a fast-moving environment where standards are being written rather than inherited Strong Candidates May Also Have Experience With: Q-SYS Lua scripting and plugin development Logitech Sync and CollabOS, and Google Meet hardware administration through the Google Admin console and Admin SDK Google Workspace APIs, GCP (Cloud Run, Cloud Scheduler, Pub/Sub, Secret Manager), and Terraform or other infrastructure-as-code Jira, Slack, and PagerDuty APIs and automation Crestron, Extron, or other control system programming Dante, AES67, NDI, and network fundamentals for AV-over-IP, including multicast, VLANs, and QoS Building with LLMs — agentic automation, LLM-assisted internal tools, or MCP integrations Monitoring and alerting stacks such as Prometheus and Grafana, and containerized deployment Q-SYS Level 2, CTS, or equivalent demonstrated capability Role Specific Location Policy: This role will be based in our San Francisco or New York City office This role is expected to be in office 50% of the time Up to 4 weeks of domestic and international travel annually for platform rollouts, new-office readiness, and on-site testing Business-hours escalation coverage for the tools and platforms this role owns Deadline to apply: None. Applications will be received on a rolling basis. 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 — $325,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Remote, United States GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role This is an overnight shift role with working hours of 12:00am - 7:00am EST. As a Support Engineer on our U.S. Government Support Engineering team, you operate at the critical intersection of Support and Engineering, enabling public sector organizations and United States government agencies to run GitLab in some of the most secure and constrained environments in the world. Your work directly supports national security missions, including customers operating within FedRAMP-authorized, air-gapped, and classified or semi-classified environments where traditional troubleshooting approaches are not always possible. In this role, you will engage deeply with GitLab through the command line; analyzing logs, system behavior, and application performance with strong Linux fundamentals and an understanding of how GitLab’s components interact end-to-end. You may partner with Product and Engineering to reproduce and document edge-case defects using sanitized or redacted data, or contribute directly to the product by opening merge requests that resolve customer-impacting issues. Beyond resolving individual cases, you’ll help strengthen GitLab by improving documentation, support workflows, and institutional knowledge, ensuring that lessons learned in highly regulated environments benefit the broader platform; all while working in a fully remote, highly collaborative environment built on trust, autonomy, and shared ownership. Some examples of our projects: A tool to quickly analyze strace output A solution to capture the state of a customer’s server for easier troubleshooting A functionality to ChatOps to make it easier to identify user accounts on GitLab.com What you’ll do Collaborate closely with public sector customers, including U.S. government agencies, the Department of War, and Intelligence Community organizations, to diagnose, troubleshoot, and resolve complex issues in GitLab deployments operating under strict security, compliance, and operational constraints. Perform deep, hands-on technical investigations using Linux command-line tooling and GitLab internals to analyze logs, system performance, and application behavior, often with limited telemetry or incomplete datasets. You will trace issues across infrastructure, application, and code paths to identify root causes, recommend mitigations, and drive durable fixes; frequently partnering with GitLab Product, Development, and Infrastructure teams to ensure outcomes that meet both mission and platform reliability requirements. Create and update clear, reusable documentation and support content based on real customer interactions to improve self-service and reduce repeated issues. Participate in regular pairing sessions with other Support Engineers and act as a stable counterpart to cross-functional teams to share knowledge and improve support processes. Contribute to building and refining internal support tools and workflows, including automation and troubleshooting utilities, to make diagnosing and resolving issues more efficient. Participate in weekday and weekend on-call rotations, providing timely emergency support for self-managed customers operating in restricted and compliance-focused environments. Support the growth of the team by reviewing applications and assessments, and participating in interviews and hiring decision discussions. What you’ll bring Due to government security requirements, you must be a United States Citizen (defined as any individual who is a citizen of the United States by law, birth, or naturalization) to be eligible for this position Experience managing customer-facing cases across the full support lifecycle, from initial inquiry and triage through reproduction, escalation, and resolution Strong Linux systems administration knowledge, with the ability to troubleshoot performance and configuration issues in complex environments Familiarity with AI/LLM-powered developer tooling and gateway architectures (for example GitLab Duo, AI gateway configurations, or similar model-serving/proxy layers), including how they're deployed and troubleshot in self-managed or air-gapped environments Ability to use scripting languages (preferably Ruby or Bash) and work within model-view-controller frameworks (preferably Ruby on Rails) to investigate and resolve issues Practical experience with Git and continuous integration/continuous delivery (CI/CD) workflows Skill in creating and maintaining clear, accurate support content and technical documentation Ability to explain complex technical topics in a clear, empathetic way to customers and coworkers with varying technical backgrounds Familiarity with DevOps practices and container-orchestration technologies such as OpenShift and Kubernetes Understanding of common information security and compliance concepts and controls (for example DISA STIG and SELinux), and eligibility to work on United States federal customer environments as required for this role About the team At GitLab Federal, LLC, the U.S. Government Support Engineering team partners with public sector organizations and United States government agencies to support GitLab in highly secure, mission-critical environments. Our work directly enables national security outcomes, supporting customers across the Intelligence Community, Department of War, and other federal, state, and local government entities operating within regulated and restricted boundaries. We are embedded within Support Engineering and work closely with Product, Development, Infrastructure, Customer Success, and Sales to solve complex, high-impact technical challenges. Our team frequently dives into code, logs, and architectural behavior to understand not just what is failing, but why; especially in environments with sanitized data, limited visibility, and strict compliance controls such as FedRAMP. We collaborate asynchronously across time zones and through structured pairing and discussion to share knowledge, strengthen solutions, and continuously improve how GitLab supports secure, air-gapped, and regulated deployments, while upholding GitLab's standards for rigor, transparency, and technical excellence. The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity . Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary. United States Salary Range $95,200 — $160,800 USD How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Location: Costa Mesa, United States 📍 Location: Costa Mesa, California, USA 📄 Employment Type: Full-Time 🏢 Work Arrangement: Remote Opportunity 🚀 Start Date: Immediate 💰 Compensation: TBD -- based on skills and experience Important Screening Requirements Must be a U.S. Citizen or U.S. Permanent Resident (Green Card holder) . Must be eligible to obtain and maintain a U.S. Department of Defense (DoD) Secret Security Clearance . Candidates should have a strong interest in supporting U.S. defense and national security programs. About the Role We are seeking an Aerodynamics Engineer to support the design, analysis, and testing of next-generation air vehicle platforms. In this role, you will perform aerodynamic modeling, aircraft performance analysis, and wind tunnel and flight testing to optimize vehicle performance from concept through flight validation. The ideal candidate has a strong foundation in aerodynamics, computational analysis, and flight testing, with experience developing innovative solutions for advanced aerospace systems. Key Responsibilities Perform aerodynamic analyses to support the design and optimization of advanced air vehicle platforms. Develop aircraft performance predictions using analytical methods and Computational Fluid Dynamics (CFD). Conduct aerodynamic evaluations using hand calculations, vortex lattice methods, panel methods, and CFD. Support wind tunnel and flight test campaigns, including test planning, data acquisition, data analysis, and project coordination. Develop and maintain engineering analysis tools using MATLAB , Python , or similar programming languages. Analyze aerodynamic performance data and recommend design improvements. Prepare technical documentation, including specifications, test procedures, engineering reports, and design analyses. Collaborate with multidisciplinary engineering teams throughout the design, integration, testing, and development lifecycle. Required Qualifications Bachelor's degree or higher in Aerospace Engineering . 3+ years of experience as an individual contributor in an aerospace engineering role. Experience with aircraft performance analysis and aerodynamic prediction. Strong experience with: Hand calculations Vortex lattice methods Panel methods Computational Fluid Dynamics (CFD) Experience supporting wind tunnel and flight testing, including data acquisition, analysis, and project coordination. Strong programming skills using MATLAB , Python , or similar high-level programming languages within a version-controlled development environment. Experience preparing technical documentation, including specifications, test procedures, and engineering reports. Strong analytical, communication, and problem-solving skills. Preferred Qualifications Experience with StarCCM+ . Experience with ice accretion or aircraft icing analysis . Experience using Missile DATCOM , OTIS , or DYMOS . Experience working with transonic and supersonic flow regimes. Active U.S. DoD Secret Security Clearance .

