Asks for

The ad says“Advanced software and AI/ML system design: Production Python and SQL, APIs, distributed or event-driven services, data persistence, testing, performance, repository governance, design review and end-to-end architecture.”
The ad says“Data, knowledge, cloud and AI operations: Trustworthy batch/streaming pipelines, data contracts, lineage, vector/graph stores, Spark or Databricks, containers, Kubernetes, MLOps/LLMOps, SLOs and lifecycle operations.”
The ad says“Deep learning, NLP and foundation models: Selection and production use of neural, transformer, embedding, vision, document and multimodal approaches, including fine-tuning versus prompting, latency, privacy and cost trade-offs.”
The ad says“GenAI, RAG, knowledge and agentic AI: Grounded retrieval, structured output, provenance, citations, abstention, entitlement controls, bounded tool use, permissions, durable state, recovery, adversarial evaluation and human control.”
The ad says“Responsible AI and regulated delivery: Risk assessment, least privilege, threat modelling, red teaming, bias and subgroup robustness, privacy, model/data documentation, human accountability, validation and applicable GxP controls.”
The ad says“Data, knowledge, cloud and AI operations: Trustworthy batch/streaming pipelines, data contracts, lineage, vector/graph stores, Spark or Databricks, containers, Kubernetes, MLOps/LLMOps, SLOs and lifecycle operations.”
The ad says“Data, knowledge, cloud and AI operations: Trustworthy batch/streaming pipelines, data contracts, lineage, vector/graph stores, Spark or Databricks, containers, Kubernetes, MLOps/LLMOps, SLOs and lifecycle operations.”
The ad says“Advanced statistics, ML and experimental design: EDA, feature engineering, supervised and unsupervised learning, predictive modelling, ensembles, anomaly detection, calibration, uncertainty, robustness, explainability and causal reasoning where justified.”
The ad says“Data, knowledge, cloud and AI operations: Trustworthy batch/streaming pipelines, data contracts, lineage, vector/graph stores, Spark or Databricks, containers, Kubernetes, MLOps/LLMOps, SLOs and lifecycle operations.”
The ad says“Deep learning, NLP and foundation models: Selection and production use of neural, transformer, embedding, vision, document and multimodal approaches, including fine-tuning versus prompting, latency, privacy and cost trade-offs.”
The ad says“Strong product thinking and ability to connect technical decisions to user, workflow, risk, cost and business value.”
The ad says“Advanced software and AI/ML system design: Production Python and SQL, APIs, distributed or event-driven services, data persistence, testing, performance, repository governance, design review and end-to-end architecture.”
The ad says“GenAI, RAG, knowledge and agentic AI: Grounded retrieval, structured output, provenance, citations, abstention, entitlement controls, bounded tool use, permissions, durable state, recovery, adversarial evaluation and human control.”
The ad says“Responsible AI and regulated delivery: Risk assessment, least privilege, threat modelling, red teaming, bias and subgroup robustness, privacy, model/data documentation, human accountability, validation and applicable GxP controls.”
The ad says“Data, knowledge, cloud and AI operations: Trustworthy batch/streaming pipelines, data contracts, lineage, vector/graph stores, Spark or Databricks, containers, Kubernetes, MLOps/LLMOps, SLOs and lifecycle operations.”
The ad says“Advanced software and AI/ML system design: Production Python and SQL, APIs, distributed or event-driven services, data persistence, testing, performance, repository governance, design review and end-to-end architecture.”
The ad says“Advanced statistics, ML and experimental design: EDA, feature engineering, supervised and unsupervised learning, predictive modelling, ensembles, anomaly detection, calibration, uncertainty, robustness, explainability and causal reasoning where justified.”

Career Category

Information Systems

Job Description

Senior Machine Learning Engineer, AI Studio

CAREER LEVEL: GCF 5 – Specialist

CAREER TRACK: Individual Contributor

PRIMARY SCOPE: End-to-end ownership of a small AI asset or substantial technical workstream

ORGANIZATION: Applied AI | AI Studio

ABOUT AMGEN

Amgen harnesses the best of biology and technology to fight the world’s toughest diseases and make people’s lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains at the cutting edge of innovation, using technology and human genetic data to push beyond what is known today.

ABOUT THE ROLE

Role Description:

The Senior Machine Learning Engineer position offers a unique opportunity to join a fun, innovative engineering team within the AI & Data Science (AI&D) - organization. We are the Applied AI team (AI Studio). AI Studio is Amgen’s enterprise engine for turning high-value business challenges into scalable AI products. We partner with key business partners across the company to identify the right opportunities, shape them into actionable use cases, and design, build, and launch AI products responsibly. Our work spans the full lifecycle—from early discovery and rapid prototyping to production deployment, reuse across the enterprise, and measurable business impact. You will be part of AI Studio and define and own AI assets or substantial technical workstream from problem framing through architecture, model and system development, evaluation, launch, stabilization, support transition, adoption and measurable outcome.

