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We're hiring a Product Operations Manager , Feedback Loops to own and continuously improve how customer signal flows into product and research decisions at Anthropic. This is a horizontal, org-wide role , you won't be embedded in a single product team, you'll build the shared operating system for voice of the customer that every product team, every surface, and every GTM motion plugs into.

\n

Feedback at Anthropic is uniquely high-leverage. We're building on frontier models that evolve constantly, serving customers from individual developers to the largest enterprises, across multiple surfaces (API, claude.ai, Claude Code). Customer signal arrives from everywhere , field conversations, support interactions, early access programs, in-product telemetry , and the opportunity is to make that signal a first-class, structured input to every product and research decision.

\n

This role will build the system that makes customer voice as easy to act on as any other data source. You treat feedback loops as a product. You're obsessed with making it effortless for the field to share what they're hearing and for product teams to know what matters most. You build AI-enabled systems that do the first pass so humans can focus on judgment, not triage. You think like a product manager, not a process administrator.

\n

Your work will directly impact how fast Anthropic learns from its customers and how reliably that learning shapes what we build next.

\n

Key Responsibilities

\n

You'll own the operating system for customer feedback across all of Anthropic , one shared platform, not a collection of per-team processes. Working horizontally across every Product team, Research PM, GTM, Customer Success, and Support, you'll establish the intake, synthesis, and routing infrastructure that makes voice of the customer a first-class input to every roadmap.

\n

You'll drive adoption through influence, making it so obviously useful that teams pull from it rather than get pushed to it.

\n

Feedback Intake & System of Record

\n

Own the single, org-wide pipeline that captures customer feedback from every channel , field teams, support, early access programs, in-product signals , into one structured system of record that serves every product surface.

\n

Build intake workflows that meet teams where they already work (Slack, Gong, CRM) without creating a documentation tax. Obsess over the submitter experience so that sharing feedback is faster than not sharing it.

\n

AI-Enabled Synthesis & Triage

\n

Build Claude-powered pipelines that enrich, tag, cluster, and summarize unstructured feedback into trackable issues , doing the first-pass work so humans focus on verification and judgment.

\n

Design the human-in-the-loop model: Claude proposes, PMs and field teams correct, and the system learns from those corrections over time.

\n

Partner with Engineering and Research on tooling strategy, evals, and the closed-loop data that makes synthesis quality measurably improve.

\n

Routing & Closing the Loop

\n

Establish clear routing so the right feedback reaches the right product or research owner at the right time , including the path from product signal back into model training priorities.

\n

Build the visibility layer that gives GTM and Support a clear line of sight from customer input to roadmap outcome, so they can close the loop with customers confidently and in real time.

\n

Voice of the Customer Programs

\n

Partner deeply with GTM, Customer Success, and Sales to design and run structured voice of the customer programs , customer advisory boards, early access programs, design partner cohorts , that generate high-signal feedback by design.

\n

Define what 'high-signal' means: feedback tied to specific use cases, blocker severity, revenue context, and customer segments so product teams can make confident tradeoffs.

\n

Continuous Improvement

\n

Define and track success metrics for feedback loop health , time-to-triage, signal quality, roadmap influence, field satisfaction , and use them to identify bottlenecks.

\n

Run regular retros with Product and GTM partners and feed learnings back into process and tooling improvements. Scale what works through documentation and enablement.

\n

You may be a good fit if you:

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Have 7+ years in product operations, customer insights, voice of the customer programs, or related roles in fast-paced tech companies.

\n

Have personally shipped AI-enabled processes and systems , you've written the prompts, built the evals, and iterated on production LLM workflows yourself. You can talk about model behavior with specificity, not just direct others to build.

\n

Have owned a customer feedback program end-to-end , intake, synthesis, routing, and closing the loop , that product teams actually used to make decisions. The customer mix can be enterprise, PLG, design partner, or dev community; what matters is that you designed it and ran it.

\n

Have operated at earlier-stage and scaling companies (Series B-D or equivalent) where you built things that didn't exist yet, shipped v1s in weeks not quarters, and iterated in public.