Location: Torrance, United States As a Senior Thermal Engineer, you will own the thermal engineering methodology across our missile systems. You will define thermal architectures, develop high-fidelity thermal models, and use first-principles analysis, simulation, and hardware testing to ensure our vehicles perform reliably in some of the most demanding thermal environments imaginable. You'll work across aerodynamics, structures, electrical, propulsion, systems, manufacturing, and test engineering to develop integrated thermal solutions from concept through flight. Your work will span transient mission analysis, electronic unit thermal management, specialized ground testing, and flight-test instrumentation and data analysis. This role is ideal for an engineer who enjoys moving fluidly between analytical models, real hardware, and flight data—and who wants to directly influence vehicle architecture and design decisions from day one. RESPONSIBILITIES Thermal design and analysis of unmanned air vehicles at conceptual, preliminary, and detailed design maturities Define thermal system architectures and material selection strategies for vehicles operating in demanding flight environments Thermal analysis of flight vehicles using standard thermal analysis codes and engineering/hand methods Transient thermal analysis for time-varying mission profiles Ground test planning, execution, and analysis in specialized thermal test facilities Flight test planning, execution, and post-flight thermal data analysis Use first principles and MATLAB/Python models to inform design decisions Collaborate with aerodynamics, structures, electrical, and systems engineers on integrated solutions Present thermal status at formal reviews internally and with customers Prepare formal documentation (specifications, test procedures, technical reports) BASIC QUALIFICATIONS BS or higher degree in Aerospace Engineering, Mechanical Engineering, or Applied Physics In-depth understanding of heat transfer, fluid dynamics, and high-temperature environments 5+ years of experience in aerospace thermal engineering on rocket, spacecraft, aircraft, or missile programs Experience with electronic unit thermal analysis and control to component junctions Ground testing experience with thermal instrumentation and data acquisition Strong programming skills in MATLAB, Python, or similar for tool development Experience preparing formal documentation CAD experience with SolidWorks or Siemens NX Ability to obtain and hold a U.S. security clearance PREFERRED SKILLS AND EXPERIENCE MS or higher degree in Aerospace Engineering, Mechanical Engineering, or Applied Physics with focus on heat transfer and fluid dynamics Experience with material ablation response modeling in extreme thermal environments Knowledge of material degradation and surface phenomena under demanding conditions Experience with specialized thermal ground test facilities Flight test experience including instrumentation design and post-flight analysis Experience with Thermal Desktop/SINDA Experience with manufacturing and assembly of thermal systems Active U.S. security clearance

Location: Torrance, United States As a Flight Software Engineer, you will play a key role in designing, developing, and testing flight software that powers these advanced systems. You'll work closely with multidisciplinary teams to deliver high-performance, real-time embedded software that operates reliably under extreme conditions. This is a hands-on role for an engineer who thrives on solving hard problems, building robust systems from the ground up, and moving fast without compromising quality. RESPONSIBILITIES Design, develop, and test real-time embedded flight software for missile systems Collaborate with GNC engineers, systems engineers, and hardware teams to ensure seamless integration Implement and maintain software for high-reliability, safety-critical environments Support simulation, hardware-in-the-loop (HIL), and flight testing to validate performance Participate in root cause analysis and debugging across software and system interfaces Follow rigorous software lifecycle practices, including configuration management and verification Contribute to defining and improving development processes, tools, and standards Stay informed on emerging technologies and best practices relevant to flight software and real-time systems BASIC QUALIFICATIONS Bachelors or Master's degree in Computer Science, Electrical Engineering, Aerospace Engineering, or related field Experience: Open to candidates across all experience levels, from early-career engineers to senior technical contributors. Professional experience, internships, research, open-source contributions, or other