A
Applied AI Engineer
AccrueTalent
1 hour ago
Full-time
On-site
San Francisco, California, United States
We're partnering with a fast-growing AI company building production-grade conversational systems for a highly regulated industry. They're hiring a Senior Applied AI Engineer to take ownership of the intelligence layer behind the product, with a clear path toward leading the AI function as the team grows.
This is not a research role. The team is looking for an applied builder who has shipped AI systems that real customers rely on, understands how to evaluate and improve model behavior, and can make strong technical decisions around models, prompts, fine-tuning, data, and system architecture. What You'll Work On
Own model selection and integration across production AI applications Build and improve real-time conversational AI systems Design evaluation frameworks to measure model and agent quality Fine-tune models using proprietary datasets and human feedback Develop prompt, orchestration, and experimentation strategies Build feedback loops that continuously improve system performance Create reliable data pipelines and APIs supporting AI workflows Monitor quality, latency, reliability, and user outcomes in production Partner closely with product and engineering to turn business requirements into AI capabilities Help define the long-term technical direction of the AI platform What We're Looking For 4-8 years of software / applied AI / ML engineering experience Strong Python and solid software engineering fundamentals Experience shipping AI or ML systems into production Hands-on experience with modern LLM frameworks and orchestration Experience building evals, running experiments, and improving models against measurable targets Exposure to fine-tuning or adapting models using proprietary data Strong understanding of system design, APIs, data pipelines, and production reliability Product mindset and ability to work across engineering and business teams
Clear communicator who can operate with significant autonomy Particularly Relevant Backgrounds
Strong candidates often come from: AI agent or conversational AI companies Voice AI, speech, or real-time media products Applied AI teams at frontier-model or developer-tool companies AI evaluation, observability, or model-infrastructure companies Founding engineering teams that have shipped LLM products from 0→1 AI products in regulated industries such as financial services, healthcare, or legal technology Strong Plus Production voice AI or telephony experience Built an evaluation harness from scratch Fine-tuned production LLMs on proprietary datasets RLHF / RLAIF or other model-feedback pipelines Startup or founding-engineer experience Experience building AI in regulated environments OSS contributions, technical writing, or talks related to applied AI
This is not a research role. The team is looking for an applied builder who has shipped AI systems that real customers rely on, understands how to evaluate and improve model behavior, and can make strong technical decisions around models, prompts, fine-tuning, data, and system architecture. What You'll Work On
Own model selection and integration across production AI applications Build and improve real-time conversational AI systems Design evaluation frameworks to measure model and agent quality Fine-tune models using proprietary datasets and human feedback Develop prompt, orchestration, and experimentation strategies Build feedback loops that continuously improve system performance Create reliable data pipelines and APIs supporting AI workflows Monitor quality, latency, reliability, and user outcomes in production Partner closely with product and engineering to turn business requirements into AI capabilities Help define the long-term technical direction of the AI platform What We're Looking For 4-8 years of software / applied AI / ML engineering experience Strong Python and solid software engineering fundamentals Experience shipping AI or ML systems into production Hands-on experience with modern LLM frameworks and orchestration Experience building evals, running experiments, and improving models against measurable targets Exposure to fine-tuning or adapting models using proprietary data Strong understanding of system design, APIs, data pipelines, and production reliability Product mindset and ability to work across engineering and business teams
Clear communicator who can operate with significant autonomy Particularly Relevant Backgrounds
Strong candidates often come from: AI agent or conversational AI companies Voice AI, speech, or real-time media products Applied AI teams at frontier-model or developer-tool companies AI evaluation, observability, or model-infrastructure companies Founding engineering teams that have shipped LLM products from 0→1 AI products in regulated industries such as financial services, healthcare, or legal technology Strong Plus Production voice AI or telephony experience Built an evaluation harness from scratch Fine-tuned production LLMs on proprietary datasets RLHF / RLAIF or other model-feedback pipelines Startup or founding-engineer experience Experience building AI in regulated environments OSS contributions, technical writing, or talks related to applied AI