Senior Architect Jobs

The Silicon Co-Design Group (SCG) sits at the crossroads of architecture, design, marketing, operations, and productization. Our work spans early architecture through final product delivery across Datacenter, Gaming, Robotics, Automotive, and Embedded markets. We work closely across functions to deliver chips that change what is possible. NVIDIA’s Silicon Co-Design Group is hiring a Chip Lead to serve as the technical lead for one of our most consequential silicon programs! This is not project management, and it is not a senior IC role. You are the person the program partners with on its hardest technical questions — when the right answer is not obvious, and when leadership needs a single technical perspective to align on direction. You are accountable for the technical integrity of the chip end-to-end. You guide co-design feature integration, help resolve the toughest multi-functional bugs, and serve as the project-specific custodian of the qualification playbook. The program runs cleaner because you are on it! What you’ll be doing: You will partner across design, validation, software, and manufacturing to keep the program’s technical narrative clear and on track. Day to day, you will: Serve as the single technical point of contact for multi-functional decisions, issues, and trade-offs. Co-lead program-level feature integration from chip to system, surfacing inter-function dependencies and guiding them to resolution. Help resolve the program’s hardest multi-functional bugs by translating ambiguous, multi-team symptoms into root-cause closure on areas such as HBM, power and thermal, high-speed I/O, and packaging. Steward the qualification playbook. When the playbook does not fit a situation, guide the mitigations and capture the lessons as reusable methodology for other SSG programs. Shape the program’s technical narrative by surfacing key risks, trade

Become a part of our caring community The Principal Storage Engineer is a senior technical leader responsible for defining and advancing the enterprise storage architecture and long-term data infrastructure strategy. This role establishes standards, develops five-year technology roadmaps, and designs secure, resilient, scalable, and cost-effective storage platforms for business-critical, analytics, and artificial intelligence workloads. The engineer serves as the organization’s storage subject-matter expert and partners with infrastructure, cloud, security, data, application, architecture, finance, and vendor teams to translate business requirements into sustainable technology capabilities. The Principal Storage Engineer is a senior technical leader responsible for defining and advancing the enterprise storage architecture and long-term data infrastructure strategy. This role establishes standards, develops five-year technology roadmaps, and designs secure, resilient, scalable, and cost-effective storage platforms for business-critical, analytics, and artificial intelligence workloads. The engineer serves as the organization’s storage subject-matter expert and partners with infrastructure, cloud, security, data, application, architecture, finance, and vendor teams to translate business requirements into sustainable technology capabilities. Key Responsibilities Define the enterprise storage vision, reference architecture, engineering standards, and five-year roadmap across block, file, object, software-defined, and hybrid storage services. Lead architecture decisions for on-premises AI infrastructure, including high-throughput and low-latency storage fo

The Applications Development Technology Lead Analyst is a senior level position responsible for establishing and implementing new or revised application systems and programs in coordination with the Technology team. The overall objective of this role is to lead applications systems analysis and programming activities. Responsibilities: Partner with multiple management teams to ensure appropriate integration of functions to meet goals as well as identify and define necessary system enhancements to deploy new products and process improvements Resolve variety of high impact problems/projects through in-depth evaluation of complex business processes, system processes, and industry standards Provide expertise in area and advanced knowledge of applications programming and ensure application design adheres to the overall architecture blueprint Utilize advanced knowledge of system flow and develop standards for coding, testing, debugging, and implementation Develop comprehensive knowledge of how areas of business, such as architecture and infrastructure, integrate to accomplish business goals Provide in-depth analysis with interpretive thinking to define issues and develop innovative solutions Serve as advisor or coach to mid-level developers and analysts, allocating work as necessary Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency. Recommended Qualifications: 6+ years of relevant experience in Apps Development or systems analysis role Extensive exp

Role Purpose We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Experience: 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow. Cloud Native: Hands-on ex

