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About Us:
At Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta Py Torch and Google Vertex AI.
The Role
We are looking for a Data Platform Engineer that specializes in Order-to-Cash (OTC) Revenue Transformation and AI Application Enablement to own and evolve the end-to-end billing, revenue and business data pipeline - from usage metering and invoice generation through revenue recognition and financial reporting. You will sit at the intersection of Engineering, Finance, and Data, ensuring every dollar of usage across our five revenue streams is accurately captured, billed, recognized, and reconciled.
This is a high-impact, cross-functional role. You will work hands-on with our billing platform (Orb, etc), accounting systems , data warehouse (Big Query), and cloud marketplaces (AWS, GCP) — and ultimately help design AI-enabled workflow agents that automate reconciliation, anomaly detection, and revenue operations once the core data infrastructure is hardened.
What You'll Do
Phase 1 – Platform & Data Foundation
- Own and enhance billing infrastructure: pricing models, usage ingestion, invoicing, and revenue workflows.
- Resolve key platform gaps (pricing flexibility, account hierarchy, overages, prepaid/credit logic).
- Integrate billing with ERP, payments, and cloud marketplaces for an automated invoice-to-ledger pipeline.
- Implement deferred revenue and prepaid amortization across all billing models.
- Build and maintain an end-to-end OTC data pipeline (usage → billing → payments → revenue → GL → reporting).
- Establish authoritative data models and ensure transaction-level reconciliation across all systems.
- Implement data quality, auditability, and SOX-ready controls.
- Strengthen CRM → Billing → ERP integration as a single source of truth.
- Automate journal entries, AR sub-ledger, and revenue postings; integrate payments and marketplace settlements.
Phase 2 – Autonomous Intelligence
- Build and deploy autonomous enterprise agents to automate and augment OTC operations, including anomaly detection, reconciliation, revenue recognition, collections, contract interpretation, and forecasting.
What We're Looking For
Preferred
- 5+ years in billing engineering, revenue systems, or order-to-cash operations at a SaaS or usage-based platform company.
- Experience with Billing Systems, ERP, AWS, K8, etc
- Strong SQL and Big Query proficiency — you can design schemas, write complex analytical queries, build dbt models, and maintain production data pipelines.
- Working knowledge of accounting systems (Quick Books or Net Suite) and the ability to map billing events to GL journal entries, manage sub-ledger reconciliation, and support month-end close.
- Experience with payment platforms including payment processing, dunning, refunds, and cash application.
- Familiarity with cloud marketplace billing — AWS Marketplace CPPO/SaaS contracts, GCP Marketplace, or Azure Marketplace private offers and settlement reporting.
- Proficiency in Python or Node.js for building integrations, data transforms, and automation scripts.
- Experience building LLM-powered agents or automation workflows — using frameworks like Lang Chain, or custom tool-calling architectures.
- Background in GPU compute or AI infrastructure billing — understanding of compute-hour metering, token-based pricing, and capacity reservation models.
- Experience with ERP migration projects (e.g., Quick Books to Net Suite).
Total compensation for this role also includes meaningful equity in a fast-growing startup, along with a competitive salary and comprehensive benefits package. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range is intended as a guideline and may be adjusted.
Base Pay Range (Plus Equity)
$175,000—$220,000 USD
Why Fireworks AI?
- Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
- Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.
- Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results.
- Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.
Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
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About Fireworks AI

Fireworks AI
Series AFireworks AI provides generative AI inference and fine-tuning platform for developers and enterprises. The company offers high-performance API services for running large language models and other generative AI workloads.
51-200
Employees
San Francisco
Headquarters
$1.2B
Valuation
Reviews
3.8
26 reviews
Work-life balance
3.5
Compensation
4.2
Culture
3.8
Career
4.0
Management
3.6
79%
Recommend to a friend
Pros
Supportive team and management
Opportunity for career growth
Interesting projects and challenges
Cons
Internal communication could improve
Career progression could be clearer
Work-life balance varies by team
Salary Ranges
0 data points
Senior
Senior · Sales
0 reports
$241,200
total per year
Base
-
Stock
-
Bonus
-
$204,020
$278,380
Interview experience
43 interviews
Difficulty
3.1
/ 5
Duration
14-28 weeks
Offer rate
41%
Experience
Positive 60%
Neutral 21%
Negative 19%
Interview process
1
Phone Screen
2
Technical Interview
3
Hiring Manager
4
Team Fit
Common questions
Technical skills
Past experience
Team collaboration
Problem solving
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