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About BackOps BackOps is building the AI resolution layer for companies that make or move physical goods. The physical economy still runs on a fragmented web of ERPs, WMSs, TMSs, carrier and vendor portals, email, spreadsheets, legacy systems, and the people who know how to hold all of it together. When something breaks, whether a shipment is delayed, an order changes, a claim needs to be filed, a document is missing, someone has to gather context across systems, determine what should happen next, take action, and make sure the problem is actually resolved. BackOps automates that work. Our platform, Relay, deploys AI operators that understand operational workflows, work across the systems our customers already use, take action, navigate exceptions, and carry work through to resolution. We aren't building another dashboard or copilot. We're building the execution layer that sits between systems and gets the work done. We recently raised a $42M Series B led by Insight Partners, just six months after our Series A, and are scaling across some of the largest companies in logistics, manufacturing, retail, and other industries that power the physical world. The opportunity ahead is much larger than any single workflow or vertical. We're building the infrastructure for AI to operate reliably inside the real world. The Role As an AI/ML Engineer, you'll own the infrastructure and systems that make Relay's underlying intelligence more capable, efficient, and reliable. Building an AI operator that can reason through a workflow is one challenge. Making that intelligence perform consistently across thousands of real-world operational scenarios, fragmented enterprise systems, and unpredictable conditions is another. That's the problem you'll help solve. You'll work at the intersection of machine learning, distributed systems, and production AI, designing the infrastructure that supports model execution, inference optimization, evaluation, and continuous improvement. You'll help determine how we select, adapt, deploy, and optimize the models powering Relay, balancing intelligence and quality with the performance, scalability, and economics required for enterprise production. This isn't a role focused on integrating AI APIs or maintaining an established ML stack. You'll make foundational technical decisions, build capabilities from the ground up, and work closely with our CTO and engineering teams to advance the intelligence behind our products. This is a senior individual contributor role with extraordinary leverage. You'll help define the AI/ML foundation BackOps scales on. What You'll Own Build the infrastructure behind Relay's intelligence Own the architecture and engineering of the AI/ML systems that power Relay, enabling our models to run reliably, efficiently, and at scale. That may include: • Model serving, inference pipelines, and execution infrastructure • Model selection, routing, and orchestration • Shared interfaces for accessing foundation models and specialized ML capabilities • Infrastructure for experimentation, evaluation, and production deployment • Model versioning, configuration, and lifecycle management • Compute orchestration and resource optimization where appropriate You'll make pragmatic decisions about what we build, what we leverage from existing providers, and where custom infrastructure creates a meaningful advantage. Make intelligence faster, more efficient, and more scalable Our agents need to do more than produce the right answer. They need to operate within the performance and reliability constraints of real enterprise workflows. You'll optimize the infrastructure and execution patterns that determine how efficiently our models perform, including latency, throughput, scalability, and inference cost. You'll identify bottlenecks across the AI/ML stack, evaluate architectural tradeoffs, and implement improvements that allow us to support increasingly sophisticated workloads without sacrificing quality or reliability. The goal isn't simply to make models run faster. It's to make better intelligence economically viable at scale. Continuously improve model intelligence and quality Model capability is evolving rapidly. Our ability to translate those advances into better outcomes for customers needs to evolve just as quickly. You'll develop the systems and technical approaches that allow us to systematically improve model quality and effectiveness. That may include: • Model evaluation, benchmarking, and comparative testing • Model selection and routing based on task complexity and performance • Context engineering, retrieval, and other techniques that improve model effectiveness • Fine-tuning, model adaptation, or specialized ML approaches where warranted • Experimentation frameworks that connect model changes to measurable improvements in production You'll partner closely with engineers building Relay's agent capabilities to understand where model limitations create execution failures and determine the most effective way to address them. Engineer reliability into production AI AI systems are probabilistic. Enterprise operations demand consistency, accountability, and predictable behavior. You'll build the infrastructure required to operate AI/ML workloads reliably, including observability, failure detection, recovery mechanisms, performance monitoring, and appropriate