We are investing in agentic AI and need a Senior AI Engineer to lead the design and delivery of these systems. This is a foundational hire: you will own both the agent-facing workstreams — pipelines, orchestration, conversational interfaces — and the underlying context layer that makes them reliable, including memory management, knowledge graph integration, and retrieval infrastructure. You will work closely with data engineers, project leads, and client stakeholders, and play a key role in shaping how Lynx builds and ships AI solutions at scale. What This Involves: Lead the architecture and delivery of agentic AI systems end-to-end: agents, orchestration, tool use, and multi-step reasoning workflows. Own the context layer: design and implement memory architectures (episodic, semantic, working memory) and integrate GraphRAG and knowledge graph retrieval into agentic pipelines. Build robust RAG systems — including vector retrieval, graph traversal, and hybrid search — and ensure retrieval quality through evaluation frameworks. Translate client requirements into technical designs, presenting approaches and trade-offs to both technical and non-technical stakeholders. Define standards and reusable patterns for agentic AI development that other engineers at Lynx can build on. Set up observability, evaluation, and monitoring pipelines to ensure AI systems perform correctly in production. Requirements: 5–8 years of software or ML engineering experience, with at least 2–3 years building LLM-based or agentic AI systems in production. Deep hands-on experience with agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, or similar) and LLM APIs (OpenAI, Anthropic, etc.). Strong understanding of agent design patterns: ReAct, planning loops, tool use, multi-agent coordination, and memory architectures. Practical experience with GraphRAG or knowledge graph-based retrieval (e.g., Neo4j, Microsoft GraphRAG) and vector databases (Pinecone, Weaviate, Qdrant, etc.). Proficiency in

Coder is looking for an experienced Senior Technical Program Manager to lead and shape our most complex, high-stakes engineering programs. You'll operate with a high degree of autonomy, setting the standard for program execution across the organization and acting as a trusted partner to senior engineering and product leadership. The role sits at the intersection of strategy and execution: you'll own the definition, cross-functional planning, and delivery of initiatives that span multiple teams and systems, and give leadership the visibility they need to steer. What you'll do here Own engineering programs end to end, from definition through delivery and iteration, across teams that don't report to one another Own scope, milestones, timelines, dependencies, and risk, driving trade-offs to a written decision and holding accountability for delivery Work hands-on with engineers and product managers, close enough to the work to influence architectural and interface decisions Serve as the central coordination point across engineering, product, design, security, and go-to-market Run the operating cadence and deliver crisp updates on program health, risks, and trade-offs to technical, executive, and customer stakeholders Improve engineering productivity through better tooling and light-weight process, including AI-native tooling and our own product What we're looking for 7+ years in technical program management, including complex multi-team programs where no single team owned the whole outcome Deep technical intuition—you can read a design doc, ask the question that exposes the unstated assumption, and argue sequencing with a staff engineer A track record of delivering at scale, including ownership of a dated external commitment such as a GA, a launch, or a regulated enterprise deadline Strong organizational influence; you set the agenda with minimal oversight and move teams that don't report to you, without being slowed down by ambiguity or competing priorities Excellent ve

MongoDB’s Storage Layer Services (SLS) team is re-architecting the MongoDB cloud storage layer and sits at the heart of our next-generation cloud storage architecture. This relatively new team is building performant, multi-tenant distributed storage services that both enhance today’s Atlas storage stack and enable more customer workloads to run more efficiently. As the Site Reliability Engineering Manager for SLS, you will partner with the teams building these storage services to define SLOs, shape capacity plans, and ensure the reliability, durability, and operational safety of the storage layer that underpins Atlas. You’ll help grow and lead a small, senior team of SREs as founding members of this organization, playing a crucial role in executing on a multi-year roadmap for MongoDB’s cloud storage architecture. We are looking to speak to candidates who are based in Dublin for our hybrid working model. Responsibilities Build and lead a team of 6-8 engineers, fostering a positive culture, handling career growth and performance conversations, and proactively removing blockers Define and drive a clear technical vision and comprehensive roadmap for our multi-tenant distributed storage systems, balancing long-term strategic infrastructure goals with immediate engineering needs Contribute through hands-on technical work, such as leading architectural design reviews, reviewing PRs, and stepping in to guide the team through complex operational challenges Act as the primary liaison for the Storage Layer Services SRE team, collaborating closely with other engineering leaders to ensure platform alignment and manage stakeholder expectations You may be a good fit if you Have 10+ years of experience working on software and operating distributed systems, with 2+ years managing engineering teams Possess a customer-focused mindset, treating internal developers as your primary users Value efficiency in processes and operations, and have a track record of optimizing team workflows Pr

MongoDB’s Storage Layer Services (SLS) team is re-architecting the MongoDB cloud storage layer and sits at the heart of our next-generation cloud storage architecture. This relatively new team is building performant, multi-tenant distributed storage services that both enhance today’s Atlas storage stack and enable more customer workloads to run more efficiently. As the Site Reliability Engineering Manager for SLS, you will partner with the teams building these storage services to define SLOs, shape capacity plans, and ensure the reliability, durability, and operational safety of the storage layer that underpins Atlas. You’ll help grow and lead a small, senior team of SREs as founding members of this organization, playing a crucial role in executing on a multi-year roadmap for MongoDB’s cloud storage architecture. We are looking to speak to candidates who are based in New York City for our hybrid working model. Responsibilities Build and lead a team of 6-8 engineers, fostering a positive culture, handling career growth and performance conversations, and proactively removing blockers Define and drive a clear technical vision and comprehensive roadmap for our multi-tenant distributed storage systems, balancing long-term strategic infrastructure goals with immediate engineering needs Contribute through hands-on technical work, such as leading architectural design reviews, reviewing PRs, and stepping in to guide the team through complex operational challenges Act as the primary liaison for the Storage Layer Services SRE team, collaborating closely with other engineering leaders to ensure platform alignment and manage stakeholder expectations You may be a good fit if you Have 10+ years of experience working on software and operating distributed systems, with 2+ years managing engineering teams Possess a customer-focused mindset, treating internal developers as your primary users Value efficiency in processes and operations, and have a track record of optimizing team workf

