Senior Applied Scientist

Adobe Systems
San Jose
Workplace: OnsiteFull timeFunction: Data Science & Machine LearningEducation: mastersSkills: ["Python","PyTorch","Hugging Face","VLLM","Weights & Biases","W&B","ML tooling","MLOps","Cloud ML services","AWS","GCP","Azure"]

Design, build, and deploy end-to-end LLM-powered systems to simulate consumer audiences for brand campaigns. Develop inference harnesses on top of large language models, tune models with survey and behavioral data, and create evaluation datasets to quantify synthetic audience quality. Collaborate with product, applied science, and engineering to ship production features from research into product.

Loading

Loading job details...

Preparing the role view and application actions.

FursaFursa
Adobe Systems
Adobe Systems
3 months ago

Senior Applied Scientist

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 21 hours agoStatus: Live
Reposted: similar role first listed 7 months ago

Job Summary

Design, build, and deploy end-to-end LLM-powered systems to simulate consumer audiences for brand campaigns. Develop inference harnesses on top of large language models, tune models with survey and behavioral data, and create evaluation datasets to quantify synthetic audience quality. Collaborate with product, applied science, and engineering to ship production features from research into product.
Location: San Jose
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning

Key Responsibilities

  • •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.

Key Requirements

  • •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 techniques including SFT, preference optimization (DPO/GRPO) and modern post-training tradeoffs.
  • •Experience with RLHF, RLAIF, or RL-based state alignment of LLMs.
  • •Proven track record of building evaluation datasets and harnesses — you have opinions about what makes an eval load-bearing versus theater.
Experience:LLMMachine learningAISynthetic audiencesNLP
Education:Master's
Skills:PythonPyTorchHugging FaceVLLMWeights & BiasesW&BML toolingMLOpsCloud ML servicesAWSGCPAzure
Languages:English
Tech Stack:PythonPyTorchHugging FaceVLLMWeights & BiasesMLflowMLOpsAWSGCPAzure

Company Brief

Adobe Systems
Provides creative, marketing, and document management software and cloud services, including Photoshop, Illustrator, Acrobat, and the Adobe Experience Cloud, serving creative professionals, enterprises, and governments worldwide.
Industry: SaaS
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: San Jose, United States
Founded: 1982
Glassdoor
Glassdoor: 4.0
WebsiteLinkedInGlassdoor