operational controls. You'll help establish the engineering standards that allow us to safely introduce new models, evaluate changes, manage infrastructure dependencies, and operate increasingly complex AI systems in production. Reliability can't be something we add after intelligence improves. It has to be built into the systems that deliver it. Make AI/ML performance measurable You'll help establish the technical metrics and feedback loops that tell us whether our AI/ML infrastructure is getting better. That includes measures such as: • Model accuracy and task-level quality • Inference latency and throughput • Cost per inference and cost per successful resolution • Model reliability, availability, and error rates • Evaluation performance and regression detection • Resource utilization and infrastructure efficiency • Time required to evaluate and deploy new model capabilities You'll work with Product and Engineering to connect these technical improvements to the outcomes that matter most: more accurate decisions, more reliable execution, and greater autonomous resolution. What We're Looking For You may be a strong fit if you: • Have 6+ years of relevant software engineering experience, with meaningful ownership of complex technical systems at the Senior or Staff level. • Have built, deployed, or optimized production AI/ML infrastructure, model-serving systems, inference pipelines, or similarly sophisticated AI systems. • Have exceptional engineering fundamentals and strong proficiency in Python, with experience designing and operating reliable distributed systems. • Understand modern foundation models and the tradeoffs involved in model selection, inference architecture, quality, latency, throughput, scalability, and cost. • Have hands-on experience with cloud infrastructure, containerization, orchestration, and the operational challenges of running production AI workloads. • Understand model evaluation and experimentation, and can connect technical changes to measurable improvements in model quality or system performance. • Can move fluidly between foundational architecture, hands-on implementation, performance debugging, and production operations. • Have operated in an environment where technical requirements evolved rapidly and architectural decisions had to balance immediate needs with long-term scalability. • Have strong opinions, loosely held. You can create clarity with incomplete information, explain technical tradeoffs, and adapt quickly as the technology and evidence evolve. • Hold an unusually high bar for engineering quality while still moving with urgency. Particularly Compelling We'd be especially interested if you've: • Built foundational AI/ML infrastructure at a breakout AI company or exceptional technology organization where performance, reliability, and scale were critical. • Worked deeply with technologies such as PyTorch, Hugging Face, vLLM, Triton, Ray, Kubernetes, or comparable model-serving and distributed-computing frameworks. • Designed systems for model optimization, fine-tuning, post-training, evaluation, or continuous improvement of production AI capabilities. • Built infrastructure supporting agentic AI systems, complex model orchestration, or multi-step autonomous workflows. • Delivered measurable improvements in model quality, inference efficiency, infrastructure cost, or production reliability. • Built shared technical capabilities from zero, establishing architectures and engineering standards that other teams could build upon. Experience with supply chain or logistics is useful. However, exceptional engineering judgment, AI/ML systems depth, and a track record of shipping production infrastructure matter more. This Probably Isn't the Role for You If • Your AI experience is primarily integrating third-party APIs or building lightweight LLM applications without meaningful infrastructure or systems ownership. • Your background is concentrated in theoretical ML research or offline model development, and you haven't owned the engineering required to bring AI capabilities into production. • You're most comfortable operating within an established infrastructure stack rather than defining foundational systems and architectural patterns. • You think optimizing model performance is exclusively about improving benchmark scores, without considering reliability, latency, cost, and real-world outcomes. • You prefer narrowly scoped implementation work over owning difficult technical problems from architecture through production. Why BackOps AI has become remarkably good at generating information. The next frontier is much harder: AI that can be trusted to do the work. In the physical economy, that means more than producing the right answer. An agent has to understand what's happening, make a decision, interact with real systems, navigate exceptions, communicate with real people, and remain accountable until the problem is actually resolved. That creates an unusually challenging engineering problem. You'll help build the AI/ML foundation that makes this possible, determining how models are deployed, evaluated, optimized, and continuously improved as Relay takes on increasingly sophisticated operational work. Few engineering teams get the opportunity to shape the underlying intelligence and infrastructure of an emerging AI platform while the category itself is still being invented. That's the opportunity at BackOps.

