Principal Engineer, AI Systems

Location: Remote / U.S. (PST preferred)

Experience: Principal (10+ years)

Who we are

At Omada.ai, we're redefining how small and mid-sized businesses create, launch, and scale their marketing, powered by AI.

Our platform blends human-centered design with intelligent automation, giving brands access to AI-driven creative agents that produce videos, graphics, campaigns, and insights in real time. Behind every one of those experiences is a system of agents, pipelines, and services that has to be fast, reliable, and able to scale.

If you want to set the technical direction for an AI-first product, and be accountable for how it holds up as we grow, this is where you'll want to be.

The Role

We're looking for a Principal Engineer, AI Systems, to be the technical leader for the platform behind Alpha and our AI marketing team.

You'll set the architecture and technical strategy for agent orchestration, tool and model harnesses, event-driven pipelines, and the real-time APIs that stream results to our users, and you'll own how that architecture evolves as usage, team size, and model capabilities change. You'll partner directly with the founders and product leadership on what we build and how, raise the bar for every engineer across our pods, and stay hands-on in the code for the hardest problems. We're past the first prototype and scaling fast: you'll decide what to keep, what to rebuild, and how to get there without slowing the business down.

Responsibilities

Define the technical vision and multi-year architecture roadmap for Omada's AI platform, and align engineering, product, and leadership around it.
Own end-to-end architecture across services: service boundaries, data models, event flows, multi-tenancy, and failure handling, with clear plans for how each evolves at 10x scale.
Set the standards for how our AI agents are built and run: orchestration patterns, tool calling, guardrails, model routing, evaluation frameworks, and cost/latency budgets.
Make and document high-leverage technical decisions, including build vs. buy, model and vendor selection, and platform trade-offs, through clear RFCs and design docs.
Establish reliability, observability, and cost targets (SLOs, error budgets, unit economics per AI workflow) and the instrumentation and on-call practices that hold us to them.
Design for bursty, long-running AI workloads on serverless and event-driven AWS (Lambda, Step Functions, DynamoDB, SQS, EventBridge), with idempotency, back-pressure, and graceful degradation as defaults.
Drive security and data protection across the platform: tenant isolation, secrets management, and safe handling of customer data in LLM pipelines.
Lead design reviews, set engineering standards for code quality, testing, and API design, and lead high-severity incidents and the retrospectives that follow.
Mentor senior engineers, grow future technical leaders, and play a central role in hiring and leveling for engineering.
Stay hands-on: prototype new architectures, write production code on critical paths, and unblock teams on the hardest technical problems.
Track the frontier in agent architectures, LLM tooling, and cloud infrastructure, and decide which advances are worth adopting and when.

What we're really screening for

Architecture at scale, and how it evolves

You've owned the architecture of a complex distributed system through significant growth. You can explain not just how you designed it, but what you'd change now, how you migrated it while it stayed live, and which trade-offs aged well. Expect to go deep on this with us.

AI platform depth

You've led production systems built around LLMs: agent loops, tool-calling frameworks, orchestration layers, evaluation pipelines, or streaming inference. You have strong opinions, backed by experience, on how to make AI systems reliable, measurable, and cost-efficient as they scale.

Technical leadership and influence

You've set technical direction across multiple teams or pods, mostly through influence rather than authority. Your design docs change minds, engineers seek out your reviews, and the people you've mentored have grown into senior and staff roles.

Serverless & event-driven AWS mastery

You've designed and operated large systems on Lambda, Step Functions, DynamoDB, SQS, and EventBridge, and you know where they break, what they really cost at scale, and when to reach for something else.

Startup judgment, 0→1 and 1→N

You've taken systems from nothing to shipped and then scaled them. You know when to move fast and when to invest in foundations, and you can make that call clearly with incomplete information.

Requirements

10+ years of experience building and operating production-grade backend systems, including 3+ years as a staff, principal, or lead engineer setting technical direction across multiple teams.
Expert-level Python, including async programming, packaging, testing, performance tuning, and modern frameworks (FastAPI or similar).
Required: deep production experience designing and operating systems on AWS Lambda, Step Functions, DynamoDB, SQS, and EventBridge at meaningful scale, including cost optimization.
A track record of shipping and running LLM-powered systems in production, including evaluation, prompt and model versioning, model routing, and cost/latency management.
Demonstrated ownership of a system's architecture through 10x or greater growth in traffic, data, or team size.
Expert understanding of API design and real-time protocols (REST, SSE, WebSockets), including versioning strategy and backward compatibility across clients.
Experience defining SLOs and building observability practices with tooling such as CloudWatch, X-Ray, OpenTelemetry, or Datadog.
Experience setting infrastructure-as-code (CDK, Terraform, or SAM) and CI/CD standards for an engineering organization.
Strong grounding in security and multi-tenant data isolation for SaaS platforms.
Exceptional written and verbal communication: you can write an RFC that aligns engineers, product, and executives.
Degree in Computer Science or a related field (or equivalent experience).