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Have operated in horizontal, cross-org roles before , you know how to build shared infrastructure that many teams depend on, drive adoption through influence rather than mandate, and earn trust across functions that don't report to you.

\n

Are comfortable with ambiguity and can create structure where none exists , you've built the v1 of a system and iterated it into something teams rely on.

\n

Are service-oriented and obsessed with making it easy for others to do great work.

\n

Strong candidates may also have experience with:

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Building AI-native workflows end-to-end , prompt design, evals, closed-loop improvement , and pushing the boundaries of what automation can own.

\n

Product Management, Customer Success Operations, or Research Operations.

\n

Feedback tooling ecosystems (Productboard, Dovetail, or homegrown equivalents) and the tradeoffs between buy vs. build.

\n

Treating process as a product with users, metrics, and continuous iteration.

\n

Track record of building and scaling operations programs from zero to one.

\n

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At Cloudflare, we're on a mission to help build a better Internet. We're looking for a highly motivated Legal Operations Analyst to join our growing team in the Legal, Policy, and Trust & Safety department. In this role, you will be responsible for optimizing and scaling our contract lifecycle management (CLM) processes, and advancing the LPT department's technology roadmap.

\n

We are seeking a highly organized and technically skilled individual with hands-on experience designing processes, implementing, managing, and integrating legal systems with enterprise platforms. The ideal candidate will have a proven track record of evaluating technology, driving automation initiatives, and delivering measurable improvements in legal workflows.

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As a Legal Operations Analyst, you will serve as a co-administrator for Ironclad, managing legal workflows, templates, clause libraries, reporting, and user access. You will facilitate end-to-end contract processes, track milestones, and ensure timely approvals. You will also help pilot, implement, and optimize AI and automation tools to streamline legal workflows and enhance productivity.

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Responsibilities:

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Requirements:

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Bonus:

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Compensation:

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What Makes Cloudflare Special?

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As a Staff Backend Engineer (Python) on the Duo Chat team in AI Engineering, you'll lead the backend architecture that powers GitLab Duo Chat across the GitLab DevSecOps platform.

\n

You'll solve hard problems in building reliable, secure, and scalable AI-powered chat workflows so customers can plan, write, review, and secure code faster, with confidence.

\n

This is a hands-on technical leadership role where you'll set direction for how we integrate and evolve large language model providers (including Google Vertex AI) across Ruby on Rails and Python services, raise the bar on observability and testing, and guide the team through ambiguous, high-impact technical decisions.

\n

Over your first year, you'll be expected to drive key architectural choices, reduce technical debt that slows iteration, and help the team ship durable improvements to response quality, reliability, and maintainability.

\n

Some examples of our projects:

\n\n\n

Responsibilities

\n\n\n\n\n\n\n\n\n

Requirements

\n\n\n\n\n\n\n\n\n

About the Team

\n

The Duo Chat team sits within GitLab's AI Engineering organization and is responsible for building and evolving GitLab Duo Chat, the AI-powered chat experience embedded across the GitLab DevSecOps platform.

\n

You'll work with a small, cross-functional group of backend, frontend, and AI specialists who collaborate asynchronously across time zones, using GitLab issues, merge requests, and documentation as the primary way of working.

\n

The team focuses on integrating and scaling generative AI capabilities (including providers like Google Vertex AI), improving reliability and performance, and strengthening debugging, observability, and testing workflows so customers can safely use AI to plan, write, review, and secure their code across GitLab.

\n

How GitLab Supports Full-Time Employees

\n\n\n\n\n\n\n\n

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We are seeking a Senior Software Engineer to join our NYC Engineering office. As one of the first engineers in the office, you will have the opportunity to be part of a small, nimble team that's innovating to build new products from the ground up. Our goal is to leverage the power of Databricks in data & AI to deliver vertical AI applications for both technical and business users.

\n

The impact you'll have:

\n\n

What we look for:

\n\n

Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above.

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Local Pay Range $166,000-$225,000 USD

\n

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We're hiring a Product Operations Manager , Feedback Loops to own and continuously improve how customer signal flows into product and research decisions at Anthropic.

