Internal Audit And Sox Manager Jobs

Here's a summary of the role: Identity infrastructure is not glamourware . It doesn't get standing ovations at company all-hands. But it is the thing every product in the portfolio depends on, and when it breaks, everyone knows. If you're the kind of PM who finds that genuinely interesting, read on. This role is for someone who has lived inside SAML flows, argued over PKCE token lifetimes, and knows exactly why SSO bypass lists are a necessary compromise. You'll own the authentication platform that thousands of enterprise customers trust, and you'll shape it alongside engineers who go deep. No surface-level roadmap ownership here. Here's a breakdown of what you'll do (not all of it, just the important stuff) Own the product strategy and roadmap for core authentication flows: SAML 2.0, OIDC/OAuth 2.0 with PKCE, MFA enforcement, token lifecycle management, and multi-IdP federation. Make product decisions that sit at the intersection of system design, security architecture, and developer experience, working directly with senior engineers on protocol choices and infrastructure trade-offs. Drive adoption of platform identity capabilities across internal engineering teams through influence, moving products toward modern API patterns and away from legacy integration surfaces. Use AI coding tools (Claude Code, Cursor, or similar) to prototype ideas and validate concepts before you pitch them; this is expected, not optional. Track the IAM standards landscape, including passkeys, FIDO2, SCIM, zero-trust, and verifiable credentials, and use that knowledge to sharpen product direction. Translate complex authentication concepts (JWT structures, SAML assertions, PKCE handshakes) into clear specs, user stories, and prioritized backlogs for audiences from engineers to executives. These are the essentials you'll ne

Position Overview Diligent is looking for an experienced GRC Advisor to join the team and work on Key accounts to drive adoption and retention and identify areas for expansion within our platform. This position will leverage our GTM, and value drivers established through combining people, process, and technology. If you are anything like the talented GRC Advisors at Diligent then this feeling you have, might be the desire to serve others- but you are blinded by the limits placed on you in your current role. The Client Success Advisor role is key to Diligent’s Customer Success retention strategy. Key Responsibilities Provide guidance and best practices on configuring, customizing, and optimizing the functionality of the GRC platform and modules to align with clients’ business processes and objectives. Conduct in-depth assessments of clients’ GRC requirements, objectives, and challenges to recommend tailored solutions to address their business needs and goals. Collaborate with clients on internal roadmaps, governance, risk, and compliance requirements to be implemented within Diligent’s platform. Ensure proper understanding and adoption of the platform solutions to identify at-risk clients and help increase usage of the software and modules. Partner closely with internal Product, Customer Success, Support, and Sales teams to drive continuous improvement of the platform based on client use cases. Monitor industry trends, regulatory developments, and emerging best practices in the GRC landscape to proactively share insights and recommendations with clients from a subject matter expert perspective. Required Experience/Skills Minimum of 5-7 years of experience in a client-facing role within the legal, compliance, and entity management or subsidiary governance industry. Adaptive mindset and ability to drive out a meaningful path to how our customers see tangible business value. Ability to build relationships both internally and externally to understand the underpinni

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human evaluation and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition. At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. At the foundation of these products is the Platform Engineering team. In this role, you will lead the design and development of core data storage, streaming, caching, and indexing platforms and underlying systems. You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies. You will: Drive the architecture, design, implementation, and reliability of our foundational data platforms and systems, working closely with stakeholders and internal customers to understand and refine requirements. Collaborate with cross-functional teams to define, design, and deliver new features. Proactively identify opportunities for, and driving improvements to, current programming practices, including process enhancements and tool upgrades. Present technical information to teams and stakeholders, providing

About Scale Scale’s mission is to develop reliable AI systems for the world’s most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust. About the Team Applied Intelligence Systems (AIS) is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. AIS spans multiple workstreams — agent evaluation and oversight, orchestration and tool-use infrastructure, model and systems optimization, and applied research on new agent capabilities — and this role is not scoped to any single one of them. We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like. About the Role As a Staff Machine Learning Research Engineer, you will operate across the full breadth of AIS’s technical needs — wherever the hardest ML problem in agentic AI happens to be that quarter. This could mean training and fine-tuning models, designing evaluation and observability systems, building improvement loops from production data, prototyping novel agent architectures, or designing internal systems and tooling that boost productivity across teams. You’re not tied to one team’s roadmap; you’re expected to move to where the technical leverage is highest, and t

About Scale AI Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities. Role Overview As a Forward Deployed AI Engineering Manager on our Enterprise team, you'll be the technical bridge between Scale AI's cutting-edge AI capabilities and our most strategic customers. You'll work with enterprise clients to understand their unique challenges, lead a team that architects specific AI solutions, and ensure successful deployment and adoption of AI systems in production environments. This is a Management role that combines deep engineering and AI expertise, leading a team, and working on customer-facing problems. You'll work directly with customer engineering teams to integrate AI into their critical workflows. Key Responsibilities Customer Integration & Deployment Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs) Build robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows Deploy and configure AI models and agents within customer security and compliance boundaries AI Agent Development Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation Architect multi-agent systems that orchestrate between different models, tools, and data sources Implement evaluation frameworks to measure agent performance and iterate toward business objectives Design human-in-the-loop workflows and feedback mechanisms for continuous agent improvement Prompt Engineeri