Listed by BackOps AI for a position based in the United States. Employers on this board attest they are hiring domestically.

Product Management

19 days ago

Product Manager, Agent Platform

BackOps AI · San Francisco

Pay

$135k–220k

Setting

On-site

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About BackOps BackOps is building the AI resolution layer for companies that make or move physical goods. The physical economy still runs on a fragmented web of ERPs, WMSs, TMSs, carrier and vendor portals, email, spreadsheets, legacy systems, and the people who know how to hold all of it together. When something breaks, whether a shipment is delayed, an order changes, a claim needs to be filed, a document is missing, someone has to gather context across systems, determine what should happen next, take action, and make sure the problem is actually resolved. BackOps automates that work. Our platform, Relay, deploys AI operators that understand operational workflows, work across the systems our customers already use, take action, navigate exceptions, and carry work through to resolution. We aren't building another dashboard or copilot. We're building the execution layer that sits between systems and gets the work done. We recently raised a $42M Series B led by Insight Partners, just six months after our Series A, and are scaling across some of the largest companies in logistics, manufacturing, retail, and other industries that power the physical world. The opportunity ahead is much larger than any single workflow or vertical. We're building the infrastructure for AI to operate reliably inside the real world. The Role As Product Manager, Agent Platform, you'll own the product foundation that makes Relay extensible, reliable, secure, and increasingly reusable across customers, systems, and workflows. BackOps operates in environments that were never designed for AI. A single workflow might span an ERP, a carrier portal without an API, an email inbox, a PDF attachment, a customer-specific business rule, and a human approval before anything can move forward. Making an agent work once is not the hard part. The hard part is building a platform that makes the next integration easier, the next workflow faster to launch, the next customer less bespoke, and thousands of concurrent resolutions more reliable than the humans and systems they replace. You'll own that problem. You'll work at the intersection of product architecture, enterprise infrastructure, agent execution, customer deployments, and developer experience. You'll partner closely with engineering, our founders, and customers to identify where recurring complexity should become a durable platform primitive, and where it shouldn't. This is a senior individual contributor role with extraordinary leverage. You won't inherit a mature roadmap. You'll help define the product architecture BackOps scales on. What You'll Own Build the platform underneath every AI operator Own the strategy and roadmap for the core capabilities that allow Relay to connect, reason, execute, recover, and scale across enterprise environments. That may include: • Integrations, connectors, APIs, webhooks, and data movement • Browser automation and interaction with non-API systems • Workflow execution and orchestration primitives • Identity, credentials, permissions, and access controls • Agent tooling, configuration, and extensibility • Execution state, retries, recovery, and exception handling • Observability, auditability, and production monitoring • Testing and evaluation infrastructure • Internal and external developer tooling • Enterprise security, governance, and deployment controls Turn deployments into product leverage Our customers have complicated environments. That complexity is a source of product insight, not a reason to build fifty different versions of BackOps. You'll work closely with engineers, designers, members of our GTM team, and our customers to understand what we're repeatedly solving, identify the right abstraction, and turn deployment patterns into reusable capabilities. You'll continually answer questions like: • What belongs in the core platform versus a customer-specific implementation? • Which integrations should we build natively, make configurable, or enable others to extend? • Where should we create an abstraction, and where would an abstraction create more complexity than it removes? • How do we reduce the marginal engineering effort required to launch the next customer, workflow, system, or vertical? Make enterprise AI reliable enough to run operations Our agents don't just surface information. They take action. You'll define the product requirements and operating standards that make that possible at enterprise scale: