We're looking for a Senior AI Product Manager to take ownership of core areas of our B2B platform - the point where powerful AI capabilities become real products that customers rely on. We've already built the foundation: rich data, agentic infrastructure, and deep domain intelligence. Now we need someone who can take what's working with one customer and make it work everywhere - productizing capabilities, scaling them across our customer base, and raising the quality bar as they grow.

This is a hands-on builder role. You'll work in close, direct partnership with both customers and R&D - running discovery, analyzing the data, and prototyping ideas yourself, then teaming up with R&D to bring them to life at scale.

This is a fast-moving area, and we'll shape the exact scope together, based on where you can create the most leverage for the business. We're looking for true startup reflexes: the ability to shift focus as priorities shift, zero in on what matters most this quarter, and make clear, deliberate trade-offs.

If you thrive in a fast-paced startup environment and want to build AI products that make complex insights accessible and actionable for a real-world, >$1B industry, this role is for you.

What You'll Do

Own core areas end to end - strategy, discovery, execution, and measurement. Define what success looks like in numbers, and stay accountable to it well beyond launch.
Productize and scale what works. Turn capabilities proven with one customer into products that work seamlessly across your entire customer base.
Lead discovery yourself. Run customer and prospect conversations to uncover what people will actually pay for, and bring back a scoped, evidenced bet.
Prototype your ideas. Go from concept to working prototype using coding agents and AI tooling, with a design bar high enough to serve as a real proposal.
Own quality. Define what "good" means for our agents, build the evals to measure it, and raise the bar on reliability, cost, and trust as the system evolves.
Partner deeply with R&D. Engage on technical trade-offs - accuracy, latency, cost, build vs. buy - and earn the team's respect through substance.
Drive it to market. Team up with design, sales, marketing, and customer success to package, position, and launch it, then feed adoption data back into the roadmap.

We're looking for an AI Tech Lead to own that standard across three surfaces:
The platform - Today each agent flow is close to a bespoke implementation. You'll turn our hard-won patterns into shared components, conventions, and infrastructure so the next agent is a week of work rather than a quarter - with evaluation, observability, and cost control built in rather than bolted on.
Enablement - Miggo's advantage compounds only if the whole company is AI-fluent, not just R&D. You'll raise that fluency everywhere - engineering, research, product, GTM - through tooling, patterns, and teaching.
The voice - You'll publish the methodology: how we benchmark agentic security output, how we model residual risk, what we learned failing. This is a category-defining position and we want it argued in public.
This is a hands-on lead role with no direct reports. Your authority comes from the quality of what you build and how clearly you explain i

Own the intelligence layer - the AI research pipeline that classifies and risk-scores every tool we find. Discovery tells us what software an organization runs; the AI does the hard part: figuring out what each tool actually is, what it can do, how it handles data, and how risky it is. You'll own that research and enrichment system end to end - the LLM-backed agents, the prompts and context that drive them, the evals that keep them honest, and the cost and latency of running them at scale.

This is an engineering role, not a research one. You'll ship production TypeScript, and you'll be measured on the accuracy, cost, and reliability of the intelligence the product depends on.

What you'll work on

The multi-agent researcher system: LLM-backed agents that research each tool across topics like platform, data policy, AI models, and agentic capabilities, and return structured, evidence-backed classifications.

Evals and quality: design eval sets, measure classification accuracy and hallucination, and turn prompt changes into regression-tested, reviewable diffs instead of guesswork.

Grounding and trust: cite evidence, resolve contradictions between AI output and validated data, and drive down hallucination on the fields that matter.

Model routing and cost/latency: choose and route across providers, tune concurrency and caching, and keep the pipeline fast and affordable as volume grows.

Structured outputs, tool/function calling, and the schemas and validation that make model output safe to persist.

Deep observability into the pipeline - spans, traces, and metrics for every model call.

