AI Deployment Strategist, Fireworks AI
$170-200k
Offers equity
Generative AI platform enabling rapid scaling for developers and businesses
Open for applications
Generative AI platform enabling rapid scaling for developers and businesses
101-200 employees
Open for applications
$170-200k
Offers equity
101-200 employees
Company mission
To help every organization unleash the full power of artificial intelligence.
Role
Who you are
- 4+ years in a technical, customer-facing role: Deployment Strategy, Technical Account Management, Solutions Engineering, Forward-Deployed Engineering, or Technical Customer Success at an enterprise software or AI/ML company
- Strong technical foundation: comfortable with APIs, cloud infrastructure (AWS/GCP/Azure), and enough hands-on coding ability to debug integrations, not just describe them
- Direct experience with LLMs in production — understanding of probabilistic model behavior, prompt engineering, fine-tuning, RAG, and/or agent architectures
- Track record of managing enterprise relationships end-to-end: technical delivery, executive communication, and commercial outcomes (renewal/expansion)
- Excellent written and verbal communication — able to translate between ML engineers and business stakeholders fluently
- Comfortable owning ambiguity in a fast-moving, early-stage function; you'll be shaping the playbook for this role at Fireworks, not just following one
- Experience scoping high-value engagements — value-based/TCO-ROI framing, defining pilot success criteria, writing scoping docs or PRDs — gained in a Deployment Strategist, Forward-Deployed Engineer, or similar "product manager for the field" capacity is a strong plus
- Bonus: experience with inference optimization, model serving infrastructure, or open-source model ecosystems (Llama, Mixtral, DeepSeek, etc.)
What the job involves
- This is a hybrid technical/commercial role
- You'll need enough depth to debug a customer's inference pipeline, discuss quantization tradeoffs, or advise on model routing and fine-tuning strategy — and enough business judgment to run a QBR, spot expansion opportunity, and manage renewal risk before it becomes a problem
- Think of it as "product manager for the customer's problem": you own the why and the what — scoping the business case, defining success criteria, and managing the relationship end-to-end
- You're embedded with your accounts from early technical evaluation through production and beyond
- Deployment Strategy & Value Scoping
- Partner with Sales during late-stage deals and early post-sale to map the customer's technical and organizational landscape — who the real stakeholders are, what "success" looks like, and where the risk sits
- Quantify the business case for the deployment (cost-to-serve, latency/quality targets, ROI vs. the customer's current approach) and define a scoped pilot with clear milestones and exit criteria
- Author the internal scoping brief that AI Field Engineering builds against — you own the "why" and "what," they own the "how"
- Onboarding & Technical Deployment
- Own the post-sale technical relationship from kickoff through go-live, including model deployment, integration architecture, SSO/security configuration, and performance benchmarking
- Partner with AI Field Engineering to deliver the successful transition from PoC to production
- Build and execute joint success plans with clear milestones, ownership, and timelines
- Trusted Advisor & Adoption
- Serve as the primary technical point of contact for a portfolio of strategic accounts, building deep relationships with engineering leaders, ML/platform teams, and power users
- Advise customers on model selection, fine-tuning, prompt engineering, latency/cost optimization, and agent architecture as their usage matures
- Drive usage against business objectives — not just technical enablement, but measurable outcomes (latency SLAs, cost per token, model quality, uptime)
- Troubleshooting & Escalation Management
- Act as the technical escalation point for production issues, coordinating with Engineering and Support to drive resolution
- Maintain runbooks and playbooks that reduce time-to-resolution across the account portfolio
- Voice of the Customer
- Synthesize patterns across your accounts and feed them into product and engineering roadmaps
- Represent customer priorities in internal planning, particularly around model support, tooling gaps, and platform reliability
- Track a tight feedback loop between what customers are building and what Fireworks ships next
- Expansion, Renewal & Business Reviews
- Own the technical narrative for quarterly/executive business reviews (QBRs/EBRs), including adoption trends, ROI, and roadmap alignment
- Partner with the Account Executive on renewal strategy and identify expansion opportunities tied to new use cases, teams, or workloads
- Forecast and proactively flag account health risks before they threaten retention
Our take
Whilst the development of generative AI is thought to be the reserve of large companies like Open AI, smaller startups such as Firework AI are making this technology easier to access and develop for smaller companies wanting to handle specific business needs using AI. As an open-source model API, it has one of the largest user bases - with over 12,000 users leveraging Fireworks AI’s software.
Though lacking the brand name recognition of generative AI developers like Google and Meta, Fireworks AI differentiates itself in a market crowded with large competitors through its unique approach to deploying its software. It creates smaller, production-grade, customizable generative AI models that can be launched privately and securely, rather than the non-private and inflexible models built by larger companies.
After its 2025 Series C round of $250M, Fireworks AI has been valued at over $4B, an impressive sum for a David in a sea of Goliaths. The company plans to use its funding to reshape AI infrastructure as well as expand globally. It will be interesting to see how the company evolves with its potentially disruptive business approach to generative AI.
Freddie
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 3 rounds)
Oct 2025
$230m
SERIES C
Jul 2024
$52m
SERIES B
Total funding: $307m
Company HQ
Redwood City, CA
Leadership
Lin Qiao
(Co-Founder & CEO)Previously a Senior Director of Engineering at Meta, a Tech Lead at Linkedin, a Research Staff Member at IBM Almaden Research Center and an Advisory Software Engineer at IBM Silicon Valley Lab.
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