{"id":1265653,"url":"https://alion.io/job/amgen-principal-machine-learning-engineer-2","title":"Principal Machine Learning Engineer","company":{"id":4775,"name":"Amgen","domain":"amgen.com","url":"https://alion.io/company/amgen","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":322,"ghost_share":0,"stale_share":0.817,"repost_share":0.003,"time_to_fill_p50_days":27,"computed_at":"2026-09-27T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Thousand Oaks, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":187395,"max":253534,"currency":"USD","period":"year","gross":null,"usd_annual":253534},"salary_estimate":null,"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"Blue-Green Deployment","optional":false},{"name":"Docker","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Java","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI SDK","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Agile","optional":true}],"status":"live","first_seen_at":"2026-09-25T22:27:26Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-09-27T05:24:35Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Career Category\nInformation SystemsJob Description\nJoin Amgen’s Mission of Serving Patients\nAt Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission-to serve patients living with serious illnesses-drives all that we do.\nSince 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas -Oncology, Inflammation, General Medicine, and Rare Disease- we reach millions of patients each year. Amgen is advancing a broad and deep pipeline of medicines to treat cancer, heart disease, inflammatory conditions, rare diseases, and obesity and obesity-related conditions. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.\nOur award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.\nPrincipal Machine Learning Engineer\nWhat you will do\nLet’s do this. Let’s change the world. In this vital role you will Own enterprise AI/ML architecture, standards, APIs, and guardrails across cloud/on-prem.\nWe are seeking a Principal Machine Learning Engineer -Amgen’s most senior individual-contributor authority on building and scaling end-to-end machine-learning and generative-AI solutions. Sitting at the intersection of engineering excellence and data-science enablement, you will develop, deploy and monitor models-classical ML, deep learning and LLMs-securely and cost-effectively. Acting as a “player-coach,” you will establish AI solution strategy, define technical standards, and partner with DevOps, Security, Compliance and Product teams to deliver a frictionless, enterprise-grade AI solutions.\nRoles & Responsibilities:\nOwn enterprise AI/ML architecture, standards, APIs, and guardrails across cloud/on-prem. \nBuild production ML/GenAI solutions and lightweight apps delivering sub-second insights. \nBuild end-to-end ML pipelines -data ingestion, feature engineering, training, hyper-parameter optimisation, evaluation, registration and automated promotion-using Kubeflow, SageMaker Pipelines, Open AI SDK or equivalent MLOps stacks. \n Build and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines or business-logic layers so insights reach users with sub-second latency. \nEstablish observability, SLOs, and safe deploys (blue-green/canary, shadow, rollbacks) with incident runbooks. \n Lead rigorous evaluation (offline/online, A/B), drift detection, and automated retraining. \nArchitect LLM/RAG with prompt management, safety guardrails, and optimized inference. \nEnforce data quality, lineage, and model/data cards; apply privacy-preserving techniques where needed. \nContribute reusable ML/GenAI components -feature stores, model registries, experiment-tracking libraries-and evangelize best practices that raise engineering velocity across squads. \nPerform exploratory data analysis and feature ideation on complex, high-dimensional datasets to inform algorithm selection and ensure model robustness. \n Prototype and benchmark new algorithms, offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs. \nTranslate domain needs (R&D, Manufacturing, Commercial) into roadmaps; mentor teams and communicate trade-offs. \nWhat we expect of you\nWe are all different, yet we all use our unique contributions to serve patients. The professional we seek is a Principal Machine Learning Engineer with these qualifications.