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NEXTMOVEFDE careers · United States
Adobe

Senior Applied Scientist

AI summary of the role

This Machine Learning Engineer role on the Adobe Brand Intelligence Predict team builds LLM-powered synthetic audience systems that let brands pre-test ads and campaigns.

No longer in the current catalog. Last included 2026-09-09. Check the employer’s posting for availability.

What you’ll do

  • Design, build, and ship LLM-powered systems that simulate consumer audiences end-to-end, from proof-of-concept to production.
  • Develop complex inference and reasoning harnesses on top of frontier LLMs, agentic flows, persona conditioning, retrieval, and sampling strategies.
  • Fine-tune LLMs on survey, panel, and behavioral data to improve alignment with real-world audience distributions.
  • Build evaluation datasets, benchmarks, and harnesses for synthetic audience quality metrics.

What you’ll bring

  • Substantial hands-on experience building LLM-based applications in production.
  • Demonstrated experience designing and shipping complex inference harnesses on top of large language models (agentic systems, structured reasoning, sampling/decoding strategies, RAG).
  • Hands-on experience fine-tuning LLMs with SFT, preference optimization (DPO/GRPO), and modern post-training tradeoffs.
  • Experience with RLHF, RLAIF, or RL-based state alignment of LLMs.

Technologies

LLM · PyTorch · Hugging Face · vLLM · W&B · AWS · GCP · Azure · RAG · RLHF

About Adobe

Builds creative, document, and enterprise customer-experience software, now embedding Firefly AI and agents across creator and marketer workflows.

Public · 5000+ people

Source and classification

Internal deployment & tooling · Evidence for this classification:

Machine Learning Engineer — Brand Intelligence Predict The Opportunity Join us at Adobe as a Machine Learning Engineer (MLE 50) on the Adobe Brand Intelligence Predict team in San Jose, CA! Help us build the next generation of synthetic audiences, LLM-powered simulated consumers that let the world's biggest brands pre-test ads, campaigns, and content before a single dollar is spent. What you'll Do Design, build, and ship LLM-powered systems that simulate consumer audiences end-to-end, from proof-of-concept to production. Develop complex inference and reasoning harnesses on top of frontier LLMs, agentic flows, persona conditioning, retrieval, and sampling strategies tuned for distributional fidelity. Fine-tune LLMs on survey, panel, and behavioral data to improve alignment with real-world audience distributions; own the full loop from data curation through eval. Build the
More from the job description

Machine Learning Engineer — Brand Intelligence Predict The Opportunity Join us at Adobe as a Machine Learning Engineer (MLE 50) on the Adobe Brand Intelligence Predict team in San Jose, CA! Help us build the next generation of synthetic audiences, LLM-powered simulated consumers that let the world's biggest brands pre-test ads, campaigns, and content before a single dollar is spent. What you'll Do Design, build, and ship LLM-powered systems that simulate consumer audiences end-to-end, from proof-of-concept to production. Develop complex inference and reasoning harnesses on top of frontier LLMs, agentic flows, persona conditioning, retrieval, and sampling strategies tuned for distributional fidelity. Fine-tune LLMs on survey, panel, and behavioral data to improve alignment with real-world audience distributions; own the full loop from data curation through eval. Build the evaluation datasets, benchmarks, and harnesses that define what "good" means for synthetic audience quality - distributional fidelity, behavioral validity, subgroup calibration. Partner with product management, applied science, and engineering to translate a fast-moving research literature into shipping product features. What you need to succeed Substantial hands-on experience building LLM-based applications in production. Demonstrated experience designing and shipping complex inference harnesses on [... source excerpt omitted ...] tooling (PyTorch, Hugging Face, vLLM, W&B or equivalents). Hands-on knowledge of MLOps practices and pipelines. Familiarity with cloud ML services (AWS, GCP, Azure). Shipped a customer-facing Gen AI feature from proof-of-concept to production end-to-end. MS or PhD or equivalent experience in Computer Science, Machine Learning, or a related technical field, or equivalent experienc Nice to have Prior work on synthetic audiences, persona simulation, or LLM-based human behavior modeling. Familiarity with the synthetic audiences research literature (e.g., silicon samples, generative agents, SubPOP, HumanLM, DeepBind). Experience with public opinion or survey data (GSS, ANES, [... source excerpt omitted ...] mulating audiences of LLM-powered synthetic consumers, brands can pre-test creative, messaging, and product concepts in minutes against statistically grounded models of their real customers. We sit at the frontier of one of the most actively-evolving areas in applied AI. The synthetic audiences research field has moved from "what if we asked GPT to play the ultimatum game?" in 2022 to competing paradigms — prompt-based persona binding, supervised fine-tuning on survey distributions, and RL-based latent state alignment — in 2026. No one has won yet. Our mission is to bring that science into product, and to do it at the speed and quality bar of a startup inside the company whose p

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