Senior Machine Learning Engineer, AI Studio

🕒 vor 1 Tag

🇺🇸 Vereinigte Staaten – Remote

💵 $156.190 - $211.315 / Jahr

⏰ Vollzeit

🟠 Senior

🤖 KI-Ingenieur

🦅 H1B-Visum-Sponsor

Dieses Unternehmen hat in der Vergangenheit H1B-Visa gesponsert.

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Amgen

WebsiteLinkedInAlle Stellen

10.000+ Mitarbeiter

Gegründet 1980

🏥 Gesundheitswesen

🧬 Biotechnologie

💊 Pharmazie

💰 €28.500.000.000 Post-IPO Debt im 2022-12

Healthcare • Biotechnology • Pharmaceuticals

Amgen ist ein weltweit führendes Unternehmen in der Biotechnologie, das sich auf die Entwicklung und Vermarktung innovativer Arzneimittel aus lebenden Zellen konzentriert. Das Unternehmen hat zum Ziel, schwere Krankheiten zu behandeln, wobei oft Krankheiten im Fokus stehen, für die es nur begrenzte therapeutische Möglichkeiten gibt. Amgen legt großen Wert auf wissenschaftliche Innovation und ist der ethischen Forschung, der Patientensicherheit und der ökologischen Nachhaltigkeit verpflichtet. Es ist aktiv in klinischen Studien engagiert und bekannt für seine Beiträge in den Bereichen Krebsbehandlung und Adipositas-Management unter anderem.

Beschreibung

• Define and own AI assets or substantial technical workstreams from problem framing through architecture, development, evaluation, launch, stabilization, support transition, adoption and measurable outcomes • Turn prioritized business demand into governed, reusable AI assets with accountable ownership and measurable value • Define users, workflows, decisions, intended use, baselines, value hypotheses, acceptance criteria, adoption paths, operating owners and measurable outcomes • Map rules, exceptions, data dependencies and human decision points before selecting automation, ML, GenAI, RAG, agents or manual approaches • 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 designs, leakage controls, uncertainty and calibration checks, subgroup and robustness checks, gold sets, error taxonomies, expert adjudication and release thresholds • Establish MLOps/LLMOps for lineage, reproducibility, versioning, CI/CD, releases, 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

🎯 Anforderungen

• Doctorate degree, or Master’s degree and 2 years of experience in Computer Science, IT or related fields, or Bachelor’s degree and 4 years of experience, or Associate’s degree and 8 years of experience, or high school diploma/GED and 10 years of experience • Demonstrated end-to-end ownership of at least one production ML, GenAI, software, data or automation system with a measurable outcome • Strong hands-on proficiency in Python and SQL • Experience designing production software, services and evaluation pipelines • Advanced capability in Applied ML, GenAI/RAG/agents or ML platform/MLOps • Experience with data-centric AI, weak supervision, active learning, conformal or Bayesian uncertainty, causal inference, time-series, survival methods or drift-aware retraining • Experience with transformers, multimodal pipelines, CNNs, RNNs, GNNs, PEFT or LoRA, fine-tuning, distillation, quantization, routing, cascades or inference optimization • Experience with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, serverless systems, infrastructure as code, MLflow, Airflow, Kubeflow, observability and FinOps • Experience with human-AI review, correction, approval, accessibility, uncertainty communication, workflow automation and GxP-relevant or validated systems • Strong product thinking and ability to connect technical decisions to user, workflow, risk, cost and business value • Technical leadership and mentoring 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

🏖️ Vorteile

• A comprehensive employee benefits package • Retirement and Savings Plan with generous company contributions • Group medical, dental and vision coverage • Life and disability insurance • Flexible spending accounts • 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 support