reliability, latency, isolation, permissions, traceability, error recovery, governance, and security. You'll partner deeply with engineering on technical tradeoffs and make sure those decisions connect back to customer value. Create a world-class builder experience The people building on BackOps should be able to create sophisticated workflows without understanding every piece of infrastructure underneath them. You'll define the primitives, interfaces, APIs, tools, and product surfaces that make the platform powerful without making it unnecessarily complicated. Define what scale actually means You'll establish the metrics that tell us whether the platform is compounding: • Time to launch a new integration or workflow • Deployment and configuration time • Reuse across customers and use cases • Execution success and recovery rates • Platform reliability and performance • Engineering effort per incremental deployment • Adoption of shared platform capabilities The goal isn't simply to ship infrastructure. It's to make every additional BackOps deployment easier because the previous ones existed. What We're Looking For You may be a strong fit if you: • Have 6+ years of relevant experience across product management, engineering, founding, or similarly technical product-building roles, with meaningful ownership at the Senior or Staff level. • Have built platform, infrastructure, API, integration, workflow, developer, or similarly technical products used in production. • Have exceptional technical fluency. You don't need to be the person writing the production code, but you can go deep with engineers on architecture, APIs, distributed systems, state, data models, permissions, reliability, and system tradeoffs. • Have repeatedly turned ambiguous or customer-specific problems into simple, reusable product abstractions. • Understand that great platform product management requires customer obsession, not distance from the customer. • Have operated in a high-growth environment where the architecture had to evolve while the company continued shipping. • Can move fluidly between long-term platform strategy and the detailed product decisions required to make it real. • Have strong opinions, loosely held. You can create clarity with incomplete information, explain your reasoning, and change your mind quickly when the evidence changes. • Hold an unusually high bar for product quality while still moving with urgency. Particularly Compelling We'd be especially interested if you've: • Built products at a breakout AI company or exceptional B2B software company where platform leverage mattered to company scale. • Worked on agent infrastructure, workflow automation, integration platforms, developer platforms, enterprise infrastructure, or AI/ML systems. • Built products that must interact with messy third-party or legacy systems rather than operating inside a pristine software ecosystem. • Worked in logistics, manufacturing, commerce, construction, healthcare, field services, or another industry where software touches the physical world. • Built technology for users who are exceptionally good at their jobs but aren't necessarily technical, and shouldn't have to become technical to use your product. Domain expertise in supply chain is useful. However, exceptional product judgment and technical depth matter more. This Probably Isn't the Role for You If • Your definition of platform product management is primarily maintaining an internal engineering backlog. • You need a fully formed roadmap before you can start executing. • You're most comfortable optimizing an established product rather than defining abstractions that don't yet exist. • You prefer staying several layers removed from customers and deployments. • You default to solving every new requirement with either a one-off build or a generalized framework. Great judgment about when to productize is fundamental to this role. Why BackOps AI has become remarkably good at generating information. The next frontier is much harder: AI that can be trusted to do the work. In the physical economy, that means more than producing the right answer. An agent has to understand what's happening, make a decision, interact with real systems, navigate exceptions, communicate with real people, and remain accountable until the problem is actually resolved. That creates an unusually rich product problem. You'll help determine how agents reason about real-world operations, where humans remain in the loop, how quality is measured, how trust is earned, and how AI progresses from assisting an operator to owning an outcome. Few product teams get the opportunity to define those interaction models while the category itself is still being invented. That's the opportunity at BackOps.

