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Software Engineer, Agent Infrastructure
Sapiom · San Francisco, California, United States
$140k–205k
8 days ago
♡
Staff Software Engineer, AI Platform
Sapiom · San Francisco, California, United States
$150k–250k
35 days ago
♡
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Software Engineer, Agent Infrastructure
Sapiom · San Francisco, California, United States
Pay
$140k–205k
Setting
On-site
About Sapiom
Sapiom is the end-to-end platform that removes barriers to ship and scale agentic products.
We unify what an agent needs to act in the world — compute and sandboxes, memory, identity, spend controls, storage and queues, monitoring — provisioned together as one thing, not handed over as a framework to assemble yourself.
We have assembled a world-class team with deep infrastructure and payments DNA to build the operating system for machines. Our founder ran payments engineering at Shopify for five years and built an autonomous consumer agent company before that. We raised a $35M Series A led by Dragonfly in August, bringing us to $50M total. Accel led our seed; Menlo Ventures and Anthropic are also behind us.
The models are the brain. We're the spine.
ABOUT THE ROLE
Agents can think now. They still can't act — not economically, not reliably, not with anyone in control. Teams build a great demo in days, then find that running it in production is brutally hard: it breaks when nobody's watching, the bill arrives before the explanation, and nobody can reconstruct what happened. Closing that gap is the whole company.
We're a flat org. Nobody has a lane, and what you work on will change as the problems change — you might be deep in routing one month and in metering the next. That's not disorganization; it's what a company this early looks like when the architecture is still being set.
We hire at two levels: engineers one to three years in, and Staff. There's no layer between you and the people setting the architecture, which is why someone this early gets real surface area here. It also means your design decisions get questioned by engineers who have made these mistakes before, which is the fastest way to get good at this.
WHAT WE'RE WORKING ON
- Routing and capacity. Every model call has to be placed against latency, cost, and quality targets in real time, and the scheduling, reservations, and forecasting underneath have to keep those decisions honest as demand shifts. Getting this right is what makes running agents economical at all.
- Metering and billing correctness. Two charging layers, hold and capture semantics, and 270M+ consumption events that all have to reconcile — in a ledger and in a usage number a customer is reading right now. Metering is the product here, not a feature, which means the correctness bar is unusually high.
- Reliability at sustained scale. We've taken a 10–20x increase that kept compounding week over week for months. The open question is what reliability should mean here, and what observability, incident practice, and architecture get us ahead of the growth rather than reacting to it.
- The control plane. Credential-scoped permissions, workflow state limits, gateway hardening. These are the decisions that determine how much authority a customer can safely hand an agent — largely unsettled, and consequential.
- Agent execution. Sandboxed runs, state, memory, tools, and the external integrations agents depend on, at tens of thousands of runs a day.
YOU MAY BE A FIT IF
- You have strong engineering fundamentals and write good code quickly.
- You've shipped something that ran in production and that you were responsible for when it broke — at a job, an internship, or a project of your own.
- You take problems from "this should exist" to shipped without much structure around you.
- You move between backend, infrastructure, and tooling as the problem requires, rather than staying where you're most comfortable.
- You make reasonable calls with incomplete information and revise them when you learn more.
- You close gaps fast — you'd rather learn the thing you're missing than route around it.
- You use AI tools in your own engineering workflow — not instead of judgment, but as a multiplier.
- Nice to have: experience with distributed systems, queues, storage, or observability; systems that talk to a lot of third-party APIs; LLM inference or agent architectures; anything you've built and operated yourself at real scale, including side projects.
If this reads like a stretch, apply anyway. At this stage we care more about how fast you close gaps than which ones you've already closed.
APPLYING
A recruiter screen, then a technical screen. If those go well, a three-part loop: an architecture deep dive, a hands-on AI project, and a conversation with our founder.
Everyone on our engineering team carries the title Member of Technical Staff internally. We post by level so the scope is clear.
Listed by Sapiom for a position based in the United States. Employers on this board attest they are hiring domestically.
Staff Software Engineer, AI Platform
Sapiom · San Francisco, California, United States
Pay
$150k–250k
Setting
On-site
About Sapiom
Sapiom is the end-to-end platform that removes barriers to ship and scale agentic products.
We unify everything an agent needs to act in the world: compute and sandboxes, memory, identity, domains and DNS, spend controls, browser automation, web search and deep research, databases, storage, queues, messaging, image generation, voice, enrichment, verification, and monitoring provisioned together as one thing, not handed over as a framework for builders to assemble themselves. Pricing is just as simple: a plan, a generous free tier, pay for what you use when you use it.
We have assembled a world-class team with deep infrastructure and payments DNA to build the operating system for machines. Backed by $55M in capital from Dragonfly, Accel, Menlo, and Anthropic, we are moving with relentless focus to allow builders to ship and scale agentic products.