Bonus Points

Deep experience with agent frameworks, MCP, or building evaluation systems for LLM output.
Experience with media processing pipelines (video, image, or audio generation), or with GPU-backed and self-hosted inference.
Experience as a founding engineer or early technical leader at a venture-backed startup.
Open-source work, talks, or writing on distributed systems or AI infrastructure that others in the field rely on.

What you'll love about working here

Set the technical direction for AI and creative technology used by thousands of SMBs.
Work directly with the founders and shape the platform, the engineering culture, and the team for years to come.
Freedom to experiment and ship fast in a startup environment that values creativity and impact.
Competitive compensation, flexible remote work, and opportunities for rapid growth as we scale.

How to apply

Email careers@omada.ai with your resume, a short cover letter, and, most importantly, links to systems you've led. Tell us about the hardest architecture decision you've made: what you chose, what you rejected, and how it played out. A design doc, architecture write-up, or open-source project that shows how you think will get our attention faster than anything else.

Principal Engineer, AI Systems

Location: Remote / U.S. (PST preferred)

Experience: Principal (10+ years)

Who we are

At Omada.ai, we're redefining how small and mid-sized businesses create, launch, and scale their marketing, powered by AI.

Our platform blends human-centered design with intelligent automation, giving brands access to AI-driven creative agents that produce videos, graphics, campaigns, and insights in real time. Behind every one of those experiences is a system of agents, pipelines, and services that has to be fast, reliable, and able to scale.

If you want to set the technical direction for an AI-first product, and be accountable for how it holds up as we grow, this is where you'll want to be.

The Role

We're looking for a Principal Engineer, AI Systems, to be the technical leader for the platform behind Alpha and our AI marketing team.

You'll set the architecture and technical strategy for agent orchestration, tool and model harnesses, event-driven pipelines, and the real-time APIs that stream results to our users, and you'll own how that architecture evolves as usage, team size, and model capabilities change. You'll partner directly with the founders and product leadership on what we build and how, raise the bar for every engineer across our pods, and stay hands-on in the code for the hardest problems. We're past the first prototype and scaling fast: you'll decide what to keep, what to rebuild, and how to get there without slowing the business down.

Responsibilities

Define the technical vision and multi-year architecture roadmap for Omada's AI platform, and align engineering, product, and leadership around it.
Own end-to-end architecture across services: service boundaries, data models, event flows, multi-tenancy, and failure handling, with clear plans for how each evolves at 10x scale.
Set the standards for how our AI agents are built and run: orchestration patterns, tool calling, guardrails, model routing, evaluation frameworks, and cost/latency budgets.
Make and document high-leverage technical decisions, including build vs. buy, model and vendor selection, and platform trade-offs, through clear RFCs and design docs.
Establish reliability, observability, and cost targets (SLOs, error budgets, unit economics per AI workflow) and the instrumentation and on-call practices that hold us to them.
Design for bursty, long-running AI workloads on serverless and event-driven AWS (Lambda, Step Functions, DynamoDB, SQS, EventBridge), with idempotency, back-pressure, and graceful degradation as defaults.
Drive security and data protection across the platform: tenant isolation, secrets management, and safe handling of customer data in LLM pipelines.
Lead design reviews, set engineering standards for code quality, testing, and API design, and lead high-severity incidents and the retrospectives that follow.
Mentor senior engineers, grow future technical leaders, and play a central role in hiring and leveling for engineering.
Stay hands-on: prototype new architectures, write production code on critical paths, and unblock teams on the hardest technical problems.
Track the frontier in agent architectures, LLM tooling, and cloud infrastructure, and decide which advances are worth adopting and when.

What we're really screening for

Architecture at scale, and how it evolves

You've owned the architecture of a complex distributed system through significant growth. You can explain not just how you designed it, but what you'd change now, how you migrated it while it stayed live, and which trade-offs aged well. Expect to go deep on this with us.

