Job description

Job Description – Forward Deployed Engineer – Language Models Location: San Francisco, CA (on-site, 5 days a week, in our San Francisco office) Or Melbourne, Australia (on-site, 3 days a week, in our Melbourne office) About Artificial Analysis Artificial Analysis is the leading independent AI benchmarking company. We support labs, engineers and enterprises to understand AI capabilities and make critical decisions about their AI strategies. We are the go-to authority for understanding AI, from AI labs and enterprises to media, investors, and policymakers. Our benchmarks don’t just measure the cutting edge of AI, they are actively shaping the frontier. Our benchmarks and analysis are trusted by hundreds of thousands of users and are the go-to reference for leading AI labs including OpenAI, Google, Meta, NVIDIA and Anthropic, and major publications including the Wall Street Journal, Bloomberg, the Financial Times and The Economist. We are a team of 40+, on track to double by end of year, backed by Nat Friedman (GitHub, Meta), Daniel Gross (SSI, Meta), Andrew Ng (Google Brain, DeepLearning.ai deeplearning.ai, Amazon), Adam D’Angelo (Quora, Poe, OpenAI), Clem Delangue (Hugging Face) and other industry leaders. The Opportunity Artificial Analysis maintains one of the most comprehensive language model benchmarking suites in the industry, evaluating frontier models across quality, speed, and pricing for the AI labs and enterprises that rely on our data. We’re hiring a Forward Deployed Engineer to own the day-to-day operation of our language model benchmarking stack and act as the technical face of Artificial Analysis to the industry’s most important labs. You’ll onboard new models to our evaluation pipeline, run and debug benchmarks, and serve as the primary technical point of contact for AI lab customers: explaining results, fielding methodology questions, and resolving endpoint issues in real time. This is a deployed role in the truest sense: you sit at the interface between our platform and our most sophisticated customers, accountable for both sides working. It is about running a sophisticated stack exceptionally well, consistently and reliably, while being the trusted engineer our customers ask for by name. What You’ll Do • Operate and maintain our Python-based language model benchmarking pipeline end-to-end: onboard new models, configure evaluations, execute benchmark runs, and validate results • Debug issues across the stack, from API endpoint timeouts and errors to unexpected benchmark outputs, and resolve them quickly • Serve as the primary technical contact for AI lab customers: communicate benchmarking results clearly, explain methodology, field technical questions, and troubleshoot integration issues via Slack and video conferencing • Monitor benchmark runs for anomalies, investigate discrepancies, and ensure the accuracy and integrity of published results • Maintain documentation of processes, known issues, and model-specific configurations • Collaborate with the engineering team to flag pipeline improvements and contribute to process refinements • Stay current with new model releases, API changes, and developments across the language model ecosystem What We’re Looking For Required: • 3+ years of experience in a client-facing technical role - solutions engineering, support engineering, technical consulting, or similar (companies like Stripe, Vercel, Cloudflare, Datadog, Palantir, Accenture, or comparable) • Strong Python proficiency and comfort working with complex codebases you didn’t write • Hands-on experience working with AI/ML model APIs (OpenAI, Anthropic, Google, Meta, etc.) • Excellent debugging skills; you can trace issues across APIs, data pipelines, and code • Strong written and verbal English communication skills, with the ability to explain technical concepts clearly to technical stakeholders • Highly responsive and reliable; you take ownership of customer issues and follow through • Comfortable with operational, repeatable work; you find satisfaction in running things exceptionally well • High attention to detail and calm under pressure Nice to have (not required): • Experience with AI evaluation, benchmarking, or testing methodologies • Familiarity with LLM inference infrastructure (tokenization, latency measurement, throughput metrics) • Experience working in or with AI labs or model providers • Background in B2B SaaS or developer tools Why Artificial Analysis? • Shape how AI gets built: The leading AI labs track our benchmarks and use them to guide their development priorities. Your work will directly influence the direction of AI. • Become a world expert in AI: You will evaluate every major model, across every major capability, as they are released. Very few roles offer this breadth of exposure to frontier AI. • Work with the most important players in AI: You’ll manage relationships with teams at the leading AI labs and major enterprises as a trusted, independent voice. • Join at a defining moment: We’re 40+ people, on track to double by end of year, backed by some of the most connected investors in AI. The people who join now will shape the product, the team, and the strategy as we scale. • Competitive compensation including equity

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Job Description – Forward Deployed Engineer – Language Models
Location: San Francisco, CA (on-site, 5 days a week, in our San Francisco office)
Melbourne, Australia (on-site, 3 days a week, in our Melbourne office)
What You’ll Do
Operate and maintain our Python-based language model benchmarking pipeline end-to-end: onboard new models, configure evaluations, execute benchmark runs, and validate results
Debug issues across the stack, from API endpoint timeouts and errors to unexpected benchmark outputs, and resolve them quickly
Serve as the primary technical contact for AI lab customers: communicate benchmarking results clearly, explain methodology, field technical questions, and troubleshoot integration issues via Slack and video conferencing
Monitor benchmark runs for anomalies, investigate discrepancies, and ensure the accuracy and integrity of published results
Maintain documentation of processes, known issues, and model-specific configurations
Collaborate with the engineering team to flag pipeline improvements and contribute to process refinements
Stay current with new model releases, API changes, and developments across the language model ecosystem
What We’re Looking For
Strong Python proficiency and comfort working with complex codebases you didn’t write
Hands-on experience working with AI/ML model APIs (OpenAI, Anthropic, Google, Meta, etc.)
Excellent debugging skills; you can trace issues across APIs, data pipelines, and code
Strong written and verbal English communication skills, with the ability to explain technical concepts clearly to technical stakeholders
Highly responsive and reliable; you take ownership of customer issues and follow through
Comfortable with operational, repeatable work; you find satisfaction in running things exceptionally well
High attention to detail and calm under pressure
Experience with AI evaluation, benchmarking, or testing methodologies
Familiarity with LLM inference infrastructure (tokenization, latency measurement, throughput metrics)
Experience working in or with AI labs or model providers
Background in B2B SaaS or developer tools
Why Artificial Analysis?

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