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About the role AI and coding agents have made building enterprise apps nearly effortless and exposed a massive gap in the infrastructure to run them. Sigma is emerging as the AI Runtime Environment that makes enterprise AI actually work, and this role will tell that story to the market, why it matters, and help define and create this new category. As Director of Product Marketing for AI, you will own the positioning and go-to-market strategy for Sigma's overall AI narrative and its specific capabilities, including: natural language exploration, agentic workflows, and AI-powered application building (both internal and using external coding agents). This is a high-visibility, high-impact role that sits at the intersection of product, sales, and the market. You'll translate a rapidly evolving AI roadmap into sharp, differentiated messaging that resonates with data teams, business leaders, and the enterprise IT buyers who evaluate and approve AI investments. You will be a key voice in helping Sigma win the category. What you'll do Define and own Sigma's AI product positioning and messaging from core value propositions to competitive differentiation and persona-specific narratives. Partner with Product and Engineering to understand and influence Sigma's AI capabilities and roadmap. Lead product launches for AI features and capabilities, coordinating across marketing, sales, customer success, and partnerships. Partner with sales leadership and frontline reps to understand AI deal dynamics, competitive objections, and messaging gaps and incorporate learnings into improved positioning, content, and enablement. Partner with the Enablement team, build and maintain sales enablement materials pitch decks, battlecards, demos, objection-handling guides that help the field win competitive AI deals. Develop thought leadership content and market education programs that shape how the industry thinks about building with AI on enterprise data. Conduct ongoing

Sigma is growing rapidly, and our Technical Support Engineering team is scaling alongside it to meet the needs of an expanding global user base. As a Technical Support Engineer at Sigma, you will be part of an award-winning team recognized with the 2024 Stevie Gold Award for Customer Service, helping customers solve technical, business, and data challenges using the Sigma platform. You'll work closely with Product, Engineering, and Go-to-Market teams to diagnose complex issues, drive solutions, and contribute to the continuous improvement of our product and support operations. Minimum Education Requirement This position requires a U.S. Bachelor's degree (or foreign equivalent) in Computer Science, Software Engineering, Information Systems, Data Science, or a closely related technical field. This requirement is a minimum and cannot be substituted by work experience alone. What You Will Be Doing You will work with Sigma's customers and the pre-sales team to assist with the diagnosis and resolution of complex technical issues. Working closely with the development team, you will develop best practices and tools for diagnosing issues and optimizing the service for performance. Collaborate with cross-functional groups — backend, frontend, DevOps, design, product, and the go-to-market teams to create a first-class experience for users of our product. Participate in quarterly projects and perform periodic on-call duties to improve automation and processes. Qualifications We Are Looking For 2+ years of experience in a customer-facing technical role (Technical Support, Solutions Engineering, or Software Engineering) at a cloud or SaaS provider. SQL proficiency — strong grasp of JOINs, Partitions, Window Functions, Aggregations, CTEs, and sub-queries. SQL query performance troubleshooting and query plan analysis. Proficient in data modeling concepts. Ability to chart data into logical visualizations. A proven track record of building trust with customers and brin