MongoDB’s Storage Layer Services (SLS) team is re-architecting the MongoDB cloud storage layer and sits at the heart of our next-generation cloud storage architecture. This relatively new team is building performant, multi-tenant distributed storage services that both enhance today’s Atlas storage stack and enable more customer workloads to run more efficiently. As the Site Reliability Engineering Manager for SLS, you will partner with the teams building these storage services to define SLOs, shape capacity plans, and ensure the reliability, durability, and operational safety of the storage layer that underpins Atlas. You’ll help grow and lead a small, senior team of SREs as founding members of this organization, playing a crucial role in executing on a multi-year roadmap for MongoDB’s cloud storage architecture. We are looking to speak to candidates who are based in Cork for our hybrid working model. Responsibilities Build and lead a team of 6-8 engineers, fostering a positive culture, handling career growth and performance conversations, and proactively removing blockers Define and drive a clear technical vision and comprehensive roadmap for our multi-tenant distributed storage systems, balancing long-term strategic infrastructure goals with immediate engineering needs Contribute through hands-on technical work, such as leading architectural design reviews, reviewing PRs, and stepping in to guide the team through complex operational challenges Act as the primary liaison for the Storage Layer Services SRE team, collaborating closely with other engineering leaders to ensure platform alignment and manage stakeholder expectations You may be a good fit if you Have 10+ years of experience working on software and operating distributed systems, with 2+ years managing engineering teams Possess a customer-focused mindset, treating internal developers as your primary users Value efficiency in processes and operations, and have a track record of optimizing team workflows Pref

Machine Learning Engineer IV – (Computer Vision) We’re looking for a Staff/Senior Machine Learning Engineer with deep expertise in computer vision and biometrics to lead the design and scaling of face recognition systems in production. You’ll build and train models, and own ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process. What You’ll Do Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team. What We’re Looking For Strong industry experience in Machine Learning, dedicated to Biometrics or Face Analysis. Deep expertise in computer vision and biometrics, especially face recognition. Fairness & Ethics: You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact. Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code. Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow. Cloud Native: Hands-on exper

We’re looking for an experienced engineering leader to lead and scale Asana’s flagship AI Teammates Pillar within our Core/AI Organization. Reporting directly to the Head of Engineering, AI & AWM, you will oversee an engineering area encompassing four key teams: AI Teammates Platform (AITP), AI Teammates Experience (AITX), AI Chat Experience, and Agent Orchestration. In this role, you will lead engineering managers and senior technical leaders to drive the end-to-end strategy, execution, and architecture that define how humans and AI collaborate in Asana, turning frontier LLM capabilities into trusted, reliable, high-value product workflows for enterprise customers worldwide. This role is based in our San Francisco office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements. What you’ll achieve Drive Strategy & Execution Across the AI Teammates Pillar: Lead, set direction, and align goals/metrics across four core engineering teams (Platform, Experience, Chat, and Agent Orchestration) to execute on Asana’s flagship AI vision while balancing business, customer, and technical needs. Architect Enterprise-Grade AI Platforms & Systems: Own the end-to-end technical strategy across model rollouts, eval infrastructure, tool orchestration, Model Context Protocol (MCP), and proactivity engines—enabling Asana to operate reliable agentic systems for top-tier enterprise customers. Define Next-Generation Human-AI Collaboration: Lead product and platform engineering teams delivering full-screen and docked chat surfaces, discovery/onboarding flows, in-product explainability, human-in-the-loop workflows, and ROI/governance experiences for admini

