Ai Soc Solutions Architect Jobs

Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. Our IP delivery timelines are set as much by flow maturity as by design work. This role develops, deploys, and owns the RTL-to-GDSII methodology the IP physical design team runs on, so a new block, node, or customer variant starts from a working flow instead of a cold start. This role is hybrid, based out of Toronto, ON; Austin, TX, or Belgrade, Serbia. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Who You Are A physical design or CAD methodology engineer who has built flows that production teams depend on daily. Automation-minded, happiest when you are removing manual steps and making PPA exploration repeatable. Building with AI as part of how you develop flows, and opinionated about where LLMs and ML-driven optimization genuinely help versus where they do not. An effective partner to design teams and EDA vendors, and a clear writer who documents flows well enough that others can run them without you. What We Need An Engineer with 5+ years developing and supporting physical design methodology or CAD flows in production use. Expertise with industry-standard tools (FusionCompiler/ICC2, Innovus/Genus, PrimeTime, RedHawk) and scripting languages (T

MeltPlan is building the “planning engine” for the $14 Tn construction industry, an AI system designed specifically to optimize decisions before construction begins. While design software optimizes use and aesthetics and construction software optimizes execution and control, MeltPlan is building the missing layer - software that optimizes decisions and tradeoffs upstream, before scope is locked, procurement begins, and change orders become inevitable. MeltPlan’s long-term goal is to help teams make construction “boring” by making planning more intense: surfacing constraints and tradeoffs early, aligning stakeholders before plans are frozen, and reducing the need for late-stage redlines, rework, and change orders. MeltPlan is founded by operators who have built at scale. Kanav previously co-founded Innovaccer, a $3Bn healthtech company focused on making US healthcare more affordable and accessible. He’s now applying that systems-level thinking to construction.He’s joined by Tanmaya Kala, former Project Executive at DPR Construction, who led large commercial, healthcare, and life sciences projects. We combine deep tech scale with real construction execution. What This Role Really is : We are seeking a detail-oriented and technically strong AI QA Engineer to ensure the quality, reliability, and performance of Large Language Model (LLM)-based systems. In this role, you will be responsible for designing and executing test strategies, validating model outputs, and building evaluation frameworks to enhance the accuracy, safety, and overall performance of AI-driven applications.We would particularly value candidates who have hands-on experience in developing evaluation frameworks (evals) for AI systems, along with strong expertise in comprehensive system testing and quality assurance practices.You are responsible for making MeltPlan work in the real world. What You'll Do: Design, develop, and execute evaluation frameworks (Evals) for Large Language Models (LLMs) and AI syst

MeltPlan | Planning Engine for the Built Environment MeltPlan is building the “planning engine” for the $14 Tn construction industry, an AI system designed specifically to optimize decisions before construction begins. While design software optimizes use and aesthetics and construction software optimizes execution and control, MeltPlan is building the missing layer - software that optimizes decisions and tradeoffs upstream, before scope is locked, procurement begins, and change orders become inevitable. MeltPlan’s long-term goal is to help teams make construction “boring” by making planning more intense: surfacing constraints and tradeoffs early, aligning stakeholders before plans are frozen, and reducing the need for late-stage redlines, rework, and change orders. MeltPlan is founded by operators who have built at scale. Kanav previously co-founded Innovaccer, a $3Bn healthtech company focused on making US healthcare more affordable and accessible. He’s now applying that systems-level thinking to construction.He’s joined by Tanmaya Kala, former Project Executive at DPR Construction, who led large commercial, healthcare, and life sciences projects. We combine deep tech scale with real construction execution. What This Role Really Is We are looking for an AI Research Scientist – Computer Vision to enhance and manage the PlanGraph model, which transforms 2D drawings into structured graphical representations of building elements. The role involves solving downstream business use cases such as quantity takeoff, code compliance, value engineering, and constructability analysis.We are specifically looking for hands-on researchers with experience in solving real-world Computer Vision problems and building custom vision models, VLMs, or VLLMs for production-grade applications. What You’ll Do Build and optimize custom Computer Vision models, VLMs, and VLLMs for construction intelligence workflows. Solve downstream business use cases including quantity takeoff, code complianc

About Dialpad Dialpad is the AI platform for customer experience, built to resolve customer problems in real time across voice and digital. Our AI agents learn from your best human agents and improve with every interaction, helping organizations understand their customers, deliver better experiences, increase operational efficiencies, and build a lasting competitive advantage. Unlike legacy systems built to route and answer, or standalone agentic bot vendors built to deflect, Dialpad was built to resolve. Our AI agents and human agents operate on a single platform with shared context, allowing Agentic AI to resolve issues, advance deals, and eliminate busywork through automation while seamlessly handing conversations to humans when needed, with full context preserved. Market-leading brands, including Randstad, Motorola Solutions, Netflix, the San Diego Padres, the Colorado Rockies Baseball Club, and Cal Athletics, trust Dialpad. Dialpad is backed by Andreessen Horowitz, GV, ICONIQ Capital, and T-Mobile. Being a Dialer At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more. We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves. We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic . Your role As an AI Engineer, the selected candidate will build and ship AI-powered tools alongside a small, technically focused team. This is a hands-on engineering role: the candidate will write Python, work with LLMs and agent frameworks, integrate APIs, and help deploy systems that real business teams depend on. Guidance will

