{"id":3589071,"url":"https://alion.io/job/amgen-reinforcement-learning-engineer","title":"Reinforcement 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":"A","score":85,"open_postings":346,"ghost_share":0,"stale_share":0.575,"repost_share":0.014,"time_to_fill_p50_days":43,"computed_at":"2026-11-03T05:54:40Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":27000,"max_usd":56000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":41},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"AI Agents","optional":true},{"name":"Gymnasium","optional":true},{"name":"Hadoop","optional":true},{"name":"Ray","optional":true},{"name":"RLlib","optional":true},{"name":"Spark","optional":true},{"name":"Stable-Baselines3","optional":true}],"status":"live","first_seen_at":"2026-10-26T00:00:00Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-11-04T05:28:39Z","board_verified":true,"closed_at":null,"days_open":9,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":9},"description":"Career Category\nEngineeringJob Description\nABOUT AMGEN\nAmgen 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 on the cutting-edge of innovation, using technology and human genetic data to push beyond what’s known today.\nABOUT THE ROLE\nRole Description:\nWe are seeking a highly skilled Reinforcement Learning Engineer or Machine Learning Engineer with experience in reinforcement learning (RL) and DevOps/MLOps to join the AI & Data Science team. You will work with data scientists and engineers to build and scale production ML systems using feedback and measured outcomes. You will be expected to own and drive forward scoped projects with technical guidance. The role offers the opportunity to apply reinforcement learning and machine learning methods to practical problems, with appropriate human review and safety controls.\nRoles & Responsibilities:\nPartner with data scientists and software engineers to frame business problems as reinforcement learning and identify suitable approaches.\nImplement and evaluate reinforcement learning methods, such as contextual bandits, policy learning, or preference-based approaches, with technical guidance, for applications that learn from feedback.\nBuild and scale Python-based ML systems from prototypes to production using frameworks such as PyTorch, scikit-learn, or other relevant machine learning libraries.\nDesign evaluation methods and analyze results using appropriate metrics, baselines, statistical methods, and offline or online experiments.\nWork with human and AI-generated feedback, evaluation data, and interaction traces to support model improvement and learning from preferences.\nImplement DevOps/MLOps best practices to build reliable, reproducible ML workflows, including experiment tracking, data and model versioning, testing, and deployment.\nCollaborate with product and platform teams to integrate models into AI applications and monitor their performance after deployment.\nDocument technical work, communicate findings, and keep current with relevant developments in reinforcement learning and machine learning.\nMust-Have Skills:\nSolid foundation in machine learning algorithms and techniques.\nExperience in MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow); Experience in DevOps tools (e.g., Docker, Kubernetes, CI/CD), including hands-on experience building and deploying ML systems.\nHands-on experience with RL concepts such as rewards, policies, value functions, explore-exploit tradeoff, and RL with human feedback.\nProficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).\nAbility to design experiments, analyze model behavior, and explain technical results clearly; strong problem-solving and collaboration skills; Ability to learn quickly; Excellent communication and interpersonal skills\nGood-to-Have Skills:\nFamiliarity with RL environments and libraries such as Gymnasium, Stable-Baselines3, Ray RLlib, TorchRL, or Vowpal Wabbit.\nExperience with big data technologies (e.g., Spark, Hadoop), and performance tuning in query and data processing.\nExperience with data engineering and pipeline development. \nFamiliarity with large language models, natural language processing, AI agents, or evaluation of generative AI systems.\nEducation and Professional Certifications\nMaster’s degree with 5+ years of experience in Computer Science, IT or related field \nOR\nBachelor’s degree with 7-9 + years of experience in Computer Science, IT or related field \n\nSoft Skills:\nExcellent verbal and written communication skills\nAbility to work effectively with cross-functional and global teams.\nAbility to own and drive scoped projects with technical guidance and regular feedback.\nCuriosity, initiative, and willingness to learn from feedback.\nAbility to organize work and manage multiple priorities.\nCollaborative approach and focus on shared team goals.\nAttention to detail when handling data, experiments, and technical documentation.\nAbility to present findings to technical and nontechnical audiences.\nStrong analytical, debugging, and problem-solving skills.\nEQUAL OPPORTUNITY STATEMENT\nAmgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.\nWe will ensure that individuals with disabilities are provided with 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 an accommodation.\nApply now for a career that defies imagination\nObjects in your future are closer than they appear. Join us.\ncareers.amgen.com\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.\nReady to Apply for the Job?\nWe highly recommend utilizing Workday's robust Career Profile feature to complete the application process. A link to update your profile is available when you click Apply. 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