\nBasic Qualifications:\nDoctorate degree and 2 years of Machine Learning Engineer experience\nOR\nMaster’s degree and 6 years of Machine Learning Engineer experience\nOR\nBachelor’s degree and 8 years of Machine Learning Engineer experience\nOR\nAssociate’s degree and 10 years of Machine Learning Engineer experience \nOR\nHigh school diploma / GED and 12 years of Machine Learning Engineer experience\nIn addition to meeting at least one of the above requirements, you must have a minimum of 2 years experience directly managing people and/or leadership experience leading teams, projects, programs, or directing the allocation or resources. Your managerial experience may run concurrently with the required technical experience referenced above\n3-5 years in AI/ML and enterprise software. \nStrong command of machine-learning algorithms - regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures (CNNs, RNNs, transformers) and modern LLM/RAG techniques-with the judgment to choose, tune and operationalize the right method for a given business problem. \nProven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale. \nExpert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, LangGraph, Semantic Kernel). \nProficiency in Python and Java; containerization (Docker/K8s); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines). \nStrong business-case skills-able to model TCO vs. NPV and present trade-offs to executives. \nExceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives. \nPreferred Qualifications:\nExperience in Biotechnology or pharma industry is a big plus \nPublished thought-leadership or conference talks on enterprise GenAI adoption. \nMaster’s degree in Computer Science and or Data Science \nFamiliarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery. \nEducation and Professional Certifications\nMaster’s degree with 10-12 + years of experience in Computer Science, IT or related field \nOR\nBachelor’s degree with 12-14 + years of experience in Computer Science, IT or related field \nCertifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus. \nSoft Skills:\nExcellent analytical and troubleshooting skills. \nStrong verbal and written communication skills \nAbility to work effectively with global, virtual teams \nHigh degree of initiative and self-motivation. \nAbility to manage multiple priorities successfully. \nTeam-oriented, with a focus on achieving team goals. \nAbility to learn quickly, be organized and detail oriented. \nStrong presentation and public speaking skills. \nWhat you can expect of us\nAs we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way.\nThe expected annual salary range for this role in the U.S. (excluding Puerto Rico) is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications.\nIn addition to the base salary, Amgen offers a Total Rewards Plan, based on eligibility, comprising of health and welfare plans for staff and eligible dependents, financial plans with opportunities to save towards retirement or other goals, work/life balance, and career development opportunities that may include:\nA comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts\nA discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan\nStock-based long-term incentives\nAward-winning time-off plans\nFlexible work models where possible. Refer to the Work Location Type in the job posting to see if this applies.\nApply now and make a lasting impact with the Amgen team.\ncareers.amgen.com\nIn any materials you submit, you may redact or remove age-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.\nApplication deadline\nAmgen does not have an application deadline for this position; we will continue accepting applications until we receive a sufficient number or select a candidate for the position.\nSponsorship\nSponsorship for this role is not guaranteed.\nAs an organization dedicated to improving the quality of life for people around the world, Amgen fosters an inclusive environment of diverse, ethical, committed and highly accomplished people who respect each other and live the Amgen values to continue advancing science to serve patients. Together, we compete in the fight against serious disease.\nAmgen is an Equal Opportunity employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law.\nWe will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.\n.Salary Range\n187,395.25USD -253,534.75 USD","description_format":"text","description_chars":9485,"description_truncated":false,"requirements":{"experience_years_min":12,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"high_school","optional":false},"security_clearance":false,"languages":[]},"benefits":["Flexible schedule","Retirement plans"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[{"name":"Puerto Rico","iso":"PR"}],"relocation_offered":false,"industries":["Prescription Drugs","Biologics & Biosimilars","Immunology & Inflammation Therapeutics","Cardiometabolic Therapeutics"],"lifecycle":[{"event":"open","at":"2026-09-25T22:27:26Z"}],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":1,"expected_fill_days":27,"reasons":["conf:0","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-09-27T05:45:00Z"},"pay":{"stated_usd_annual":253534,"is_top_pay":true},"html_url":"https://alion.io/job/amgen-principal-machine-learning-engineer-2","json_url":"https://alion.io/job/amgen-principal-machine-learning-engineer-2.json","meta":{"generated_at":"2026-09-28T04:42:49Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3021,"day_limit":5000,"remaining_today":1979,"minute_limit":60,"resets_at":"2026-09-29T00:00:00Z"}}}