About Dialpad Dialpad is the AI platform for customer experience, built to resolve customer problems in real time across voice and digital. Our AI agents learn from your best human agents and improve with every interaction, helping organizations understand their customers, deliver better experiences, increase operational efficiencies, and build a lasting competitive advantage. Unlike legacy systems built to route and answer, or standalone agentic bot vendors built to deflect, Dialpad was built to resolve. Our AI agents and human agents operate on a single platform with shared context, allowing Agentic AI to resolve issues, advance deals, and eliminate busywork through automation while seamlessly handing conversations to humans when needed, with full context preserved. Market-leading brands, including Randstad, Motorola Solutions, Netflix, the San Diego Padres, the Colorado Rockies Baseball Club, and Cal Athletics, trust Dialpad. Dialpad is backed by Andreessen Horowitz, GV, ICONIQ Capital, and T-Mobile. Being a Dialer At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more. We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves. We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic . Your Role As an AI Engineer on our Speech Team, you'll own the back-end implementation and linguistic optimization of the voice ( TTS ) layer for our next-generation AI agents. You'll work squarely within our Speech Team, a high-impact R&D and engineering group focused on speech recognition, enhancement, and synthesis;

About Graphcore At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence. Job Summary As a research engineer at Graphcore, you will contribute to the advancement of AI research, investigating new ideas that push the limits on important AI/ML problems. Specialised hardware has been the key driver of the progress of AI over the last decade, and we believe that hardware-aware AI algorithms and AI-aware hardware developments will continue to be critical to advancing this exciting field. We are therefore looking for individuals who combine strong machine learning experience with practical engineering skills to deliver impactful AI research. We are seeking AI researchers with strong software engineering experience, particularly in lower-level programming and performance optimisation for hardware efficiency. Our research spans a broad range of topics, including efficient training and inference, world models, life sciences, reinforcement learning, and beyond. You will work closely with researchers to generate ideas and translate them into scalable implementations, contributing to publications and projects that help to steer the future of AI hardware. The Team Graphcore Research participates in both fundamental and applied research, to characterise the computational requirements of machine intelligence a

About Us: Sauce Labs is the world’s largest full-lifecycle, test automation platform, and the company behind Selenium. Trusted by 80% of the world’s top ten largest financial institutions and over 300,000 enterprise users, Sauce Labs provides the only AI platform capable of turning business intent into autonomous testing and quality assurance. With a proprietary dataset of 8.7 billion test runs, Sauce Labs empowers the Fortune 2000 to bridge the gap between AI-driven code generation and enterprise-grade software quality. Learn more at saucelabs.com . The Role: We are seeking an innovative and experienced AI Architect to join our engineering leadership team. This is a strategic role that will be instrumental in designing and building the next generation of AI-powered features for our continuous testing platform. You will be responsible for architecting scalable and robust AI solutions that transform how our customers gain insights from their test data and production environments, and how they create tests. Responsibilities: Define AI Architecture: Lead the design and architecture of cutting-edge AI/ML solutions for new product offerings, ensuring scalability, performance, quality and reliability within a cloud-native environment. AI-Powered Insights (Test & Production): Architect AI systems to derive actionable insights from vast quantities of test run logs and analytics data. This includes identifying patterns, anomalies, and performance trends. Production Error Reporting Integration: Design AI solutions that integrate with our existing error reporting product to analyze production issues for mobile and web applications, providing deeper understanding and predictive capabilities. Unified Data Intelligence: Develop architectures for combining insights from both test runs and production data, creating a holistic view of application quality and user experience. Automated Failure Analysis & Remediation: Architect AI models and systems t

About the Team The DoorDash Research Fellowship is a 3-month program (extendable to 6 months) looking for Summer and Fall 2026 cohorts, for researchers and engineers who want to work on the hardest applied ML and AI problems in local commerce. Fellows are given the resources, autonomy, and access to real-world operational data needed to pursue ambitious research directions — with the goal of producing work that influences both the field and how DoorDash operates at scale. This program is modeled on the best external research fellowships: fellows are treated as independent researchers, not as junior employees on a product team. You pick the problem (within a set of priority areas), you own the direction, and you publish or ship the outcome. You’re excited about this opportunity because you will receive… Dedicated compute allocation sized to the research agenda — GPU clusters for training and inference budgets for experimentation Full access to DoorDash's research infrastructure — our internal RL stack, training and evaluation pipelines, RL environments built on real operational systems, agent evaluation harnesses, and the tooling our own research teams use day-to-day. Fellows are first-class users, not sandboxed visitors. Access to DoorDash operational data — real-world datasets spanning logistics, merchant operations, consumer behavior, and marketplace dynamics, under appropriate data governance Research mentorship from senior researchers and engineering leaders at DoorDash, plus a named research sponsor for each fellow who meets with you weekly and is accountable for unblocking your work Speaker series featuring leading researchers and practitioners from academia and industry — faculty from top ML programs, research leads from frontier AI labs, and senior operators from across tech. Fellows get dedicated 1:1 time with speakers when possible. A cohort of fellows working alongside you — a small, tight-knit group of researchers tackling different problems but sharing