You will remain hands-on while leading decisions across software, statistics, classical ML, deep learning, NLP, GenAI, RAG, bounded agents, data and knowledge pipelines, APIs, MLOps/LLMOps, security, governance and operations. Within Applied AI - AI Studio turn prioritized business demand into governed, reusable AI assets with accountable ownership and measurable value across software, data, automation, machine learning, Generative AI, RAG, bounded agents, evaluation, observability and lifecycle operations.

Roles & Responsibilities:

Define the user, workflow, decision, intended use, baseline, value hypothesis, acceptance criteria, adoption path, operating owner and measurable technical and business outcomes.

Map rules, exceptions, data dependencies and human decision points before selecting deterministic automation, classical ML, deep learning, GenAI, RAG, agents or a manual approach.

Own production architecture across data, feature and knowledge pipelines, models, retrieval, agents, APIs, persistence, workflows, user experience, security zones and human review.

Lead hands-on development of production software, EDA, feature engineering, predictive models, deep-learning or NLP components, inference services, RAG, agent tools and workflow orchestration.

Establish baselines, experiment design, leakage controls, uncertainty, calibration, subgroup and robustness checks, gold sets, error taxonomies, expert adjudication and release thresholds.

Establish MLOps/LLMOps for lineage, reproducibility, versioning, CI/CD, canary or shadow release, observability, drift monitoring, SLOs, rollback, incidents, disaster recovery, capacity, cost and runbooks.

Coordinate security, privacy, Responsible AI, Quality, legal, model-risk and GxP controls; create reusable capabilities, measure adoption and value, mentor engineers and improve delivery practices.

Basic Qualifications and Experience:

• Bachelor’s/Master’s degree with 8 - 13 years of experience in Computer Science, IT or related field.

Functional Skills:

Advanced software and AI/ML system design: Production Python and SQL, APIs, distributed or event-driven services, data persistence, testing, performance, repository governance, design review and end-to-end architecture.

Advanced statistics, ML and experimental design: EDA, feature engineering, supervised and unsupervised learning, predictive modelling, ensembles, anomaly detection, calibration, uncertainty, robustness, explainability and causal reasoning where justified.

Deep learning, NLP and foundation models: Selection and production use of neural, transformer, embedding, vision, document and multimodal approaches, including fine-tuning versus prompting, latency, privacy and cost trade-offs.

GenAI, RAG, knowledge and agentic AI: Grounded retrieval, structured output, provenance, citations, abstention, entitlement controls, bounded tool use, permissions, durable state, recovery, adversarial evaluation and human control.

Data, knowledge, cloud and AI operations: Trustworthy batch/streaming pipelines, data contracts, lineage, vector/graph stores, Spark or Databricks, containers, Kubernetes, MLOps/LLMOps, SLOs and lifecycle operations.

Responsible AI and regulated delivery: Risk assessment, least privilege, threat modelling, red teaming, bias and subgroup robustness, privacy, model/data documentation, human accountability, validation and applicable GxP controls.

Must-Have Skills:

Demonstrated end-to-end ownership of at least one production ML, GenAI, software, data or automation system that delivered a measurable outcome.

Strong hands-on proficiency in Python and SQL, with experience designing production software, services and evaluation pipelines.

Advance capability in at least one role-defining pillar—Applied ML, GenAI/RAG/agents or ML platform/MLOps—plus credible depth across the production lifecycle.

Good-to-Have Skills:

Advanced ML, causal and uncertainty methods: Experience with data-centric AI, weak supervision, active learning, conformal or Bayesian uncertainty, causal inference, time-series, survival methods or drift-aware retraining.

Advanced deep learning and model efficiency: Experience with transformers, multimodal pipelines, CNNs, RNNs, GNNs, PEFT or LoRA, fine-tuning, distillation, quantization, routing, cascades or inference optimization.

Cloud, platform and AI operations: Experience with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, serverless systems, infrastructure as code, MLflow, Airflow, Kubeflow, observability and FinOps.

Human-AI and regulated delivery: Experience with review, correction, approval, accessibility, uncertainty communication, workflow automation and GxP-relevant or validated systems.

Soft Skills:

Strong product thinking and ability to connect technical decisions to user, workflow, risk, cost and business value.

Technical leadership, mentoring and constructive challenge while remaining hands-on.

Excellent analytical judgment and clear communication of evidence, uncertainty, trade-offs and limitations.

Cross-functional leadership across business, product, architecture, engineering and control functions.

Ownership, resilience and continuous improvement through incidents, feedback and measured outcomes.

EQUAL OPPORTUNITY STATEMENT

Amgen is an Equal Opportunity employer and will consider you without regard to your race, colour, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law.