AI platform depth

You've led production systems built around LLMs: agent loops, tool-calling frameworks, orchestration layers, evaluation pipelines, or streaming inference. You have strong opinions, backed by experience, on how to make AI systems reliable, measurable, and cost-efficient as they scale.

Technical leadership and influence

You've set technical direction across multiple teams or pods, mostly through influence rather than authority. Your design docs change minds, engineers seek out your reviews, and the people you've mentored have grown into senior and staff roles.

Serverless & event-driven AWS mastery

You've designed and operated large systems on Lambda, Step Functions, DynamoDB, SQS, and EventBridge, and you know where they break, what they really cost at scale, and when to reach for something else.

Startup judgment, 0→1 and 1→N

You've taken systems from nothing to shipped and then scaled them. You know when to move fast and when to invest in foundations, and you can make that call clearly with incomplete information.

Requirements

10+ years of experience building and operating production-grade backend systems, including 3+ years as a staff, principal, or lead engineer setting technical direction across multiple teams.
Expert-level Python, including async programming, packaging, testing, performance tuning, and modern frameworks (FastAPI or similar).
Required: deep production experience designing and operating systems on AWS Lambda, Step Functions, DynamoDB, SQS, and EventBridge at meaningful scale, including cost optimization.
A track record of shipping and running LLM-powered systems in production, including evaluation, prompt and model versioning, model routing, and cost/latency management.
Demonstrated ownership of a system's architecture through 10x or greater growth in traffic, data, or team size.
Expert understanding of API design and real-time protocols (REST, SSE, WebSockets), including versioning strategy and backward compatibility across clients.
Experience defining SLOs and building observability practices with tooling such as CloudWatch, X-Ray, OpenTelemetry, or Datadog.
Experience setting infrastructure-as-code (CDK, Terraform, or SAM) and CI/CD standards for an engineering organization.
Strong grounding in security and multi-tenant data isolation for SaaS platforms.
Exceptional written and verbal communication: you can write an RFC that aligns engineers, product, and executives.
Degree in Computer Science or a related field (or equivalent experience).

Bonus Points

Deep experience with agent frameworks, MCP, or building evaluation systems for LLM output.
Experience with media processing pipelines (video, image, or audio generation), or with GPU-backed and self-hosted inference.
Experience as a founding engineer or early technical leader at a venture-backed startup.
Open-source work, talks, or writing on distributed systems or AI infrastructure that others in the field rely on.

What you'll love about working here

Set the technical direction for AI and creative technology used by thousands of SMBs.
Work directly with the founders and shape the platform, the engineering culture, and the team for years to come.
Freedom to experiment and ship fast in a startup environment that values creativity and impact.
Competitive compensation, flexible remote work, and opportunities for rapid growth as we scale.

How to apply

Email careers@omada.ai with your resume, a short cover letter, and, most importantly, links to systems you've led. Tell us about the hardest architecture decision you've made: what you chose, what you rejected, and how it played out. A design doc, architecture write-up, or open-source project that shows how you think will get our attention faster than anything else.

Principal Engineer, AI Systems

Location: Remote / U.S. (PST preferred)

Experience: Principal (10+ years)

Who we are

At Omada.ai, we're redefining how small and mid-sized businesses create, launch, and scale their marketing, powered by AI.

Our platform blends human-centered design with intelligent automation, giving brands access to AI-driven creative agents that produce videos, graphics, campaigns, and insights in real time. Behind every one of those experiences is a system of agents, pipelines, and services that has to be fast, reliable, and able to scale.

If you want to set the technical direction for an AI-first product, and be accountable for how it holds up as we grow, this is where you'll want to be.

The Role

We're looking for a Principal Engineer, AI Systems, to be the technical leader for the platform behind Alpha and our AI marketing team.

You'll set the architecture and technical strategy for agent orchestration, tool and model harnesses, event-driven pipelines, and the real-time APIs that stream results to our users, and you'll own how that architecture evolves as usage, team size, and model capabilities change. You'll partner directly with the founders and product leadership on what we build and how, raise the bar for every engineer across our pods, and stay hands-on in the code for the hardest problems. We're past the first prototype and scaling fast: you'll decide what to keep, what to rebuild, and how to get there without slowing the business down.