hands-on technical work will be considered. Proficiency in C/C++ and experience with real-time operating systems (RTOS) Familiarity with hardware/software integration and real-time system constraints Experience working with GNC systems or similar control-intensive applications Strong understanding of testing practices including simulation and HIL environments Excellent problem-solving skills and attention to detail Ability to work effectively in a fast-paced, multidisciplinary team environment Active or eligible for a Top-Secret security clearance PREFERRED SKILSS AND EXPERIENCE Experience developing flight software for missile, launch vehicle, or spacecraft systems Exposure to FPGA-based systems and embedded software optimization techniques Knowledge of secure software development and cybersecurity best practices Familiarity with agile or iterative development methodologies Hands-on experience with software verification, validation, and integration in defense environments

Location: Torrance, United States As an Embedded Software Engineer you will be at the forefront of creating, developing, and testing the software that drives and simulates our flight systems. Your role involves close collaboration with fellow Client's engineers across software and hardware domains to implement and debug software across the entire technology stack from applications and operating systems to networking, firmware, and simulation, ensuring complete mission success. We seek intelligent, collaborative, an driven engineers of all levels who are passionate about problem-solving and eager to contribute to an extraordinary mission. If you thrive in challenging environments and are excited by the prospect of making a tangible impact, we want to hear from you. RESPONSIBILITIES Autonomous Systems: Design and implement reliable autonomous software systems, along with the simulations required to validate their precision and dependability. Comprehensive Software Development: Manage the entire software development process, from initial design, prototyping, and testing, to final integration and deployment for mission-critical applications. System Testing: Evaluate test outcomes and troubleshoot issues necessitating detailed investigations, including hands-on integration and debugging of hardware/software systems. Develop tools to facilitate testing across various environments: virtualized hardware, real hardware-in-the-loop, and vehicle-in-the-loop testing. COLLABORATION Partner with multidisciplinary engineering teams to brainstorm, design, and develop next-generation hypersonic missile capabilities, including advanced guidance algorithms and integrated seeker image processing. Project Leadership: Lead major projects that propel Client's mission forward, ensuring alignment with overall objectives and occasionally traveling to launch and customer sites for testing and operations. Enhance Standards and Practices: Continuously improve software standards and best practices within the software team, fostering an environment of ongoing improvement and innovation. BASIC QUALIFICATIONS Educational Background: Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related STEM field, or equivalent practical experience. Experience: Open to candidates across all experience levels, from early-career engineers to senior technical contributors. Professional experience, internships, research, open-source contributions, or other hands-on technical work will be considered. Technical Proficiency: Experience with systems programming in languages such as C++, Python, or Rust, and familiarity with Linux-based development environments. Experience may include work with low-level systems such as Linux kernel development, hardware interfaces, or driver development (e.g., UART, I2C, or similar peripherals). PREFERRED SKILLS AND EXPERIENCE Programming Expertise : Proven proficiency in C++, Rust, or similar systems programming languages. System Experience: Practical experience with real-time embedded systems or distributed computing environments in Linux-based systems, including developing high-quality Linux-based C++ software for common processors and microcontrollers (e.g., PowerPC, x86, ARM). TECHNICAL SKILLS Proficiency in Linux, including Bash shell scripting, network troubleshooting, and performance analysis (strace, ftrace, etc.), with strong skills in debugging, performance optimization, and unit testing. System Knowledge: Comprehensive understanding of computer architecture and networking protocols (TCP, UDP, etc.). Problem-Solving: Innovative problem-solving approach using first-principles thinking, including developing prototypes to validate key design concepts and quantify technical constraints.