ABOUT THE ROLE
As a Staff AI Platform Engineer, you'll help define the technical direction of the platform from the ground up. You'll architect distributed systems that power AI agents in production, establish engineering best practices, and partner closely with leadership to shape both the technology and the company.
This is an opportunity to join early and have an outsized impact on the architecture, culture, and product strategy of an AI infrastructure company.
WHAT YOU'LL DO
- Architect the core platform that powers AI agents in production.
- Design distributed systems for agent orchestration, execution, memory, tool calling, workflow coordination, and communication.
- Build reliable infrastructure that enables AI agents to operate safely and efficiently across enterprise environments.
- Lead the technical design of foundational platform capabilities, balancing performance, reliability, extensibility, and developer experience.
- Drive architecture decisions across backend systems, APIs, infrastructure, and AI runtime services.
- Establish engineering standards for scalability, observability, security, and operational excellence.
- Partner with product and engineering leadership to translate emerging AI capabilities into production-ready platform features.
- Mentor engineers through design reviews, technical guidance, and hands-on collaboration.
- Lead complex, cross-functional initiatives from concept through production.
WE'RE LOOKING FOR SOMEONE WHO HAS
- 8+ years of experience building large-scale backend systems, distributed systems, or developer platforms.
- Experience operating technical leadership at the Staff or Principal Engineer level, or demonstrated equivalent scope and impact.
- Strong programming skills in Python, Go, Typescript, or similar languages.
- Experience building developer platforms, SDKs, or API-first products that prioritize reliability, scalability, and developer experience.
- Experience working with modern AI systems, including LLMs, tool calling, structured outputs, retrieval, or agentic workflows.
- Strong systems thinking with the ability to simplify complex technical problems.
- A track record of driving technical strategy while remaining hands-on.
- Experience with event-driven architectures, workflow engines, or distributed execution systems.
NICE TO HAVE
- Experience building production AI agent platforms or orchestration frameworks.
- Familiarity with LangGraph, OpenAI Agents SDK, CrewAI, AutoGen, Temporal, or similar orchestration technologies.
- Experience with vector databases, retrieval systems, and knowledge infrastructure.
- Experience with LLM serving technologies such as vLLM, SGLang, or TensorRT-LLM.
- Contributions to open-source infrastructure or AI projects.
WHY SAPIOM
We're building the infrastructure that will power the next generation of AI applications.
Rather than building a single AI product, we're creating the platform developers use to build intelligent, autonomous systems that can coordinate work, integrate with enterprise software, and execute complex workflows reliably.
As one of the earliest senior engineers, you'll have significant influence over our architecture, engineering culture, and product direction. You'll help define the technical foundation of a company solving some of the hardest problems in production AI.
Listed by Sapiom for a position based in the United States. Employers on this board attest they are hiring domestically.
Staff Software Engineer, Agent Infrastructure
Sapiom · San Francisco, California, United States
Pay
$140k–250k
Setting
On-site
About Sapiom
Sapiom is the end-to-end platform that removes barriers to ship and scale agentic products.
We unify everything an agent needs to act in the world: compute and sandboxes, memory, identity, domains and DNS, spend controls, browser automation, web search and deep research, databases, storage, queues, messaging, image generation, voice, enrichment, verification, and monitoring provisioned together as one thing, not handed over as a framework for builders to assemble themselves. Pricing is just as simple: a plan, a generous free tier, pay for what you use when you use it.
We have assembled a world-class team with deep infrastructure and payments DNA to build the operating system for machines. Backed by $55M in capital from Dragonfly, Accel, Menlo, and Anthropic, we are moving with relentless focus to allow builders to ship and scale agentic products.
About the Role
This is a high-autonomy engineering role at the intersection of AI, infrastructure, and distributed systems.
You’ll design and build the core systems that power Sapiom’s agent platform — the infrastructure that enables agents to access tools, interact with the web, manage state and memory, execute workflows, and operate reliably in production.
The problems are broad and technically challenging. You might work on orchestration and execution systems one day, platform infrastructure or developer tooling the next, and reliability, observability, or integrations after that. We’re looking for engineers who enjoy operating at this level of ambiguity and can move from architecture → implementation → production without needing a lot of structure around them.
You’ll work closely with the founding team and have a meaningful role in shaping both the technology and product as Sapiom evolves.
AI-native development is also a core part of how we work. We use AI tools throughout the engineering workflow and expect engineers to do the same — not as a replacement for engineering judgment, but as a force multiplier.
