AI Jobs in San Mateo

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AI Field Engineers at Fireworks build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints. They architect inference foundations for customers whose core product is built on GenAI, size deployments to scale without infrastructure bottlenecks, run load tests, establish latency, throughput, and cost baselines, tune deployments, and deploy and validate new model families on inference frameworks while determining optimal configurations and serving patterns. They guide customers on model selection, fine-tuning strategy, and evaluation methodology, build and run fine-tuning pipelines with customers, design and implement evaluation frameworks measuring production-quality metrics, and lead structured discovery conversations to understand customer pain points and success criteria. They own the technical relationship from first engagement through production deployment, embedding with customer engineering teams to build trust, spend time on-site, translate customer pain points into product proposals, codify repeatable deployment patterns, and feed customer signals back into the product roadmap with specificity and urgency.

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AI Field Engineers at Fireworks embed with customers and technology partners to turn complex AI problems into production systems quickly. They build POCs, MVPs, and production integrations, and engage in executive-level conversations about architecture, strategy, and business outcomes. Responsibilities include shipping code, running benchmarks, debugging production issues, and architecting deployments. They lead discovery conversations, align stakeholders, and translate customer pain points into product improvements. Engineers work on building end-to-end POCs and MVPs inside customer codebases and infrastructure, architect inference foundations for GenAI core products, run load tests and tune deployments, deploy and validate new model families on inference frameworks, guide customers on model selection and fine-tuning strategies, build and run fine-tuning pipelines, and design evaluation frameworks. They manage customer engagement by leading discovery conversations, owning technical relationships, embedding with customer teams on-site, identifying recurring pain points, proposing product improvements, and codifying deployment patterns for internal use and platform improvement.

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Design agent systems from first principles including deciding the loop, tools, context strategy, evaluation harness, and system topology. Engineer the context by focusing on prompt construction, context windows, tool surfaces, structured outputs, and citation grounding. Drive evaluation rigor by building evaluations prior to agent construction, diagnosing failures, fixing root causes, and proving improvements through metrics. Use AI tooling such as Claude Code and Codex extensively to plan, scaffold, refactor, and debug work. Become a domain expert in healthcare claims, coding guidelines, and medical records as an integral part of the job.

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Build and maintain components of Clarium's master data enrichment pipeline, which classifies and enriches every product flowing through the platform; design and own classification and entity resolution workflows that combine deterministic logic and large language models (LLMs) for production data processing; build and operate evaluation harnesses, label sets, and regression suites to measure and improve pipeline quality; write production-level Python and SQL code; analyze complex datasets using statistics and machine learning to surface actionable insights and inform pipeline improvements; proactively audit data for quality issues, diagnose root causes, and implement fixes.

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Design and build extensible backend systems that support flexible configurations for different customers and content types. Develop infrastructure that interfaces cleanly with large language models (LLMs), enabling prompt engineering, context injection, and modular evaluation workflows. Build tooling and platforms that enable fast iteration by AI engineers and analysts, including declarative pipelines, parameterized jobs, and reproducible experiments. Prioritize ease of deployment, integration, and testing for both internal teams and external partners. Collaborate closely with product, data, and policy teams to translate nuanced safety needs into scalable, maintainable software systems.

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Contributors may generate prompts that challenge AI, evaluate AI-generated solutions for correctness, assumptions, and logic, improve AI reasoning to align with first principles and accepted standards, and apply structured scoring criteria to assess multi-step problem solving.

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Contributors may generate prompts that challenge AI, evaluate AI-generated solutions for correctness, assumptions, and logic, improve AI reasoning to align with first principles and accepted standards, and apply structured scoring criteria to assess multi-step problem solving.

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Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.

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Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.

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Contributors generate prompts that challenge AI, evaluate AI-generated solutions for correctness, assumptions, and logic, improve AI reasoning to align with first principles and accepted standards, and apply structured scoring criteria to assess multi-step problem solving.