Responsibilities

Define the technical vision and multi-year architecture roadmap for Omada's AI platform, and align engineering, product, and leadership around it.
Own end-to-end architecture across services: service boundaries, data models, event flows, multi-tenancy, and failure handling, with clear plans for how each evolves at 10x scale.
Set the standards for how our AI agents are built and run: orchestration patterns, tool calling, guardrails, model routing, evaluation frameworks, and cost/latency budgets.
Make and document high-leverage technical decisions, including build vs. buy, model and vendor selection, and platform trade-offs, through clear RFCs and design docs.
Establish reliability, observability, and cost targets (SLOs, error budgets, unit economics per AI workflow) and the instrumentation and on-call practices that hold us to them.
Design for bursty, long-running AI workloads on serverless and event-driven AWS (Lambda, Step Functions, DynamoDB, SQS, EventBridge), with idempotency, back-pressure, and graceful degradation as defaults.
Drive security and data protection across the platform: tenant isolation, secrets management, and safe handling of customer data in LLM pipelines.
Lead design reviews, set engineering standards for code quality, testing, and API design, and lead high-severity incidents and the retrospectives that follow.
Mentor senior engineers, grow future technical leaders, and play a central role in hiring and leveling for engineering.
Stay hands-on: prototype new architectures, write production code on critical paths, and unblock teams on the hardest technical problems.
Track the frontier in agent architectures, LLM tooling, and cloud infrastructure, and decide which advances are worth adopting and when.

What we're really screening for

Architecture at scale, and how it evolves

You've owned the architecture of a complex distributed system through significant growth. You can explain not just how you designed it, but what you'd change now, how you migrated it while it stayed live, and which trade-offs aged well. Expect to go deep on this with us.

AI platform depth

You've led production systems built around LLMs: agent loops, tool-calling frameworks, orchestration layers, evaluation pipelines, or streaming inference. You have strong opinions, backed by experience, on how to make AI systems reliable, measurable, and cost-efficient as they scale.

Technical leadership and influence

You've set technical direction across multiple teams or pods, mostly through influence rather than authority. Your design docs change minds, engineers seek out your reviews, and the people you've mentored have grown into senior and staff roles.

Serverless & event-driven AWS mastery

You've designed and operated large systems on Lambda, Step Functions, DynamoDB, SQS, and EventBridge, and you know where they break, what they really cost at scale, and when to reach for something else.

Startup judgment, 0→1 and 1→N

You've taken systems from nothing to shipped and then scaled them. You know when to move fast and when to invest in foundations, and you can make that call clearly with incomplete information.

Requirements

10+ years of experience building and operating production-grade backend systems, including 3+ years as a staff, principal, or lead engineer setting technical direction across multiple teams.
Expert-level Python, including async programming, packaging, testing, performance tuning, and modern frameworks (FastAPI or similar).
Required: deep production experience designing and operating systems on AWS Lambda, Step Functions, DynamoDB, SQS, and EventBridge at meaningful scale, including cost optimization.
A track record of shipping and running LLM-powered systems in production, including evaluation, prompt and model versioning, model routing, and cost/latency management.
Demonstrated ownership of a system's architecture through 10x or greater growth in traffic, data, or team size.
Expert understanding of API design and real-time protocols (REST, SSE, WebSockets), including versioning strategy and backward compatibility across clients.
Experience defining SLOs and building observability practices with tooling such as CloudWatch, X-Ray, OpenTelemetry, or Datadog.
Experience setting infrastructure-as-code (CDK, Terraform, or SAM) and CI/CD standards for an engineering organization.
Strong grounding in security and multi-tenant data isolation for SaaS platforms.
Exceptional written and verbal communication: you can write an RFC that aligns engineers, product, and executives.
Degree in Computer Science or a related field (or equivalent experience).

Bonus Points

Deep experience with agent frameworks, MCP, or building evaluation systems for LLM output.
Experience with media processing pipelines (video, image, or audio generation), or with GPU-backed and self-hosted inference.
Experience as a founding engineer or early technical leader at a venture-backed startup.
Open-source work, talks, or writing on distributed systems or AI infrastructure that others in the field rely on.

What you'll love about working here

Set the technical direction for AI and creative technology used by thousands of SMBs.
Work directly with the founders and shape the platform, the engineering culture, and the team for years to come.
Freedom to experiment and ship fast in a startup environment that values creativity and impact.
Competitive compensation, flexible remote work, and opportunities for rapid growth as we scale.

How to apply

Email careers@omada.ai with your resume, a short cover letter, and, most importantly, links to systems you've led. Tell us about the hardest architecture decision you've made: what you chose, what you rejected, and how it played out. A design doc, architecture write-up, or open-source project that shows how you think will get our attention faster than anything else.