About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craf

About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craf

About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craf

Overview: Guidepoint seeks an experienced AI Engineer as an integral member of the Toronto-based AI team. The Toronto Technology Hub serves as the base of our Data/AI/ML team, dedicated to building a modern data infrastructure for advanced analytics and the development of responsible AI. This strategic investment is integral to Guidepoint’s vision for the future, aiming to develop cutting-edge Generative AI and analytical capabilities that will underpin Guidepoint’s Next-Gen research enablement platform and data products. This role demands exceptional leadership and technical prowess to drive the development of next-generation research enablement platforms and AI-driven data products. You will develop and scale Generative AI-powered systems, including large language model (LLM) applications and research agents, while ensuring the integration of responsible AI and best-in-class MLOps. The AI/ML Engineer will be a primary contributor to building scalable AI/ML capabilities using Databricks and other state-of-the-art tools across all of Guidepoint’s products. Guidepoint’s Technology team thrives on problem-solving and creating happier users. As Guidepoint works to achieve its mission of making individuals, businesses, and the world smarter through personalized knowledge-sharing solutions, the engineering team is taking on challenges to improve our internal application architecture and create new AI-enabled products to optimize the seamless delivery of our services. This is a hybrid position based in Toronto. What You'll Do: Architect and Build Production Systems: Design, build, and operate scalable, low-latency backend services and APIs that serve Generative AI features, from retrieval-augmented generation (RAG) pipelines to complex agentic systems. Own the AI Application Lifecycle: Own the end-to-end lifecycle of AI-powered applications, including system design, development, deployment (CI/CD), monitoring, and optimization in production

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

We are expanding our agentic AI capability and are looking for an AI Engineer to join the team. You will work alongside senior engineers to build and maintain AI systems — contributing to agentic pipelines, retrieval infrastructure, and the integrations that tie these systems together. This is a hands-on implementation role with real ownership of components. You will grow your skills in a fast-moving AI practice, working on production systems that directly affect client outcomes. What This Involves: Build and maintain agentic pipelines and workflows under the guidance of senior engineers: tool use, orchestration, and multi-step reasoning. Implement and tune RAG pipelines — including embedding, chunking strategies, vector retrieval, and retrieval evaluation. Contribute to memory and context layer components: integrating vector databases, supporting knowledge graph pipelines, and helping maintain state management across agentic systems. Write clean, well-tested Python code and participate in code reviews. Debug and improve existing AI systems based on evaluation results and production feedback. Collaborate with data engineers and domain experts to integrate AI components with upstream data sources and downstream applications. Document implementations clearly and contribute to shared internal tooling. Requirements: 2–4 years of software or ML engineering experience, with at least 1 year working with LLMs or AI systems in a professional setting. Working knowledge of LLM APIs (OpenAI, Anthropic, or similar) and at least one agentic or RAG framework (LangChain, LlamaIndex, or equivalent). Solid Python skills and comfort with software engineering basics: version control, testing, REST APIs. Familiarity with vector databases or embedding-based search. Curiosity about agentic AI — you follow developments in the space and are eager to apply new techniques. Excellent communication and collaboration skills — comfortable working across cross-functional and client-facing te

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems. About the Role EnCharge AI is seeking a highly skilled and experienced AI Compiler Engineer to spearhead the efforts in developing and optimizing graph compilers tailored to cutting-edge AI and ML workloads. You will collaborate with hardware architects, and AI researchers to enhance performance, optimize computation graphs, and enable efficient model deployment on EnCharge’s Inference Accelerators. Responsibilities Architect, design, and implement optimizations for AI model execution on graph compilers to improve performance, reduce latency, and maximize hardware utilization. Work closely with ML researchers, hardware engineers, and software developers to design and deploy AI models, understanding and addressing hardware-specific challenges. Work on performance optimizations for neural network models, such as layer fusion, operator fusion, and graph-level transformations. Develop compiler optimizations and passes that convert high-level AI models (e.g., from TensorFlow, PyTorch) into intermediate representations (IR). Implement parsing, semantic analysis, and IR generation for deep learning frameworks. Research and integrate the latest advancements in compiler design, ML model optimizations, and hardware acceleration into graph compilers. Provide leadership, mentorship, and technical guidance to a team of engineers focused on graph compiler optimizations. Qual