We are looking for a Senior AI Engineer to design and build production-grade, LLM-powered systems. You'll work at the intersection of software engineering and applied AI - shipping agents, RAG pipelines, and tool-using systems that solve real problems at scale. This is a hands-on, high-ownership role for someone who thrives at the frontier of what's possible with modern LLMs and isn't afraid to write the glue, the infrastructure, and the prompts that make it all work.
This is a **cross-functional, company-wide role**. You won't be embedded in a single product team - instead, you'll partner with every department to identify high-leverage opportunities and build AI-powered tools and workflows that boost productivity and efficiency across the entire organization.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
What You'll Do:
- Design, build, and operate LLM-powered applications, agents, and workflows end-to-end - from prototype to production.
- Architect retrieval, context engineering, and tool-use strategies that make models reliable, accurate, and cost-efficient.
- Integrate LLMs with internal services, third-party APIs, and data stores to automate complex business and engineering workflows.
- Build, evaluate, and continuously improve evaluation harnesses for non-deterministic systems.
- Collaborate closely with product, research, and platform teams to translate ambiguous problems into shipped capabilities.
- Stay ahead of the rapidly evolving LLM ecosystem (models, frameworks, agentic patterns) and bring the best ideas into our stack.

We are looking for a Senior Software Engineer to join the AI Engineering team in a role focused on embedding directly with business teams to drive AI transformation. Be at the forefront of Applied AI. This role is outward-facing: you will engage with GTM and G&A functions - Sales, Marketing, Finance, HR, Legal, Customer Success - and fundamentally alter how these teams operate with AI.

What you will be doing:
Embed with GTM and G&A teams (Sales, Marketing, Finance, HR, Legal, Procurement, Customer Success) to identify, scope, and prioritize the highest-impact AI opportunities within each function, and act as both product manager and engineer for these initiatives.
Own the full lifecycle of each solution, from requirements gathering through deployment to adoption measurement and iteration, with a focus on business outcomes.
Serve as the primary technical partner and AI advisor for business function leaders, translating their needs into engineering plans and helping them develop their own AI fluency.
Develop and deliver hands-on AI enablement and training for non-R&D teams, building their ability to use AI tools independently and effectively.
Collaborate with the team to leverage shared infrastructure (MCP servers, RAG systems, evaluation frameworks, guardrails) while feeding back requirements from the field.
Measure and report on AI adoption impact - unlocking new opportunities, process improvements, and capability gains - to build the case for continued investment and to guide prioritization.

Were looking for a talented AI-native Software Engineer to join our innovative R&D team!
we help its customers acquire better users and spend less doing it - and our core team owns the engine behind it. We turn raw customer data into ad-network conversions, end-to-end: through large-scale ML and data pipelines, event-driven delivery services on AWS, and the UI that ties it together. There's no narrow lane here - you own problems wherever they live in the stack.
We're an AI-native team: we treat AI and agentic tooling as a first-class part of how we build, and it's how a small team credibly owns this much surface area. We're looking for a high-ownership generalist who works this way (or is hungry to), and who thrives in a correctness-critical domain where a bug means real customer ad-spend going the wrong way.
Responsibilities:
Turn raw customer data into ad-network conversions end to end, building across the whole stack: ML and data pipelines, delivery services, and the customer-facing UI.
Take features from idea to production largely on your own, designing, shipping, monitoring, and iterating, using AI and agentic tooling as a force multiplier, and owning your systems in production where mistakes translate directly into customer spend.
Collaborate closely with the team lead, product, and data scientists to take models and ideas from prototype to reliable, scaled-up production.

The company building an agentic development lifecycle, an infrastructure of autonomous agents that work alongside our engineers to accelerate and improve how we build software. Our goal is to ship faster, with higher quality, and to continuously tighten the feedback loop between what the agents produce and what engineering actually needs. Over time, this system should compound: every improvement makes the next one easier to reach.
we are an enterprise secure browser used by some of the largest organizations in the world. It's a complex, multidisciplinary product spanning browser core, frontend, extensions, and backend services, and it runs at scale for customers who need it to always work. The bar for what we ship is high. That means whatever agentic infrastructure we build has to meet the same standard. We're not here to vibe code our way to production.
We're looking for an AI Engineer with a product builder's mindset. You have real experience with AI and agentic workflows, and you know how to take a complex project from idea to adoption, technically and organizationally. That means working across teams, aligning with security, infrastructure, and other engineering groups, and understanding that building the system is only half the job. Getting people to trust it is the other half.
We aren't looking for a conventional senior developer; we need someone whose mindset is adapted to technical challenges that didn't even exist 18 months ago.

