Oracle
Job Description
At Oracle Analytics, we are building the next generation of enterprise AI products to enable intelligent data analysis at scale. Leveraging our foundational strengths in data management and enterprise software applications, we are advancing our platforms and applications by deeply embedding cutting‑edge agentic AI, generative AI, and innovations in machine learning and optimization. We are seeking a Senior Applied Scientist to perform innovation in learning from human feedback (LFHF) and user preference modeling, with a strong focus on in‑context learning and post training for large language models. You will design data and feedback strategies, build preference/reward models, and develop post‑training pipelines (e.g., SFT, DPO, RLHF/RLAIF) that deliver safe, high‑quality, and cost‑efficient enterprise AI experiences. You will partner closely with research engineers and product teams to ship aligned models to production, instrument rigorous evaluation, and drive measurable customer and business impact.
Responsibilities
Define annotation rubrics, sampling strategies, and quality controls; design rater guidelines and human‑in‑the‑loop workflows in collaboration with product/UX and data engineering.
Build preference and reward models: pairwise and listwise modeling, win‑rate optimization, uncertainty estimation, and active learning to improve sample efficiency and data quality.
Develop post‑training pipelines: supervised fine‑tuning (SFT), direct preference optimization (DPO/IPO/ORPO), RLHF/RLAIF, and distillation‑balancing quality, safety, latency, and cost for enterprise workloads.
Advance in‑context learning: retrieval‑augmented prompting, dynamic few‑shot selection, tool‑use/orchestration‑aware prompting, instruction following, and mitigation of ICL brittleness and context overflow.
Optimize inference and efficiency: PEFT/LoRA/QLoRA, quantization, speculative decoding, caching, and distillation for scalable deployment on Oracle infrastructure.
Evaluate rigorously: establish offline/online metrics; pairwise and rubric‑based human evals; red teaming; safety/guardrail tests; A/B experiments; win‑rate tracking; perform offline policy evaluation where applicable.
Ensure safety, privacy, and compliance: apply content safety policies, guardrail configuration, PII handling/redaction, differential logging, and model governance appropriate for regulated enterprise settings.
Productionize solutions: collaborate with platform teams to ship models and evaluation services; implement observability, telemetry, canarying, rollback, and lifecycle management.
Stay current with research and translate advances into production differentiators; mentor teammates and contribute to a culture of scientific rigor and impact.
Qualifications
MS, PhD (preferred) in Computer Science, Machine Learning, Statistics, Electrical Engineering, or related field with a focus relevant to LFHF, reinforcement learning, NLP, or human‑AI interaction.
Experience (industry or applied research) building and deploying ML systems, including LLM post‑training and evaluation.
Demonstrated expertise in learning from human or AI feedback: data/rubric design, preference/reward modeling, and optimization methods (e.g., SFT, DPO, RLHF/RLAIF).
Strong background in in‑context learning, prompt/program design, retrieval‑augmented generation, and model alignment for accuracy, safety, and robustness.
Proficient in Python and modern ML stacks: PyTorch/JAX, Transformers, libraries for post‑training and evaluation; solid software engineering practices and experimentation discipline.
Track record of publications in top venues (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL). Preferred qualifications:
Experience designing at scale data pipelines for feedback collection, active learning, and rater operations; familiarity with label quality auditing and bias/variance trade‑offs.
Knowledge of bandits/off‑policy evaluation, causal inference for policy changes, and statistical testing for online experiments.
Familiarity with LLM efficiency and serving: tensor/graph optimization, KV cache management, batching strategies, and throughput/latency trade‑offs.
Experience integrating safety/guardrails, policy enforcement, and privacy‑preserving telemetry into production workflows aligned with enterprise compliance.
Comfortable collaborating across research, engineering, product, and legal/compliance; excellent communication skills to explain methods and results to technical and non‑technical stakeholders.
Practical experience with experiment tracking, model registries, and CI/CD for ML.
Career Level IC3
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Research, Analyst, and Information Technology
Industries IT Services and IT Consulting
Salary Range US: Hiring Range in USD from: $97,500 - $199,500 per year. May be eligible for bonus and equity. Oracle maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, market conditions and locations.
Benefits
Medical, dental, and vision insurance.
Short‑term and long‑term disability.
Life insurance and AD&D.
Supplemental life insurance.
Health care and dependent care Flexible Spending Accounts.
Pre‑tax commuter and parking benefits.