AI only answers correctly when it can trust the data underneath it. This role owns two connected parts of how Sigma shows up in that world: where Sigma's experience lives outside its own product (MCP, a CLI, the Claude and ChatGPT marketplaces, integrations like Slack, Teams, and Glean), and the semantic layer that makes every one of those surfaces trustworthy. The first mandate is Sigma's AI ecosystem: defining Sigma's approach to MCP, giving external agents structured, governed access to Sigma's data model; owning the CLI, giving developers a fast way to work with Sigma outside the UI; and building Sigma's presence in the Claude and ChatGPT marketplaces, plus integrations for Slack, Teams, Glean, and similar surfaces, so people can reach Sigma's data wherever they already work. This is some of the most visible, fastest-growing surface area in the product. The second mandate is the semantic layer underneath it all. Every agent, chat answer, and integration is only as reliable as the data model behind it — get a metric definition wrong here, and every surface built on top inherits the mistake. This includes setting the roadmap for how semantic views connect across data platforms and how the model evolves as new AI capabilities emerge. What you'll do Define Sigma's approach to MCP, giving external agents and tools structured, governed access to Sigma's data model. Own the CLI roadmap, giving developers a fast, scriptable way to work with Sigma outside the UI. Build Sigma's presence in the Claude and ChatGPT marketplaces, along with integrations for Slack, Teams, Glean, and similar surfaces, so people can reach Sigma's data wherever they're already working. Set the roadmap for Sigma's data modeling strategy, including how semantic views connect across data platforms and how the semantic layer evolves as new AI capabilities emerge. Partner with engineering and design to ship integration and semantic modeling capabilities that hold up at enterprise scale.

About the role: Are you a relationship builder? Are you curious to learn about customers and passionate about giving them the best customer experience? Are you a self starter and thrive in a dynamic and fast-paced environment where you can make a difference in creating playbooks and processes? We’re looking for a Renewals Manager to be the post-sales quarterback for our corporate customers. In this role, you will be responsible for renewing and generating expansion opportunities. You will work with your Sigma colleagues to maximize customer value and to grow their business. Responsibilities: Develop professional and personal relationships with key stakeholders within each customer account Partner with Customer Success to minimize attrition, identify growth opportunities, drive adoption and increase usage Work with customers to create a strategy for achieving goals and assist Customer Success with removing barriers to business growth Collaborate closely with internal stakeholders such as Product, Engineering, and Alliances. Be able to communicate feedback and insights to the business. to improve product and customer experience Serve as an advocate for our customers and set appropriate expectations Leverage data to effectively guide meaningful customer conversations Facilitate business reviews to accomplish goals and opportunities Help identify opportunities for improvement in existing processes Required Skills / Experience: 2+ years working in Renewals / Customer Success role Ability to react and adapt to potential rapid shifts in priorities and organizational policies Strong negotiation and objection handling skills that allow for value-based contract negotiations Strong process management and detail oriented skills Strong customer management skills, including soft skills Additional Job details The base salary range for this position is $100k - 120k annually. Compensation may vary outside of this range depending on a number of factors, incl

Solution Architect Sigma Computing The SA role has evolved. Here’s the version we’re hiring for. The SA job in 2026 is not the SA job in 2023. Three things now sit at the center of how we evaluate this role. This hire has to do all three at a senior level, with the architectural depth to back it up. 1. Use AI every day to do the job better. If you are not using Claude, ChatGPT, Cursor, or equivalents to accelerate your account prep, architecture diagramming, prototype builds, RFP responses, and discovery synthesis, you are getting outworked by SAs who are. We expect this hire to treat AI tooling as default infrastructure, not novelty. Come with a point of view on what you run, why, and how you use it to compress weeks of work into days. 2. Sell AI into the account. Buyers want to talk about agents, MCP, A2A, context engineering, and which model is powering what. You have to be fluent. You know Sigma’s AI surface cold: Sigma Assistant in build, analyze, and plan modes, AI functions, input tables with LLM enrichment, MCP integration, and warehouse-native agent patterns. You can architect Sigma agents and warehouse agents into a customer’s stack and explain the tradeoffs to a head of data and a CISO in the same call. You also speak credibly about Claude, OpenAI, Gemini, and the broader stack the customer already runs. 3. Sell against AI. Every enterprise deal has AI competition in it. Sometimes it is Databricks Genie. Sometimes it is Snowflake Cortex Analyst. Sometimes it is a systems integrator pitching a bespoke agent built over the weekend. You know where each of these breaks at scale, where Sigma’s warehouse-native architecture wins on governance, freshness, and cost, and how to draw the line for a skeptical CDO without hand-waving. You can defend that position in an architecture review, on a security questionnaire, and across three follow-up calls. About Sigma Sigma is the AI runtime environment for the modern enterprise. Teams build apps, agents, an