About the Role This role will serve as a key execution partner to the GenAI Customer Compliance Manager, with a strong focus on supporting GenAI delivery workflows through embedded compliance operations and Legal coordination. The GenAI Compliance Operations & Programs Associate will sit at the intersection of Delivery, Ops, and Legal, ensuring that compliance considerations are integrated early in the project lifecycle, risks are clearly identified and prioritized, and Legal is engaged with the right context at the right time. By owning execution, coordination, and delivery integration, this role frees the Customer Compliance Manager to focus on strategy, governance, and risk frameworks. The role requires strong operational ownership, comfort with ambiguity, and the ability to drive alignment across fast-moving, cross-functional teams. Key Responsibilities Compliance Review Operations & Legal Coordination Manage end-to-end compliance review workflows for GenAI projects — from early-stage intake through review, approval, launch, and verification — in close partnership with Engagement Management and Delivery. Evaluate incoming work for compliance risk signals (e.g., data sensitivity, copyright exposure, privacy concerns); triage and prioritize requests for Legal based on risk, scope, and delivery timelines. Partner with Engagement Management to translate and execute risk mitigation at both the project and customer level, gather operational input to develop and prioritize systematic compliance controls, and train/educate Ops stakeholders on common or emerging compliance risks. Ensure Legal receives clear, structured, and actionable context; coordinate reviewers across Legal and internal teams to support SLA adherence. Identify workflow friction points and contribute to improving processes, templates, and prioritization frameworks. Maintain dashboards to track throughput, bottlenecks, SLAs, and risk trends. Risk Mitigation Solutions & Monitoring Support Eng

Owns day-to-day deal pricing execution for Enterprise, including pricing calculations, staffing inputs, and dashboard reporting that gives Sales and Deal Desk leadership visibility into deal status, pricing, and margin. Partners with the Deal Desk Manager on customer deal structuring and on identifying ways to accelerate the deal approval process. What You’ll Do Deal Pricing Execution Manages day-to-day deal pricing calculations for Enterprise deals, including ROI modeling and EPD staffing inputs Applies existing pricing frameworks consistently across Land, Expansion, and Framework deals Partners with the Deal Desk Manager to design customer-specific deal structures for non-standard or complex deals Pricing & Deal Status Reporting Builds and maintains a pricing dashboard tracking deal status, pricing, margin, and overall deal health across the Enterprise pipeline Updates pricing and trend reporting to keep leadership current on where deals stand and how pricing is trending against targets Process Optimization Identifies bottlenecks in the deal review & approval process and proposes ways to accelerate cycle time Helps standardize and streamline recurring pricing/approval steps into repeatable, lower-friction workflows Tracks deal approval cycle time and surfaces trends to inform process improvements Cross-Functional Deal Coordination Owns deal crafting end-to-end from initial pricing/modeling through contract review, coordinating with sales management, reps, and internal stakeholders Ensures the relevant stakeholders (Accounting, Finance, EPD, Legal) review and approve deals at the appropriate stage before they progress Routes deal packages to the correct reviewers and tracks open items so deals aren't stalled waiting on sign-off Strategic & GTM Support Tackles ambiguous, open-ended questions around Scale's go-to-market motion and iterates quickly on solutions that deliver measurable results Supports Sales and Finance leadership in quarterly strategy and

About Scale AI Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities. Role Overview As a Senior Staff Frontier Agents Engineer on our Enterprise team, you'll be the technical bridge between Scale AI's cutting-edge AI capabilities and our most strategic customers. You'll work with enterprise clients to understand their unique challenges, architect custom AI solutions, and ensure successful deployment and adoption of AI systems in production environments. This is a hands-on technical role that combines deep engineering expertise with customer-facing problem solving. You'll work directly with customer engineering teams to integrate AI into their critical workflows. Key Responsibilities Customer Integration & Deployment Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs) Build robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows Deploy and configure AI models and agents within customer security and compliance boundaries AI Agent Development Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation Architect multi-agent systems that orchestrate between different models, tools, and data sources Implement evaluation frameworks to measure agent performance and iterate toward business objectives Design human-in-the-loop workflows and feedback mechanisms for continuous agent improvement Prompt Engineering & Optimization Create sophisticate

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human eval and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition. At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. At the foundation of these products is the Platform Engineering team. In this role, you will support the design and development of shared platforms used across Scale. This includes designing our foundational data platforms and lifecycle, architecting Scale’s core cloud infrastructure and orchestration stack, and redefining how engineers develop, build, test, and deploy software at Scale. You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies. You will: Drive the design, and implementation of our foundational platforms and systems, working closely with stakeholders and internal customers to understand and refine requirements. Collaborating with cross-functional teams to define, design, and deliver new features. Proactively identifying opportunities for, and driving improvements to, current p