401(k) savings and investment plan with company match.
Paid time off: flexible vacation, paid sick leave, paid parental leave, adoption assistance.
Employee stock purchase plan.
Voluntary benefits including auto, homeowner and pet insurance.
Equal Employment Opportunity Oracle is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status or any other characteristic protected by law. Oracle will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.
Location Seattle, WA and Redmond, WA (positions may be remote or on‑site).
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Responsibilities
Define annotation rubrics, sampling strategies, and quality controls; design rater guidelines and human‑in‑the‑loop workflows in collaboration with product/UX and data engineering.
Build preference and reward models: pairwise and listwise modeling, win‑rate optimization, uncertainty estimation, and active learning to improve sample efficiency and data quality.
Develop post‑training pipelines: supervised fine‑tuning (SFT), direct preference optimization (DPO/IPO/ORPO), RLHF/RLAIF, and distillation‑balancing quality, safety, latency, and cost for enterprise workloads.
Advance in‑context learning: retrieval‑augmented prompting, dynamic few‑shot selection, tool‑use/orchestration‑aware prompting, instruction following, and mitigation of ICL brittleness and context overflow.
Optimize inference and efficiency: PEFT/LoRA/QLoRA, quantization, speculative decoding, caching, and distillation for scalable deployment on Oracle infrastructure.
Evaluate rigorously: establish offline/online metrics; pairwise and rubric‑based human evals; red teaming; safety/guardrail tests; A/B experiments; win‑rate tracking; perform offline policy evaluation where applicable.
Ensure safety, privacy, and compliance: apply content safety policies, guardrail configuration, PII handling/redaction, differential logging, and model governance appropriate for regulated enterprise settings.
Productionize solutions: collaborate with platform teams to ship models and evaluation services; implement observability, telemetry, canarying, rollback, and lifecycle management.
Stay current with research and translate advances into production differentiators; mentor teammates and contribute to a culture of scientific rigor and impact.
Qualifications
MS, PhD (preferred) in Computer Science, Machine Learning, Statistics, Electrical Engineering, or related field with a focus relevant to LFHF, reinforcement learning, NLP, or human‑AI interaction.
Experience (industry or applied research) building and deploying ML systems, including LLM post‑training and evaluation.
Demonstrated expertise in learning from human or AI feedback: data/rubric design, preference/reward modeling, and optimization methods (e.g., SFT, DPO, RLHF/RLAIF).
Strong background in in‑context learning, prompt/program design, retrieval‑augmented generation, and model alignment for accuracy, safety, and robustness.
Proficient in Python and modern ML stacks: PyTorch/JAX, Transformers, libraries for post‑training and evaluation; solid software engineering practices and experimentation discipline.
Track record of publications in top venues (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL). Preferred qualifications:
Experience designing at scale data pipelines for feedback collection, active learning, and rater operations; familiarity with label quality auditing and bias/variance trade‑offs.
Knowledge of bandits/off‑policy evaluation, causal inference for policy changes, and statistical testing for online experiments.
Familiarity with LLM efficiency and serving: tensor/graph optimization, KV cache management, batching strategies, and throughput/latency trade‑offs.
Experience integrating safety/guardrails, policy enforcement, and privacy‑preserving telemetry into production workflows aligned with enterprise compliance.
Comfortable collaborating across research, engineering, product, and legal/compliance; excellent communication skills to explain methods and results to technical and non‑technical stakeholders.
Practical experience with experiment tracking, model registries, and CI/CD for ML.
Career Level IC3
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Research, Analyst, and Information Technology
Industries IT Services and IT Consulting
Salary Range US: Hiring Range in USD from: $97,500 - $199,500 per year. May be eligible for bonus and equity. Oracle maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, market conditions and locations.
Benefits
Medical, dental, and vision insurance.
Short‑term and long‑term disability.
Life insurance and AD&D.
Supplemental life insurance.
Health care and dependent care Flexible Spending Accounts.
Pre‑tax commuter and parking benefits.
401(k) savings and investment plan with company match.
Paid time off: flexible vacation, paid sick leave, paid parental leave, adoption assistance.
Employee stock purchase plan.
Voluntary benefits including auto, homeowner and pet insurance.
Equal Employment Opportunity Oracle is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status or any other characteristic protected by law. Oracle will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.
Location Seattle, WA and Redmond, WA (positions may be remote or on‑site).
#J-18808-Ljbffr