Fireworks AI — AI Field Engineer

• →5–10 years in customer-facing ML/AI engineering (FDE, Applied AI, or Solutions Engineering) as a senior IC — ~60% hands-on coding/deployment, ~40% client engagement + product feedback [Required]

• Built and shipped production ML/AI systems from the ground up [Must have]
• Direct client-facing engineering experience — ran POCs/MVPs, managed accounts, presented to stakeholders [Must have]
• Archetype A: FDE/embedded engineer at AI-native startups or professional services firms — consulting/delivery focus [Required]
• Archetype B: Senior ML/AI engineer with training, fine-tuning, inference, or model deployment, plus demonstrated client-facing experience [Required]
• Background at an AI/ML startup/infra company, professional services firm, or big tech with client-facing exposure [Strongly preferred]

• Strong Python + hands-on ML/AI engineering depth [Must have]
• Experience with inference serving frameworks (vLLM, SGLang) and cloud GPU infrastructure (AWS, GCP, Azure) [Strongly preferred]
• Familiarity with Kubernetes [Strongly preferred]

• Collaborated with PMs to rapidly channel client feedback into product improvements [Must have]
• Low ego, extreme ownership [Required]

• Comfortable with regular on-site customer visits (AI-native accounts move at YC startup pace) [Required]

• Pure advisory/SA profiles who have never shipped code in a customer's production environment
• No AI/ML exposure on their resume — no open-source model experience, no GenAI features built or integrated
• Multiple job tenures under 1 year
• Pure Big Tech IC without any external exposure

Candidate salary$176K–$228K base (OTE $220K–$285K)EquityCompetitive equityOn-site policyUS-based; open to remote or in-office in New York, NY or San Mateo, CA; regular on-site customer travel expectedVisa sponsorshipH-1B transfers and TN visas sponsored; O-1 case-by-caseEmployment typeFull-timeLocationNew York, NY / San Mateo, CA / Remote, USA

1. Are you based in the US and comfortable with regular on-site travel to client offices?
2. Describe a time you built and shipped a POC or production integration directly inside a customer's codebase — what was the stack and what did you deliver?
3. What ML/AI systems have you built from the ground up? Walk me through the architecture and your specific contributions.
4. How have you taken client feedback and channeled it into product improvements internally?
5. What's your comfort level with ambiguity and fast context-switching across multiple accounts?
6. What is their salary expectation?
7. How actively is this candidate exploring new opportunities?

Updated Jun 22, 2026

For sourcing reference — these companies and adjacent companies are good starting points.

Ideal Companies Harvey, C3 AI

Forward-deployed / professional services engineering firms (explicitly mentioned by HM) Palantir Technologies, BCG X, C3.ai Digital Transformation Institute, Scale AI

AI-native startups and direct competitors with FDE or embedded engineering motions (explicitly mentioned by HM) Together AI, Baseten, Anyscale, Modal Labs, Replicated, Groq, Cohere, Perplexity AI, Harvey AI, Sierra Nevada Corporation

Big Tech with strong ML/AI engineering and some client-facing exposure (explicitly mentioned by HM) Google DeepMind, Meta AI, OpenAI, Anthropic, NVIDIA, Databricks, Snowflake, Hugging Face

AI-native inference, MLOps, and LLM infrastructure companies (highest-priority talent pool — deep open-model and serving framework experience) Together AI, Replicated, Modal Labs, Baseten, Anyscale, OctoAI, Groq, Cerebras, Mistral, Cohere

Hyperscaler AI platforms and cloud infrastructure with LLM/GPU deployment experience (Azure AI, AWS, GCP — in JD and intake) Microsoft, Google, Amazon Web Services (AWS), NVIDIA, AMD, Databricks, Snowflake, MongoDB

AI-native developer tools and production AI application companies (hands-on LLM integration + customer-facing field engineering) Cursor, Notion, Scale AI, Weights & Biases, Hugging Face, LangChain, Pinecone, Weaviate, Glean, Perplexity AI

Hyperscaler cloud solutions architects (HM: better fit for Enterprise role, not AI Natives) Amazon Web Services (AWS), Microsoft Azure DevOps

Pure closed-model API wrapper companies — engineers only work with OpenAI/Anthropic APIs, no open-model inference or fine-tuning (flagged disqualifying by David in intake) OpenAI, Anthropic, Jasper, Copy.ai, WRITER, Typeface

Traditional enterprise SaaS where AI is a bolt-on feature layer (candidates lack AI-native depth and open-model experience) Salesforce, ServiceNow, Workday Peakon Employee Voice, SAP, Oracle, HubSpot, Zendesk

For reference only — do not source these specific profiles.

Anthony Nguyen — LinkedIn AI Engineer | Coral Springs, United States

• Strong companies (C3 AI)
• C3 is a professional services firm — likely some client-facing exposure
• Worked with AI
• Areas for improvement: nothing on resume explicitly shows client-facing experience ("none of it seems to be client facing"); Ravi flagged him as looking like "a backend engineer"

Ameer Qamar — LinkedIn Building AI Voice Agents for CX | Toronto, Canada

• Resume was specific — tied buzzwords to actual projects/company work, not generic
• AI voice agent experience — relevant AI domain
• Cresta — a Fireworks client, so familiar with the context
• University of Waterloo — strong technical bar
• Areas for improvement: "Would interview him, but he's not top top. He's a pass, let's move it forward."

• None recorded yet.