About the role As a Customer Success Manager I at 3rdRisk, you're the person who turns a new customer into a confident, successful one. You own onboarding from kickoff to go-live — guiding organizations through implementation and configuration until they're getting real value from the platform — and you stay with them afterwards as their trusted point of contact, running the relationship through regular check-ins and Quarterly Business Reviews. Based out of Diligent's Vancouver office, you'll serve 3rdRisk customers across North America as part of a Customer Success team that's still taking shape: four CSMs split across Vancouver and Amsterdam, working closely as one team across both hubs. That early stage is the opportunity. Alongside the customer work, you'll help build the function itself — turning what works into repeatable playbooks, templates, and processes, and helping align how 3rdRisk operates as it integrates with Diligent. You'll have real ownership from day one and a direct hand in shaping how the team works as it grows. It's a role for someone who genuinely enjoys the customer relationship, likes creating structure where there isn't any yet, and wants to grow their Customer Success career somewhere their work has visible impact. What you'll do Own customer onboarding end to end — from kickoff through configuration to go-live — so every new customer reaches value quickly and confidently. Build and continuously improve the onboarding experience, capturing what works in playbooks, templates, and documentation the whole team can use. Run the ongoing relationship with your customers: proactive check-ins, Quarterly Business Reviews, and the day-to-day partnership that drives adoption, satisfaction, and retention. Be the trusted point of contact your customers rely on, and step in on more complex situations — coordinating with product, support, and engineering to get to a resolution. Help build 3rdRisk's Customer Success function: shaping process, tooling, and

Position Overview: The Services team has unrivalled expertise across corporate governance, audit, risk, compliance, and regulatory environments in many sectors. They have the scale and reach to deliver significant change smoothly, leveraging solution design and delivery to provide global, dynamic, flexible, and reliable outcomes with lasting results. Their vision is to partner as trusted advisors, working in joint teams that complement client strengths with the right mix of talent. A Delivery Analyst is an early‑career consultant in the Services team who supports delivery of audit, risk, and compliance solutions for clients. They assist with discovery and requirements capture by reviewing client documentation and helping lead discovery workshops to understand current processes and needs. Guided by senior advisors, they translate requirements into technical design documents, configure and iterate Diligent One prototypes, deliver enablement training, maintain issue logs, and follow the standard project methodology to deliver on time and in scope. Key Responsibilities Utilize your product and domain expertise to deliver customer requests and create a delightful customer experience. Be responsible for the timely and quality delivery of the services requested by customers. Collaborate with Customer Success Managers, Senior Managers and Managers to ensure any new service requests are handled appropriately or routed the request to the correct teams. Document and maintain the standard operating procedures for performing the customer-specific request. Consult with the global implementation teams and customer success teams to provide technical guidance to the customer to leverage more value from the product. Required Experience/Skills Ability to effectively engage customer per

Here’s a summary of the role: Diligent is seeking for a Delivery Analyst (Entities) to join our growing Professional Services team. We're looking for individuals who are passionate about technology and consistently exceeds client expectations, from meeting deadlines to delivering high-quality outcomes. In this role, you will be responsible for executing and delivering solutions in response to managed services requests from customers. As a subject matter expert in Diligent's solutions, you will collaborate closely with internal teams to ensure successful delivery and an exceptional client experience. Here’s a breakdown of what you’ll do (not all of it, just the important stuff): Deliver Professional Services over the course of the entire customer journey and/or product enablement lifecycle. Assist PS management, CS team, and other stakeholders (where relevant and appropriate. Example: Sales, PM, PMM or Marketing) in the different stages of the customer lifecycle. Leverage product expertise to provide guidance to customers on best practices regarding solution rollout and project execution. Continue to develop and maintain Domain and Product expertise to provide guidance to customers on best practices for solution rollout and project execution through continuous learning and personal development. Build confidence to lead the implementation process end-to-end independently including engagement with the customer. Work with clients to understand their program goals and how they collect/process data. Support the assessment of source data and document structures, including metadata, naming conventions, relationships, and access requirements, to help define migration scope and mapping needs. Prepare and cleanse data and documents for migration by assisting with deduplication, metadata normalization, validation, and identificati