WHAT YOU’LL DO
- Own end-to-end delivery of critical platform and infrastructure systems, from architecture through production
- Design and build reliable systems that allow AI agents to execute complex, multi-step workflows
- Build infrastructure for agent execution, orchestration, state, memory, tools, integrations, and external services
- Make pragmatic architectural decisions in ambiguous and rapidly changing environments
- Work across backend, infrastructure, platform, and developer tooling as the problems demand
- Identify and solve the reliability, scalability, performance, and operational challenges that come with running agents in production
- Partner closely with the founding team to shape product and engineering direction
- Establish patterns and systems that allow the engineering organization to move faster as the platform scales
- Use AI-native development tools extensively to accelerate development, debugging, and problem solving
REQUIREMENTS
- Strong software engineering fundamentals and exceptional coding ability
- Experience designing and building distributed, highly reliable systems
- High agency and a strong sense of ownership — you naturally take problems from “this should exist” to production
- Systems thinker who enjoys solving ambiguous, technically difficult problems
- Comfortable operating across different layers of the stack
- Strong architectural judgment and ability to make pragmatic tradeoffs
- Bias toward shipping, iterating, and learning rather than over-engineering
- Excited about the challenges of building infrastructure for AI agents
- Comfortable working in a fast-moving startup environment with significant autonomy
- Actively uses AI-native development tools as part of your engineering workflow
NICE TO HAVE
- Experience building infrastructure, platforms, or developer tools
- Experience with distributed systems, orchestration, or workflow engines
- Experience building systems that interact with external APIs, services, or infrastructure
- Familiarity with AI/ML systems, LLMs, inference, or agent architectures
- Experience with databases, queues, messaging, storage, or observability infrastructure
- Experience at a high-growth startup or early-stage company
- Experience in payments or financial infrastructure
- Experience working on highly autonomous engineering teams
Listed by Sapiom for a position based in the United States. Employers on this board attest they are hiring domestically.
Software Engineer, Data Infrastructure
Sapiom · San Francisco, California, United States
Pay
$140k–205k
Setting
On-site
About Sapiom
Sapiom is the end-to-end platform that removes barriers to ship and scale agentic products.
We unify everything an agent needs to act in the world: compute and sandboxes, memory, identity, domains and DNS, spend controls, browser automation, web search and deep research, databases, storage, queues, messaging, image generation, voice, enrichment, verification, and monitoring provisioned together as one thing, not handed over as a framework for builders to assemble themselves. Pricing is just as simple: a plan, a generous free tier, pay for what you use when you use it.
We have assembled a world-class team with deep infrastructure and payments DNA to build the operating system for machines. Backed by $55M in capital from Dragonfly, Accel, Menlo, and Anthropic, we are moving with relentless focus to allow builders to ship and scale agentic products.
About the Role
This is a foundational infrastructure role at a company where the data layer isn't a back-office function — it's the nervous system of a payments platform processing every agent transaction, policy decision, and risk signal in real time. The right person thrives on ownership, has strong opinions about data quality and governance, and moves with the urgency of someone who knows that bad data costs more than bad code. As an early data engineer, you'll define not just the pipelines but the standards, architecture, and culture of data at Sapiom.
What You Will Do
You'll own Sapiom's data infrastructure end-to-end — designing and scaling ETL pipelines, defining schemas that survive 10x growth, and building the governance and quality frameworks that make data trustworthy across the company. You'll architect standardized data models that enable self-serve AI-powered insights, giving Analytics, Data Science, and product teams the visibility they need to move fast without coming to you for every query. The mandate is broad: pipelines, quality, security, observability, and the cross-functional partnerships that keep it all running.
Responsibilities
- Build, scale, and optimize production-quality ETL pipelines — owning the full lifecycle from ingestion through availability, with clear quality and SLA standards
- Design data schemas and architect for scale — anticipating 10x data growth and building models that don't require rework when it arrives
- Own data quality, governance, security, and schema design across the platform — setting the standards and making sure they hold
- Develop standardized, self-serve data models that enable AI-powered analytics — reducing friction for partner teams and eliminating one-off data pulls
- Instrument pipeline observability and surface key health metrics to Analytics, Data Science, and DevOps — proactively surfacing issues before they become incidents
- Partner closely with Data Science, Analytics, and DevOps — operating as a force multiplier across teams, not a bottleneck
Requirements
- Demonstrated track record — 5+ years — transforming raw data into governed, well-documented, production-ready datasets that business teams can trust and use
- Deep hands-on experience building and deploying production data pipelines using SQL, Python, Spark, AWS Glue, EMR, DBT, and Airflow
- Strong command of MPP databases — Snowflake, AWS Redshift, or Teradata — with 3+ years of hands-on production use
- Proven partnership record with Engineering, Analytics, Data Science, and DevOps teams — someone who treats cross-functional relationships as core to the job, not peripheral to it
- Architectural instincts — able to design schemas and systems that scale gracefully, not just handle today's load
- Comfort operating in an on-call rotation — including incident response outside regular working hours when the pipeline demands it
- Clear communicator who can translate complex data infrastructure decisions into plain-language insights for both technical and non-technical stakeholders
Listed by Sapiom for a position based in the United States. Employers on this board attest they are hiring domestically.
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