\n

This is a horizontal, org-wide role , you won't be embedded in a single product team, you'll build the shared operating system for voice of the customer that every product team, every surface, and every GTM motion plugs into.

\n

Feedback at Anthropic is uniquely high-leverage. We're building on frontier models that evolve constantly, serving customers from individual developers to the largest enterprises, across multiple surfaces (API, claude.ai, Claude Code).

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Customer signal arrives from everywhere , field conversations, support interactions, early access programs, in-product telemetry , and the opportunity is to make that signal a first-class, structured input to every product and research decision.

\n

You treat feedback loops as a product. You're obsessed with making it effortless for the field to share what they're hearing and for product teams to know what matters most.

\n

You build AI-enabled systems that do the first pass so humans can focus on judgment, not triage. You think like a product manager, not a process administrator.

\n

Your work will directly impact how fast Anthropic learns from its customers and how reliably that learning shapes what we build next.

\n

Key Responsibilities

\n

You'll own the operating system for customer feedback across all of Anthropic , one shared platform, not a collection of per-team processes.

\n

Working horizontally across every Product team, Research PM, GTM, Customer Success, and Support, you'll establish the intake, synthesis, and routing infrastructure that makes voice of the customer a first-class input to every roadmap.

\n

You'll drive adoption through influence, making it so obviously useful that teams pull from it rather than get pushed to it.

\n

Feedback Intake & System of Record

\n\n\n

AI-Enabled Synthesis & Triage

\n\n\n\n

Routing & Closing the Loop

\n\n\n

Voice of the Customer Programs

\n\n\n

Continuous Improvement

\n\n\n

You may be a good fit if you:

\n\n\n\n\n\n\n\n

Strong candidates may also have experience with:

\n\n\n\n\n\n

Annual compensation range for this role is $260,000-$325,000 USD

\n

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Join Razer on a global mission to revolutionize the way the world games. As a Senior Data Analyst, you will be responsible for conducting data analysis, building dashboards and reports, and supporting business domains like business performance, product optimization, and helping drive business insights across teams.

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Job Responsibilities

\n\n

Pre-Requisites

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Benefits

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We are looking for a Senior Product Manager to drive Copilot model capabilities such as tool-use to ensure that the language models that power Microsoft Copilot deliver high quality responses to our users whilst being grounded, reliable, and cost-efficient.

\n

As a Senior Product Manager, you will work at the nexus of product and research, driving execution in partnership with engineers, language engineers, data scientists and researchers. You will develop and execute on LLM platform strategy for Copilot that extend language model's capabilities. You will prototype approaches by steering language models to drive response quality across a wide range of scenarios. You will identify and prioritise platform, orchestration and language model issues that impact quality, factuality and safety and working with engineers and researchers to find a path to resolution.

\n

You will define and build measurable evaluations with relevant datasets to demonstrate quality improvements. You will define, deploy and manage experiments in production that impact language model's tool use, driving measurable improvements in relevance for and engagement with Copilot users. You will partner with product teams to scale tool building and work with inference, agents and orchestration teams to resolve dependencies. You will be accountable to own the status of key projects, proactively identifying risks and proposing solutions to ensure timely delivery.

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Responsibilities include:

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Summary

\n

Microsoft AI are looking for a talented Senior Product Manager, Copilot AI at their Mountain View office. This role sits at the heart of strategic decision-making, turning market data into actionable insights for a company that's revolutionising AI technology. You'll work directly with leadership to shape the company's direction in the AI landscape.

\n

About the Role

\n

As a Senior Product Manager, you will drive Copilot model capabilities such as tool-use to ensure that the language models that power Microsoft Copilot deliver high quality responses to our users whilst being grounded, reliable, and cost-efficient. You will work at the nexus of product and research, driving execution in partnership with engineers, language engineers, data scientists and researchers.

\n

Accountabilities

\n\n

The Candidate we're looking for

\n

Experience:

\n\n

Technical skills:

\n\n

Personal attributes:

\n\n

Benefits

\n\n

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