we are building an agentic development lifecycle, an infrastructure of autonomous agents that work alongside our engineers to accelerate and improve how we build software. Our goal is to ship faster, with higher quality, and to continuously tighten the feedback loop between what the agents produce and what engineering actually needs. Over time, this system should compound: every improvement makes the next one easier to reach.
we are an enterprise secure browser used by some of the largest organizations in the world. It's a complex, multidisciplinary product spanning browser core, frontend, extensions, and backend services, and it runs at scale for customers who need it to always work. The bar for what we ship is high. That means whatever agentic infrastructure we build has to meet the same standard. We're not here to vibe code our way to production.
We're looking for an AI Engineer with a product builder's mindset. You have real experience with AI and agentic workflows, and you know how to take a complex project from idea to adoption, technically and organizationally. That means working across teams, aligning with security, infrastructure, and other engineering groups, and understanding that building the system is only half the job. Getting people to trust it is the other half.
We aren't looking for a conventional senior developer; we need someone whose mindset is adapted to technical challenges that didn't even exist 18 months ago.

Required AI Data Engineer
About the role:
We're building the intelligence layer that lets everyone ask hard questions of our data and get trustworthy answers - in plain language, in seconds. As AI Data Engineer, you own the semantic and AI-native surface of our Snowflake platform: the governed semantic layer that defines "what a metric means," the Cortex Agents that let business users query it conversationally, and the infrastructure that keeps all of it fast, reliable, and ready to scale.
This is a builder-owner role. You'll ship the semantic models, wire up the agents, and set the standard for how analysts across the company work with data. You'll also lead the Data Analyst guild - the connective tissue that keeps our hub-and-spoke analytics model coherent as it grows.
If you're excited by the space where data engineering, LLM tooling, and analytics governance meet, this role sits right in the middle of it.
What youll Own:
Snowflake semantic layer - Own the semantic layer end-to-end as the single source of truth for metrics; design semantic models, enforce naming standards, and ensure consistent metric definitions across dashboards and AI agents.
Cortex Agents - Design and deploy conversational AI agents using Cortex Analyst and Cortex Search; tune for accuracy and safety, expose through multiple surfaces (Snowflake Intelligence, Streamlit, MCP), and build evaluation harnesses to maintain quality at scale.
Data craft (Analytics guild) - Co-lead the technical track of the Analytics guild; set SQL and modeling standards, run technical enablement and code reviews, and serve as the technical authority for analysts.
Scaling data infrastructure - Improve performance, reliability, cost efficiency, and governance across the dbt/Airflow/Airbyte/Snowflake stack as data volume and query load grow; optimize warehouse sizing, medallion layers, and ingestion pipelines.
What you'll do day to day:
Model and maintain semantic views that power both dashboards and AI agents, keeping definitions versioned, tested, and certified.
Build, evaluate, and iterate on Cortex Agents - including retrieval quality, guardrails, and observability.
Extend and optimize dbt models, Airflow DAGs, and Airbyte connectors across bronze/silver/gold layers.
Partner with GTM, Finance, CS, and Product stakeholders to translate business questions into governed, reusable data assets.
Run the Data Analyst guild: standards, reviews, enablement, and tooling.
Own data quality, lineage, and cost monitoring across the Snowflake platform.
Expose data and agents through Streamlit apps, BI tools (Omni), and MCP servers for internal AI workflows.

The same systems work as our senior role, aimed at someone earlier in their career who gets real work done in a hard codebase with agentic tools. Systems programming has a reputation for being locked behind ten years of experience. We don't fully buy that. Someone with solid fundamentals, real curiosity about how machines work, and deep control of agentic coding tools can contribute here much sooner - as long as they have the judgment to check what the tools hand back. That judgment matters more here than almost anywhere: the agent runs as root and SYSTEM, and the codebase forbids unwrap, panic, and unchecked indexing, so you can't ship code you don't actually understand.

What you'll work on

Real pieces of the sensor across macOS and Windows: inventory, telemetry, the policy enforcement path, installers, and the integrations that hook into AI coding tools and browser extensions.

Moving through unfamiliar OS APIs quickly with agentic tools, then checking the result against the docs, the tests, and a real machine.

Small tools and tests that make the codebase easier for everyone to work in.

Tech you'll work with

The work is low-level systems development in Rust, close to the operating system across Windows, macOS, and Linux - memory, processes, files, and the OS internals that expose them. Alongside that, you'll lean hard on agentic coding tools; deep fluency with at least one is central to how we work, not a bonus. Expect to range across the stack - OS internals, AI tooling internals, CI, and a fair amount of reverse engineering - and to learn the parts you don't know yet.