Senior Applied Scientist at Microsoft

Beijing, Beijing, China -

Full Time

Start Date

Immediate

Expiry Date

18 Feb, 26

Salary

0.0

Posted On

20 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Applied ML, NLP, Python, PyTorch, Model Development, Data Engineering, Experimentation, Statistical Analysis, Collaboration, Monitoring, Production ML Engineering, Responsible AI, Customer-Facing Experience, Cloud, Security, Privacy

Industry

Software Development

Description

Ship features with PM & Engineering. Co‑own scenario goals; translate product requirements into scientific plans and productionized solutions that meet quality/latency/cost targets. Model development & optimization. Design, fine‑tune, and evaluate models for LLM‑based authoring, summarization, reasoning, voice/chat, and personalization (e.g., SFT, alignment, prompt/tool use, safety filtering, multilingual & multimodal). Data & evaluation at scale. Build/extend data pipelines for curation/labeling/feature stores; author offline eval harnesses; run online A/Bs and interleavings; define guardrails and success metrics; author scorecards and decision memos. Production ML engineering. contribute to service code and configs; add monitoring, tracing, dashboards, and auto‑scaling; participate in on‑call and postmortems to improve live‑site reliability. Responsible AI. Collaboration & mentoring. Partner across PM/ENG/Design/CE/ORA/CELA; share methods and code, review PRs, improve reproducibility and documentation; mentor junior scientists. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) 2+ years customer-facing, project-delivery experience, professional services, and/or consulting experience 4+ years applied ML/NLP experience delivering models and features to production at scale. Proficiency in Python and PyTorch (or equivalent DL framework). Solid SDLC practices: unit/integration testing, CI/CD, code reviews, version control, performance profiling, and reliability hardening. Ability to write clean, maintainable, efficient code for production services and clients. Experimentation & evaluation: sound experimental design, metric design (quality, safety, latency, cost), and statistical analysis; experience running online A/B tests. Proven collaboration with PM & Engineering to integrate ML into shipped product (APIs/services/clients) and to drive measurable user or business impact. These requirements include but are not limited to the following specialized security screenings: Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience. Graduate degree (MS/PhD) in ML/AI or related field (or equivalent applied research impact). Depth in transformers/LLMs (pretraining, SFT, alignment/RLHF/DPO), RAG, prompt/agent tooling, and safety/abuse mitigation for generative systems. Production ML engineering at scale: Model serving/inference (e.g., ONNX Runtime, vLLM, Triton, quantization, distillation, caching, dynamic batching, rate limiting). Service development: stable APIs/SDKs, microservices, feature flags, safe rollouts/rollbacks, config & traffic ramps. Observability & live‑site: SLIs/SLOs, dashboards, structured logging, tracing, alerting, on‑call, and postmortems. Experimentation: A/B & interleavings, guardrail metrics (quality/safety/latency/cost), sequential testing, eval governance. Data engineering: ETL at scale (Spark/Databricks), feature stores, vector indexing (Azure AI Search/FAISS/Milvus), data quality checks. Cloud & orchestration: Azure ML, AKS/Kubernetes, containerization, autoscaling, artifact & secret management, policy enforcement. Security & privacy: data minimization, access controls, audit logging in enterprise SaaS contexts.

Responsibilities

The Senior Applied Scientist will co-own scenario goals and translate product requirements into scientific plans and productionized solutions. Responsibilities include model development and optimization, data evaluation at scale, and ensuring responsible AI practices.