Sr Machine Learning Engineer
About this opportunity
Career Category
Engineering
Job Description
Sr Machine Learning Engineer
ABOUT AMGEN
Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, making 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.
ABOUT THE ROLE
Role Description:
Let’s do this. Let’s change the world. We are looking for a highly motivated expert Data Engineer to design and develop scalable, secure, and reliable data pipelines and ingestion solutions that power k nowledge layer s and assistant experiences via generative AI solutions for our Manufacturing Applications Product Team. The ideal candidate will be responsible for designing, developing, and optimizing data pipelines, data integration frameworks, and metadata-driven architectures that enable seamless data access and analytics for Manufacturing and Operations use cases. This role requires deep expertise in big data processing, distributed computing, data modeling, LLMs, vector stores , productized assistant workflows. and governance frameworks to support self-service analytics, AI-driven insights, and enterprise-wide data management.
Roles & Responsibilities
Design, develop, and maintain complex ETL/ELT data pipelines in Databricks using PySpark , Scala, and SQL to process large-scale datasets.
Champion ML/LLM feature engineering including data ingestion, embeddings/vector DBs, RAG/LLM serving, latency optimization , and knowledge graph/metadata.
Build highly efficient data pipelines to migrate and deploy complex data across systems, with an understanding of biotech/pharma/manufacturing or related domains.
Design and implement solutions to enable secure access, logging , privacy controls. unified data access, governance, and interoperability across hybrid cloud environments.
Ingest and transform structured and unstructured data from databases (PostgreSQL, MySQL, SQL Server, MongoDB, etc.), APIs, logs, event streams, images, PDFs, and third-party platforms.
Ensure data integrity, accuracy, and consistency through rigorous quality checks and monitoring.
Innovate, explore, and implement new tools and technologies to enhance efficient data processing.
Proactively identify and implement opportunities to automate tasks and develop reusable frameworks.
Work in an Agile and Scaled Agile ( SAFe ) environment, collaborating with cross-functional teams, product owners, and Scrum Masters to deliver incremental value.
Use JIRA, Confluence, and Agile DevOps tools to manage sprints, backlogs, and user stories.
Support continuous improvement, test automation, and DevOps practices in the data engineering lifecycle.
Collaborate and communicate effectively with product teams and cross-functional teams to understand business requirements and translate them into technical solutions.
Must-Have Skills
Hands-on experience in data engineering technologies such as Databricks, PySpark , SparkSQL , Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies.
Proficiency in workflow orchestration and performance tuning on big data processing.
Strong understanding of AWS services.
Ability to quickly learn, adapt, and apply new technologies .
Strong problem-solving and analytical skills.
Excellent communication and teamwork skills.
Experience with Scaled Agile Framework ( SAFe ), Agile delivery practices, and DevOps practices.
Experience with streaming technologies such as Apache Kafka, Debezium , or similar platforms for real-time data processing and integration.
Good-to-Have Skills
Experience with AI assisted code development using tools like GitHub Copilot, Cursor, Claude Code.
Collaboration with ML engineers, prompt engineers, P roduct Managers and Owners.
Data engineering experience in biotechnology or pharma industry.
Experience in writing APIs to make data available to consumers.
Experience with SQL/NoSQL databases, vector databases for large language models.
Experience with data modeling and performance tuning for both OLAP and OLTP databases.
Experience with software engineering best practices, including version control (Git, Subversion, etc.), CI/CD (Jenkins, Maven, etc.), automated unit testing, and DevOps.
Experience with manufacturing related data sources like SCADA, Data Historian is a plus
Education and Professional Certifications
Doctorate Degree OR
Master’s degree with 6-8 years of experience in Computer Science, IT or related field OR
Bachelor’s degree with 8-10 years of experience in Computer Science, IT or related field
Soft Skills
Excellent analytical and troubleshooting skills.
Strong verbal and written communication skills .
Ability to work effectively with global, virtual teams.
High degree of initiative and self-motivation.
Ability to manage multiple priorities successfully.
Team-oriented, with a focus on achieving team goals.
Ability to learn quickly, be organized, and detail-oriented .
Ready to Apply for the Job?
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Job details
How this role compares
Computed from every other active Information Technology role in our database, not just this employer's listings.
We currently track 378 comparable Senior Information Technology roles across 34 biopharma companies.
Salary context
45 of 378 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 378 comparable roles
+ 15 more countries
Seniority mix
378 of 378 peers have a known seniority level
Therapeutic area mix
0 of 378 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
No peers with a known therapeutic area yet.
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.