Listed by BackOps AI for a position based in the United States. Employers on this board attest they are hiring domestically.

Product Management

19 days ago

Product Manager, AI Agents

BackOps AI · San Francisco

Pay

$135k–220k

Setting

On-site

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About BackOps BackOps is building the AI resolution layer for companies that make or move physical goods. The physical economy still runs on a fragmented web of ERPs, WMSs, TMSs, carrier and vendor portals, email, spreadsheets, legacy systems, and the people who know how to hold all of it together. When something breaks, whether a shipment is delayed, an order changes, a claim needs to be filed, a document is missing, someone has to gather context across systems, determine what should happen next, take action, and make sure the problem is actually resolved. BackOps automates that work. Our platform, Relay, deploys AI operators that understand operational workflows, work across the systems our customers already use, take action, navigate exceptions, and carry work through to resolution. We aren't building another dashboard or copilot. We're building the execution layer that sits between systems and gets the work done. We recently raised a $42M Series B led by Insight Partners, just six months after our Series A, and are scaling across some of the largest companies in logistics, manufacturing, retail, and other industries that power the physical world. The opportunity ahead is much larger than any single workflow or vertical. We're building the infrastructure for AI to operate reliably inside the real world. The Role As Product Manager, AI Agents, you'll define what it means for an AI operator to be truly excellent at operational work. This isn't a role focused on adding AI features to an existing SaaS product. AI is the product. Relay has to understand an incoming problem, reconstruct context scattered across multiple systems, interpret company-specific rules, determine the right next action, use tools to execute it, recognize when something has gone wrong, know when human judgment is necessary, and carry the work through to a measurable outcome. And it has to do this in environments where the end user may be a logistics coordinator, warehouse operator, customer service representative, dispatcher, buyer, or operations manager, not an AI expert. You'll own the product systems and experiences that make those agents intelligent, trustworthy, controllable, and increasingly autonomous. This is a senior individual contributor role for someone who wants to help define a new category of software from first principles. What You'll Own Define how BackOps agents work Own product strategy across the agent lifecycle: how agents understand work, reason about it, take action, handle ambiguity, recover from failure, learn from outcomes, and collaborate with humans. You'll help answer fundamental questions such as: • What context does an agent need to make the right decision? • Which parts of a workflow should be deterministic and which should be agentic? • When should an agent act autonomously versus request approval? • How should agents interpret SOPs, business rules, historical outcomes, and unstructured information? • How should an agent behave when reality doesn't match the documented process? • How do we make agent behavior configurable without forcing customers to become prompt engineers? Turn messy operations into great products The workflows BackOps automates were rarely designed cleanly in the first place. You'll spend time with customers and operators understanding how work really happens, including the workarounds, exceptions, judgment calls, and tribal knowledge that never appear in an SOP. Then you'll determine what the best AI-native version of that workflow should be. You'll work across areas like order management, shipment exceptions, claims, billing, document processing, scheduling, vendor coordination, and entirely new categories we haven't built yet. The objective isn't to reproduce the manual process step for step. It's to build a better one. Own the quality bar for production AI For traditional software, you can usually define what the product will do. For AI products, you have to define what good looks like and build systems for continuously determining whether you're achieving it. You'll own the product strategy for agent quality, partnering with engineering to develop: • Evaluation frameworks and success criteria • Golden datasets and representative test cases • Simulation and regression testing • Human evaluation and annotation • LLM-based evaluation where appropriate • Production quality monitoring • Failure taxonomies and root-cause loops • Feedback systems that turn real-world outcomes into better agent behavior You'll define the domain-specific quality bar; our platform team will help build the infrastructure that makes evaluating and operating agents at scale possible. Design human + AI collaboration The goal isn't autonomy