About Scale At Scale AI, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we’re accelerating the abundance of frontier data to pave the road to Artificial General Intelligence (AGI), and building upon our prior model evaluation work with enterprise customers and governments, to deepen our capabilities and offerings for both public and private evaluations. About Data Engine Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through world-class RLHF (Reinforcement Learning with Human Feedback), human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. Our Approach As part of the interview process, you’ll be considered for opportunities across several teams within the GenAI Engineering organization, based on your interests, expertise, and business needs. Potential team placements include Allocation, Growth, Frontier Data, Trust & Safety, Pay, Operator, or Tasking Experience. Together, these teams power Scale’s AI data operations - from building high-impact datasets that push the boundaries of LLM capabilities, to optimizing contributor onboarding and incentives, to safeguarding data integrity through advanced trust, safety, and security measures. They work at the intersection of ML, operations, and analytics to ensure we deliver the highest-quality data at scale. Responsibilities: Design, build, and maintain robust, scalable systems across the full stack, including front-end, back-end, and infrastructure layers Implement high-impact features using modern technologies such as TypeScript, React, Node.js, MongoDB, Elasticsearch, and Temporal Collaborate closely with internal operators (your use

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. Tenstorrent is seeking an Physical Design Engineer to lead cross-functional efforts to solve complex physical design challenges and develop end-to-end RTL-to-GDS methodologies across advanced nodes, with a strong focus on PPA and runtime improvements. The engineer will architect, integrate, and deploy AI/ML-driven solutions into production physical design flows, creating custom CAD tools and partnering with internal teams and EDA vendors to drive next-generation, ML-enabled capabilities. This role is hybrid, based out of Santa Clara, CA or Austin, TX or Fort Collins, CO. 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 BS in Electrical or Computer Engineering (or equivalent experience) with 5+ years in Physical Design CAD methodology at advanced nodes. Proven track record improving PPA and/or runtime on high-performance, low-power taped-out designs. Hands-on with industry-standard EDA tools (e.g., Fusion Compiler) across synthesis, P&R, STA, signoff, and hierarchical flows. Strong Python/Tcl and data skills, with interest or experience in ML frameworks (PyTorch, TensorFlow), and the ability to drive complex projects independent

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human eval and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition. At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. At the foundation of these products is the Platform Engineering team. In this role, you will support the design and development of shared platforms used across Scale. This includes designing our foundational data platforms and lifecycle, architecting Scale’s core cloud infrastructure and orchestration stack, and redefining how engineers develop, build, test, and deploy software at Scale. You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies. You will: Drive the design, and implementation of our foundational platforms and systems, working closely with stakeholders and internal customers to understand and refine requirements. Collaborating with cross-functional teams to define, design, and deliver new features. Proactively identifying opportunities for, and driving improvements to, current p

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. We are seeking a hands-on Data Center Technician, IT Contractor to support the installation, deployment, relocation, cabling, inventory, maintenance, and decommissioning of servers, network equipment, storage systems, and other IT infrastructure. The successful candidate will follow established procedures, maintain accurate documentation, and coordinate effectively with internal technical teams and data center personnel. This role will be a 6-month contract fully on-site, based out of the downtown Toronto, ON Beanfield data center, with travel to Tenstorrent office locations and other data center facilities as required. 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 You bring approximately 2 to 5 years of hands-on experience in data center operations, IT infrastructure, server or network hardware, or a similar technical role. You are comfortable working with servers, network equipment, storage systems, racks, rack layouts, and copper and fiber optic cabling. You are detail-oriented, organized, and able to follow documented procedures, cabling standards, installation instructions, and safety requirements. You communicate clearly, work well wi

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. At Tenstorrent, we are building open, scalable compute for real AI workloads. As Director, Customer Hardware Engineering, you own the technical relationship with strategic customers and FAEs, turning their silicon needs into precise requirements for our hardware and software teams. You connect customer architectures to Tenstorrent platforms so their models run efficiently on our silicon. This role is hybrid, based out of Toronto, 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 Experienced leader of customer-facing technical teams and Field Application Engineering organizations. Strong background across RTL, verification, and physical design for custom silicon programs. Systems thinker who understands how RTL choices affect software, performance, and customer solutions. Clear communicator who aligns customers, FAEs, and internal teams around shared technical goals. What We Need Own technical customer relationships and convert high-level asks into concrete engineering specifications. Coordinate with hardware and software leads on customer-specific NEO silicon configurations. Oversee RTL changes and NEO cus

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. We are looking for a Field Application Engineer to serve as the technical bridge between Tenstorrent and customers across Southeast Asia. Based in Singapore, you will work closely with customers, partners, sales, and global engineering teams to understand AI and machine learning workloads, guide technical evaluations and deployments, troubleshoot issues across hardware and software, and help customers realize the performance of Tenstorrent’s AI platforms. This is a highly visible, customer-facing role that combines hands-on technical problem solving, solution development, and regional relationship building, with regular travel throughout Southeast Asia. This role is remote, based out of Singapore. 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 customer-focused technical professional who can build trust with application developers, engineering teams, and business stakeholders. Comfortable translating complex AI/ML hardware and software concepts into clear recommendations for both technical and non-technical audiences. A proactive and self-directed problem solver who can coordinate internal teams, external service providers, and customer sta