Machine Learning Engineer, AI Studio

🕒 Agosto 24

🇺🇸 Estados Unidos – Remoto (EUA)

💵 $129.654 - $175.415 / ano

⏰ Tempo Integral

🟠 Sênior

🔴 Especialista

🤖 Engenheiro de IA

🦅 Patrocina Visto H1B

Esta empresa já patrocinou vistos H1B no passado.

👻 Score fantasma 18%

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Amgen

SiteLinkedInTodas as Vagas

10.000+ funcionários

Fundada em 1980

🏥 Saúde

🧬 Biotecnologia

💊 Farmacêutico

💰 $28.500.000.000 Post-IPO Debt em 2022-12

Healthcare • Biotechnology • Pharmaceuticals

A Amgen está comprometida a liberar o potencial da biologia para pacientes que sofrem de doenças graves, descobrindo, desenvolvendo, fabricando e fornecendo terapias humanas inovadoras. Esta abordagem começa utilizando ferramentas como genética humana avançada para desvendar as complexidades das doenças e entender os fundamentos da biologia humana.

Descrição

• Independently own defined production components within enterprise AI products and automation solutions • Design, release, diagnose and support components while connecting technical measures to user and workflow outcomes • Define component boundaries, intended use, acceptance criteria, non-functional requirements, decision consequences, support expectations and technical estimates with product and architecture partners • Design and implement maintainable Python, SQL, API, data, model, retrieval, agent-tool and workflow components with clear contracts, configuration, testing, error handling and documentation • Apply EDA, feature engineering, supervised or unsupervised methods, baselines, cross-validation, leakage prevention, calibration, subgroup, threshold, explainability and error analysis where relevant • Build GenAI, NLP, RAG and bounded agent components using structured output, embeddings, hybrid search, reranking, provenance, citations, permissions, approvals, retries and recoverable failure behaviour • Engineer batch or event-driven data, document, feature, embedding, label and evaluation pipelines with schema validation, lineage, provenance, access control and consistency checks • Define representative evaluation for model quality, uncertainty, retrieval, grounding, citations, task success, tool correctness, safety, latency, cost and user impact • Release and support components using cloud services, containers, CI/CD, versioning, monitoring, rollback, incident response and runbooks • Lead diagnosis of moderately complex failures • Apply security, privacy, Responsible AI, validation, auditability, human oversight and applicable GxP controls • Contribute reusable assets and guide Associate engineers on familiar work

🎯 Requisitos

• Master’s degree OR Bachelor’s degree and 2 years of Computer Science, IT or related field experience OR Associate’s degree and 6 years of Computer Science, IT or related field experience OR High school diploma / GED and 8 years of Computer Science, IT or related field experience • Demonstrated ownership of at least one production software, data, ML, GenAI or automation component • Strong hands-on proficiency in Python and SQL, with sound software-engineering and testing practices • Strong capability in at least one of classical ML, GenAI/RAG/agents or MLOps/platform engineering, with working knowledge of adjacent areas • Experience with advanced ML and deep learning tools and methods, including PyTorch, TensorFlow, Hugging Face, scikit-learn, XGBoost, PyMC, computer vision, NLP, GNNs, causal inference or uncertainty estimation • Experience with advanced GenAI and knowledge systems, including LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, hybrid retrieval, knowledge graphs, graph RAG or evidence verification • Experience with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, infrastructure as code, MLflow, Airflow, Kubeflow or GitHub Actions • Familiarity with MCP-style integration, agent tracing, adversarial testing, durable workflows, permissions, human review, BI or process automation • Experience in healthcare, life sciences, GxP, validated systems or another regulated or high-impact environment • Independent problem solving and sound component-level technical judgment • Clear communication of assumptions, evidence, trade-offs, risks and support implications • Strong collaboration with business SMEs, product, architecture, software, data, platform, evaluation and control partners • Ownership, reliability and disciplined follow-through from design through production support • Ability to guide junior engineers and learn new tools through evidence-based experimentation

🏖️ Benefícios

• A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions • Group medical, dental and vision coverage • Life and disability insurance • Flexible spending accounts • A discretionary annual bonus program • Stock-based long-term incentives • Award-winning time-off plans • Flexible work models where possible • Career development opportunities • Work/life balance • Financial plans with opportunities to save towards retirement or other goals