for autonomy's sake. Some tasks should happen automatically. Some require approval. Some need a human because judgment or accountability matters. You'll define those boundaries and build experiences that allow operators to understand what Relay is doing, trust it, correct it when necessary, and progressively hand over more work as confidence grows. Great AI UX at BackOps might mean making something disappear entirely. It might also mean showing exactly the right context at exactly the right moment for a five-second human decision. Knowing the difference is core to this job. Make intelligence compound Every workflow creates data about how operations actually function: which exceptions occur, where people intervene, which decisions work, where processes break, and what ultimately resolves the issue. You'll help determine how BackOps turns that data into a product advantage, improving agent quality, workflow design, and customer outcomes over time. Measure outcomes, not demos You'll define and own metrics such as: • End-to-end resolution rate • No-touch / autonomous resolution rate • Task accuracy and quality • Human intervention and escalation rate • Time to resolution • Failure and recovery patterns • Customer adoption and trust • Operational impact generated for customers A compelling demo is useful. A production system that completes mission-critical work every day is the standard. What We're Looking For You may be a strong fit if you: • Have 6+ years of relevant product-building experience, with meaningful ownership at the Senior or Staff level. • Have shipped AI/ML, LLM, agentic, automation, or other technically sophisticated products into production and owned the outcome, not simply added an AI feature to an existing roadmap. • Understand the unique challenges of non-deterministic products: evaluation, quality measurement, grounding, tool use, context, failure modes, guardrails, human escalation, and continuous improvement. • Have exceptional product instincts and can take a complicated workflow and make the resulting user experience feel obvious. • Are technically fluent enough to work deeply with engineers and AI practitioners without outsourcing the hard product decisions to them. • Have built zero-to-one products or substantially rethought how an existing category should work. • Stay extraordinarily close to users and are comfortable observing messy real-world processes rather than relying exclusively on feature requests. • Can distinguish between an impressive AI capability and a product customers will trust with real work. • Move quickly, think rigorously, and are energized by a product surface where many of the right answers haven't been discovered yet. Particularly Compelling We'd be especially interested if you've: • Built core products at a breakout AI company or an exceptional, high-growth B2B software company. • Directly owned agentic systems, LLM applications, AI workflow products, AI-native interfaces, or evaluation systems. • Built products where reliability, accuracy, explainability, or trust had meaningful real-world consequences. • Worked in logistics, manufacturing, commerce, construction, healthcare, field services, or another operationally complex industry. • Built for users who aren't highly technical and have little patience for software that requires them to change how they work. • Developed products where software interacts with both digital systems and real-world operational processes. You don't need to arrive knowing supply chain. You do need to be the kind of product builder who can become dangerous in a complicated domain very quickly. This Probably Isn't the Role for You If • Most of your AI experience is adding a chatbot or generative feature to an otherwise traditional SaaS product. • You think the objective is to maximize autonomy rather than maximize customer outcomes. • You prefer product problems where behavior can be completely specified in advance. • You stay primarily at the roadmap/PRD layer and expect engineering or research to make the difficult AI product decisions. • Your first instinct is to replicate a customer's existing workflow exactly as it exists today. Why BackOps AI has become remarkably good at generating information. The next frontier is much harder: AI that can be trusted to do the work. In the physical economy, that means more than producing the right answer. An agent has to understand what's happening, make a decision, interact with real systems, navigate exceptions, communicate with real people, and remain accountable until the problem is actually resolved. That creates an unusually rich product problem. You'll help determine how agents reason about real-world operations, where humans remain in the loop, how quality is measured, how trust is earned, and how AI progresses from assisting an operator to owning an outcome. Few product teams get the opportunity to define those interaction models while the category itself is still being invented. That's the opportunity at BackOps.