We’re looking for an experienced engineering leader to lead and scale Asana’s flagship AI Teammates Pillar within our Core/AI Organization. Reporting directly to the Head of Engineering, AI & AWM, you will oversee an engineering area encompassing four key teams: AI Teammates Platform (AITP), AI Teammates Experience (AITX), AI Chat Experience, and Agent Orchestration. In this role, you will lead engineering managers and senior technical leaders to drive the end-to-end strategy, execution, and architecture that define how humans and AI collaborate in Asana, turning frontier LLM capabilities into trusted, reliable, high-value product workflows for enterprise customers worldwide. This role is based in our San Francisco office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday. Most Asanas have the option to work from home on Wednesdays. Working from home on Fridays depends on the type of work you do and the teams with which you partner. If you're interviewing for this role, your recruiter will share more about the in-office requirements. What you’ll achieve Drive Strategy & Execution Across the AI Teammates Pillar: Lead, set direction, and align goals/metrics across four core engineering teams (Platform, Experience, Chat, and Agent Orchestration) to execute on Asana’s flagship AI vision while balancing business, customer, and technical needs. Architect Enterprise-Grade AI Platforms & Systems: Own the end-to-end technical strategy across model rollouts, eval infrastructure, tool orchestration, Model Context Protocol (MCP), and proactivity engines—enabling Asana to operate reliable agentic systems for top-tier enterprise customers. Define Next-Generation Human-AI Collaboration: Lead product and platform engineering teams delivering full-screen and docked chat surfaces, discovery/onboarding flows, in-product explainability, human-in-the-loop workflows, and ROI/governance experiences for admini

Lead Forward Deployed Engineers (FDE) are the most senior technical leaders in the FDE organization. They own the overall technical strategy for the FDE domain, not just individual engagements, and are accountable for how the entire function operates, scales, and improves over time. They set direction across multiple concurrent engagements, teams, and quarters, and are the technical voice the organization relies on when stakes, ambiguity, or cross-functional complexity are highest. This role sits at the intersection of technical depth, organizational strategy, and people leadership, setting the technical bar for the function, shaping how Product and Engineering prioritize based on field reality, and developing the next generation of Staff-level FDEs. We are looking to speak to candidates who are based in New York City for our hybrid working model. Responsibilities Own the FDE organization's overall engagement strategy, serving as the primary technical escalation point across the portfolio for the highest-risk, highest-visibility, and most ambiguous situations Act as the trusted technical counterpart to customer executives and internal VP/executive stakeholders on strategic bets, major risks, and cross-portfolio tradeoffs Set the technical architecture standards and design principles that govern how the FDE domain builds customer-facing solutions and platform capabilities, turning organization-level ambiguity into coherent multi-quarter technical strategy Stay hands-on where it matters most, writing and reviewing code and personally unblocking the hardest technical failures to keep the organization's technical bar grounded in reality Own the translation of field-wide patterns into the FDE domain's long-term technical vision, representing that vision to Product and Engineering executive leadership and defending multi-quarter roadmap tradeoffs Own the technical growth and career development of FDEs through mentorship, coaching, and calibration of the org's technical ba

About Ema Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs. We are backed by industry leading investors including Accel, Naspers/Prosus, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production — we ship real systems that run real business processes at scale. Role Overview & Key Responsibilities This is a high-leverage leadership role that spans architecture, execution, and org-building, and will shape the direction of our AI / ML initiatives at Ema. We are seeking an AI / ML technical leader who can take a vision and build it. As a Principal ML Engineer at Ema, you will be a senior technical leader responsible for shaping the machine learning roadmap, architecting large-scale ML systems, driving innovation, and ensuring our mixture of expert models (LLM + SLM + Custom Model) is accurate and performant at scale. You will collaborate across teams (research, product, infra, data, etc.), mentor senior engineers, and influence strategy and execution at company-wide levels. Responsibilities Lead the technical direction of GenAI and agentic ML systems that power enterprise-grade AI agents — spanning reasoning, retrieval, tool use, and integrations across various SaaS products. Architect, design, and implement scalable production pipelines for model training, fine-tuning, retrieval (RAG), agent orchestration, and evaluation — ensuring robustness, latency efficiency, and continuous learning. Define and own the multi-year ML roadmap for GenA

JLL empowers you to shape a brighter way . Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology for our clients. We are committed to hiring the best, most talented people and empowering them to thrive, grow meaningful careers and to find a place where they belong. Whether you’ve got deep experience in commercial real estate, skilled trades or technology, or you’re looking to apply your relevant experience to a new industry, join our team as we help shape a brighter way forward. Key Responsibilities: Provide support in producing tender documentation packages including plans, details, schedules and specifications Take site measurements or survey Client's existing furniture inventory, if required Provide support in producing materials and finishes scheme and documentation Prepare finishes, furniture and door hardware schedules Upkeep and maintenance of library facility Assist Interior Designers and Senior Interior Designers in producing quality visuals for presentation boards Requirements: Currently pursuing a Diploma in Interior Design, Architecture or related field Proficiency in AutoCAD and SketchUp Familiarity with Revit is a plus point