About Prophecy Prophecy is building the next generation AI-powered data prep and analysis platform. Our platform enables business analysts and data teams to transform raw data into reliable, production-ready datasets and insights faster, using modern data infrastructure and AI-driven capabilities. We work with leading enterprises to simplify how organizations prepare, analyze, and operationalize data, while maintaining strong governance, security, and operational control. Our mission is to make it dramatically easier for organizations to turn complex data into trusted insights that drive decisions. About the Roles We are looking for a Director of Strategic Partnerships who can do both: drive revenue through Prophecy's partner ecosystem, and build an effective partner program. This is an early-stage, high-ownership motion, the playbook is still being written, and you'll have real influence over how we engage partners, what good looks like for partner-sourced pipeline, and how we build durable co-sell relationships with Snowflake, Databricks, GCP field and partner teams. You will sit at the intersection of sales, partnerships, and strategy, owning partner performance while building the programs and processes that scale it. You'll work directly with our AEs and SEs to bring partners into deals at the right moments, and you'll serve as the primary point of contact for our strategic cloud and ecosystem partners. What You’ll Own Partner Revenue & Pipeline Own and exceed partner-sourced and partner-influenced revenue targets on a quarterly basis Proactively generate pipeline through Snowflake, Databricks, and GCP field AEs and PDMs — building the relationships that produce qualified, sourced opportunities Drive joint account mapping and target account activation against Prophecy's ICP: enterprises running Alteryx on Snowflake or Databricks Activate co-sell motions through marketplace programs (GCP Marketplace, Snowflake Partner Network, Databricks

Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including frontier model training, enterprise adoption, defense applications, and autonomous vehicles. Our mission is to develop reliable AI systems for the world's most important decisions. The next frontier for AI is the physical world. We're looking for an AI Product Manager to own the Robotics vertical within our Physical AI team. In this role, you'll own both the development of the data and training environments (the teleoperated demonstrations, real-world collections, simulated tasks, and annotation products that labs use to train and evaluate robot policies) and the "data as a product" strategy that powers them. You'll understand where physical AI is headed, decide what robot tasks and embodiments are worth collecting, how to source and structure the underlying data, and how to turn deep domain knowledge into a defensible product. The ideal candidate has lived inside robotics or physical AI research, and is able to pair that domain understanding with a sense for where current robot policies succeed and fail in real-world workflows. You'll translate that expertise into datasets, environments, and evaluation frameworks that teach robots to do real physical work, and you'll be the domain expert Scale's most important customers and their leading researchers turn to. A strong entrepreneurial & go-to-market mindset will be necessary. What You'll Do Own the Robotics AI roadmap & data strategy: Set product direction for the robotics training stack and the data strategy behind it — what data we collect, on which hardware and embodiments, and what we source internally vs. through our marketplace. Establish a vision for where physical AI is heading, driving execution across engineering, operations, and go-to-market teams. Build partnerships with research teams at frontier labs: Work directly with researchers at leading physical AI labs to understand where their robot p

Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform that provides APIs for knowledge retrieval, inference, evaluation, and more. We are looking for a strong engineer to join our team and help us build and scale our product in a fast-paced environment. The ideal candidate will have a strong understanding of software engineering principles and practices, as well as experience with large-scale distributed systems. You will be responsible for owning large new areas within our product, working across backend, frontend, and interacting with LLMs and ML models. You will solve hard engineering problems in scalability and reliability. You will: Own large new areas within our product Work across backend, frontend, and interacting with LLMs and ML models Deliver experiments at a high velocity and level of quality to engage our customers Work across the entire product lifecycle from conceptualization through production Be able, and willing, to multi-task and learn new technologies quickly Ideally you'd have: 7+ years of full-time engineering experience, post-graduation Experience scaling products at hyper growth startups Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies Proficient in Python or Javascript/Typescript, and SQL Experience with Kubernetes Experience with major cloud providers (AWS, Azure, GCP) Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval

Scale’s ML platform (RLXF) team builds our internal distributed framework for large language model training and inference. The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM's, as well as evaluation of data quality. Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely across Scale’s ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation. If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you! You will: Build, profile and optimize our training and inference framework Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation Research and integrate state-of-the-art technologies to optimize our ML system Ideally you’d have: Strong excitement about system optimization Experience with multi-node LLM training and inference Experience with developing large-scale distributed ML systems Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc. Strong written and verbal communication skills and the ability to operate in a cross functional team environment Nice to haves: Demonstrated expertise in post-training methods &/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the positi

About Scale AI Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with the world's leading enterprises and government organizations to accelerate their AI transformation through frontier AI systems that solve real business problems. Every day, we work with organizations across finance, healthcare, manufacturing, media and telecommunications to build production AI agents that automate complex workflows, help humans, reason over enterprise knowledge, and operate safely at scale. The Opportunity Applied AI is moving faster than ever. New foundation models, reasoning techniques, agent architectures, and research papers emerge every week. Yet building AI systems that reliably solve real-world problems remains one of the hardest engineering challenges. As a Frontier Agent Engineer (Applied AI) , you'll bridge the gap between cutting-edge AI research and production deployment. You'll work directly with enterprise customers to design, evaluate, and deploy intelligent systems that combine frontier models with structured knowledge, retrieval, traditional machine learning, and enterprise software. Unlike traditional ML roles that focus on a single model or product, you'll work across a diverse portfolio of AI challenges spanning multiple industries and use cases. You may build a multi-agent research system and then participate in designing a customer intelligence platform, a healthcare copilot, or an autonomous workflow for a Fortune 100 company. If you enjoy reading new AI papers, experimenting with the latest models, and shipping production systems that create measurable business impact, you'll fit right in. What You'll Build Frontier AI Systems Design and deploy production AI agents that leverage the latest advances in large language models, reasoning, retrieval, memory, and tool use. Architect intelligent systems that combine LLMs, traditional machine learning, structured knowledge, enterprise data

Director of Engineering, Physical AI Role Overview The Director of Engineering will report to the General Manager of Physical AI, and will be responsible for leading a multi-disciplinary engineering organization. In this senior leadership role, you will own the execution of the Physical AI Data Engine — the platform powering the next generation of Physical AI/Embodied AI. You will collaborate closely with Operations and GTM to guide product direction and help solve the data bottleneck that stands between today's robotics research and real-world deployment. This role requires significant ownership in a fast-paced environment and you will motivate internal teams to set the pace for business growth. Travel will come into play. Key Responsibilities: Set and drive the technical vision across data collection infrastructure, teleoperation systems, ML training pipelines, model evaluation frameworks, annotation tooling, and research Lead a multidisciplinary engineering organization—spanning engineering managers, software engineers, ML engineers, and ML research scientists—while designing the organizational structure, talent strategy, and culture required to scale rapidly without compromising on quality or strategic alignment Maintain exceptional technical and operational excellence by deeply understanding team deliverables, asking incisive questions, identifying slipping standards early, and knowing precisely when to step in Drive cross-functional alignment across Engineering, Operations, and GTM on platform architecture, release processes, and shared priorities Collaborate with researchers and clients to architect and deliver scalable, production-grade data infrastructure tailored for complex robotics workloads Required Qualifications: Bachelor's degree in Engineering, Robotics, Computer Science, or a related technical field 8+ years of engineering experience in fast-paced environments, including 4+ years direct people management demonstrated history of recruiting, mentorin