Listed by BackOps AI for a position based in the United States. Employers on this board attest they are hiring domestically.

Marketing

45 days ago

AI GTM Strategy Manager

BackOps AI · San Francisco

Pay

$120k–225k

Setting

On-site

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About BackOps Supply chain operations are some of the most complex, high-stakes workflows in any business, and for too long, the teams running them have had to hold it together manually. Missed shipments, cascading exceptions, hours of coordination that pulls people away from the work that actually moves the business forward. The infrastructure hasn't kept up with the complexity. BackOps is changing that. We build agentic AI that automates the operational work supply chain teams have always had to do by hand: investigating exceptions, coordinating across systems and stakeholders, and driving work through resolution, not by wrapping AI around legacy tools, but by rethinking the infrastructure from the ground up. We're ~50 people, headquartered in San Francisco, and backed by investors who support category-defining companies early and with conviction: our Series B was led by Insight Partners, with participation from Theory Ventures, Construct Capital, Gradient Ventures, and 10VC. The problem is real, the timing is right, and we're building the team that will define how this space evolves. If you want to do work that matters, with people who take ownership seriously, at a company where what you build becomes the foundation, keep reading. The Role We’re hiring our first AI GTM Strategy Manager to build the systems, automation, and data foundation that make our go-to-market organization more effective as we scale. You’ll sit at the intersection of Sales, Customer Success, and our product and data systems, turning complex GTM workflows into tools that help the team understand accounts faster, identify the right customer problems to solve, move opportunities forward, and clearly demonstrate the value BackOps delivers. That challenge is particularly interesting at BackOps. We sell into large enterprises with multiple sites, stakeholders, systems, and workflows, where traditional CRM structures and off-the-shelf RevOps playbooks break down quickly. You’ll help us design the infrastructure behind that motion while also building practical, field-facing tools that make the team better today. One of your first priorities will be building what we call the value ledger: a reliable, continuously updated view of the value BackOps creates for each customer and workflow: from hours saved to claims filed, exceptions resolved, and response times improved. Done well, it becomes a shared source of truth for customer outcomes, renewals, expansion, and storytelling. This is a hands-on builder role. You’ll work directly with GTM leaders and the field to identify bottlenecks, structure ambiguous problems, prototype solutions, ship quickly, measure adoption and impact, and evolve what works into durable systems. What You’ll Do Build leverage for the field • Build tools that give AEs, Solutions Engineers, and CSMs the context they need to act quickly, including account structure, locations, systems, stakeholders, workflows, pilots, and recommended next actions. • Create a workflow discovery system that can turn discovery calls, customer SOPs, ticket exports, and other inputs into a structured, ROI-ranked view of automation opportunities. • Instrument pilots and POVs so the team can quickly understand progress, adoption, conversion risk, customer value, and what needs attention. • Build AI-enabled workflows that reduce administrative work after customer interactions: drafting follow-ups, extracting next steps and product feedback, and proposing CRM updates for review. • Surface expansion opportunities by connecting product usage, customer outcomes, account context, and workflow-level signals so the field knows where to go deeper and why. Build the infrastructure behind it • Design the GTM data model for complex enterprise customers, including parent/account/site relationships, buying groups, workflows, pilots, ownership, and stage logic. • Build and maintain the value ledger, connecting product activity and operational outcomes to a defensible view of value realized at the account and workflow level. • Create a reusable context layer that powers GTM tooling with trusted information about accounts, stakeholders, product usage, workflows, and recommended actions. • Establish data-quality observability across the GTM stack, including completeness, freshness, duplicates, orphaned records, stale ownership, and broken integrations. • Partner with GTM leadership on where systems, automation, process, or operating-model changes can unlock greater productivity and scale, not simply where another tool should be added. At this stage, we expect the role to spend more time building the underlying GTM data and systems foundation, with an increasing portion of time focused on field-facing tooling as that foundation matures. What You’ll Bring • 5+ years of experience across GTM systems, Revenue Operations, technical operations, growth, automation, business operations, or another applied technical role supporting a complex B2B organization. • A track record of building things people actually use. You can point to systems, tools, automations, or workflows you shipped, how users adopted them, and how you improved them after launch. • Strong technical fluency with SQL, APIs, data models, automation/orchestration tooling, and modern AI-enabled development tools. You don’t need to be a full-time software engineer, but you should be comfortable building rather than only configuring. • Strong GTM and commercial judgment. You understand how sellers and customer teams operate, what information changes behavior, and the difference between a technically interesting system and one that actually helps move revenue. • The ability to structure ambiguous business problems and translate them into systems, processes, and operating models, not simply execute against a predefined requirements document. • Comfort reasoning about complex enterprise data models, including organizations with multiple sites, stakeholders, systems, and workflows. • High agency and comfort operating in ambiguity. There is no large RevOps or data organization to hand requirements to; you’ll work directly with the field, form a point of view, build, test, and iterate. Nice to Have • Experience working in an early-stage or high-growth environment where you helped establish systems before the organization scaled. • Experience in supply chain, logistics, manufacturing, or another complex, multi-site enterprise vertical. • Familiarity with a modern GTM stack spanning CRM, cloud data infrastructure, enrichment/orchestration, BI, and related tooling. • Experience building with or alongside AI agents, copilots, and LLM-enabled workflows rather than only using them as end-user tools. • Experience in strategy, consulting, or business operations that strengthened your ability to structure ambiguous GTM problems, paired with demonstrated technical building depth. This May Not Be the Role for You If • You primarily want to administer a CRM or manage a collection of SaaS tools rather than build systems and workflows yourself. • You enjoy data infrastructure but have little interest in how Sales and Customer Success actually operate or make decisions. • You prefer long build cycles and highly specified requirements over shipping, testing with users, and iterating quickly. • Your default response to a new GTM problem is to buy another tool before understanding whether we can solve it more simply or effectively ourselves. • You want a clearly bounded function with established processes, dedicated supporting teams, and a predefined roadmap. Why BackOps • Be our first AI GTM Strategy Manager and shape the systems, automation, and data foundation behind a rapidly scaling enterprise GTM motion • Work directly with GTM leadership and the field to turn real customer and seller bottlenecks into tools that create meaningful leverage • Backed by Insight Partners, Theory Ventures, Construct Capital, Gradient Ventures, and 10VC • Competitive salary, meaningful equity, and comprehensive benefits

Listed by BackOps AI for a position based in the United States. Employers on this board attest they are hiring domestically.

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