Senior scientist

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

What you will do-

Perform end to end LFHF programs: 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, and 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.Minimum 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, and libraries for post training and evaluation; solid software engineering practices and experimentation discipline.

• Track record publications in top venues (e.g., 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

Disclaimer:

Certain US customer or client-facing roles may be required to comply with applicable requirements, such as immunization and occupational health mandates.

Range and benefit information provided in this posting are specific to the stated locations only

US: Hiring Range in USD from: $97,500 to $199,500 per annum. 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, as well as reflect Oracle's differing products, industries and lines of business.

Candidates are typically placed into the range based on the preceding factors as well as internal peer equity.

Oracle US offers a comprehensive benefits package which includes the following:

Medical, dental, and vision insurance, including expert medical opinion

Short term disability and long term disability

Life insurance and AD&D

Supplemental life insurance (Employee/Spouse/Child)

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 is provided to all eligible employees assigned to a salaried (non-overtime eligible) position. Accrued Vacation is provided to all other employees eligible for vacation benefits. For employees working at least 35 hours per week, the vacation accrual rate is 13 days annually for the first three years of employment and 18 days annually for subsequent years of employment. Vacation accrual is prorated for employees working between 20 and 34 hours per week. Employees working fewer than 20 hours per week are not eligible for vacation.

11 paid holidays

Paid sick leave: 72 hours of paid sick leave upon date of hire. Refreshes each calendar year. Unused balance will carry over each year up to a maximum cap of 112 hours.

Paid parental leave

Adoption assistance

Employee Stock Purchase Plan

Financial planning and group legal

Voluntary benefits including auto, homeowner and pet insurance

The role will generally accept applications for at least three calendar days from the posting date or as long as the job remains posted.

Career Level - IC3

About Us

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