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About This Role
About the Role:
This application is for a 6-month student role from January - June 2027. Resume review begins in October 2026.
Portfolio Data & Digital Innovation (PDDI) is a multidisciplinary team dedicated to transforming how Development leverages data, technology, and artificial intelligence to make faster, better-informed decisions. We bring together expertise in portfolio analytics, data management, AI/ML innovation, Planisware technology, and digital product delivery to create scalable solutions that improve portfolio visibility, streamline operations, and accelerate decision-making. Through initiatives such as Project FORWARD, AI-powered automation, portfolio intelligence tools, and a connected data foundation, PDDI partners across Development, Finance, IT, and other functions to drive innovation, strengthen governance, and enable a more data-driven and agile organization.
What You’ll Do:
As a member of the Portfolio Data & Digital Innovation (PDDI) team, you will help shape the future of data-driven decision making across Biogen's R&D portfolio. Working alongside data scientists, portfolio strategists, data engineers, and business leaders, you will develop AI-powered solutions that improve portfolio visibility, accelerate decision-making, enhance data quality, and streamline operational workflows. You will contribute across the full solution lifecycle, from data acquisition and engineering to model development, deployment, monitoring, and continuous improvement.
Key responsibilities include:
- Collaborate with data scientists, portfolio analysts, and subject matter experts to develop and deploy innovative AI and analytics solutions.
- Design and implement machine learning, predictive analytics, and decision-support models that address real-world portfolio and operational challenges.
- Develop interactive dashboards, reporting tools, and data products that translate complex data into actionable insights.
- Research and evaluate emerging AI, machine learning, and agentic AI technologies and identify opportunities to apply them within pharmaceutical R&D.
- Partner with stakeholders across Development, Portfolio Management, and Operations to understand business needs and deliver scalable, high-impact solutions.
- Contribute to the design and maintenance of data pipelines, AI platforms, and software products that support enterprise decision-making.
Promote responsible AI practices, model transparency, and continuous learning through experimentation and innovation. *
Example projects may include:
- Building AI agents that automate portfolio reporting, risk identification, and data reconciliation workflows.
- Developing predictive and prescriptive analytics to support portfolio prioritization and strategic planning.
- Developing scalable data pipelines and ETL processes that integrate data from Planisware, quality systems, financial systems, and other enterprise platforms.
- Supporting the design, optimization, and governance of Biogen's Snowflake-based data ecosystem, enabling trusted, AI-ready data products and analytics.
- Creating machine learning models and decision-support tools for resource forecasting, quality analytics, and operational intelligence.
Developing Project FORWARD capabilities that connect data, AI, and workflow automation to enable a more proactive and intelligent portfolio management ecosystem. *
Who You Are:
You are a curious, collaborative, and impact-oriented candidate who enjoys solving complex business problems through data, analytics, and AI. You combine strong technical skills with a passion for translating innovative ideas into practical solutions that create measurable business value.
Qualifications:
- Proficiency in Python, R, or another programming language commonly used for data science and analytics.
- Strong foundation in machine learning, statistics, and data analysis.
- Familiarity with NLP, LLMs, generative AI, and their underlying methodologies.
- Experience with modern machine learning and deep learning frameworks such as PyTorch, TensorFlow, Hugging Face, or similar platforms.
- Experience building and maintaining data pipelines, ETL workflows, and data integration processes.
- Proficiency in SQL and experience working with enterprise-scale relational and cloud-based data platforms.
- Familiarity with Snowflake, Databricks, or other modern cloud data warehousing technologies.
- Understanding of data modeling concepts, including dimensional modeling, star schemas, and data architecture best practices.
- Ability to communicate technical concepts and analytical findings to both technical and non-technical audiences.
- Strong problem-solving skills and a desire to work collaboratively in a multidisciplinary environment.
Passion for learning, experimenting with emerging AI technologies, and applying them to business challenges. *
Preferred Qualifications:
- Experience developing AI-powered applications, decision-support systems, or data products.
- Experience with Monte Carlo simulation, optimization, forecasting, or decision science methodologies.
- Familiarity with cloud platforms, GitHub, CI/CD pipelines, k8s, containerization, or Linux environments.
- Experience with data visualization and product development frameworks such as Streamlit, Power BI, or similar technologies.
- Experience in pharmaceutical, biotechnology, healthcare, quality, compliance, or R&D portfolio analytics.
Demonstrated interest in reproducing and implementing state-of-the-art AI and machine learning research. *
To participate in the Biogen Internship or Co-op Program, students must meet the following eligibility criteria:
- Legal authorization to work in the U.S.
- At least 18 years of age prior to the scheduled start date.
Be currently enrolled in an accredited community college, college, university or skills program/apprenticeship. *
Education Requirements
Currently pursuing a Master’s degree in Data Science, Statistics, Bioinformatics, Computer Science, Computational Biology, or related field.
Job Level: Internship
Additional Information
The base compensation range for this role is: $29.00-$29.00
The actual hourly wage offered will consider the candidate’s current academic level and degree candidacy, inclusive of Associate, Bachelor’s, Master’s, JD, MD, PhD and MBA programs. It will comply with state or local minimum wage requirements specific to the job location.
In addition to compensation, Biogen offers a range of benefits designed to support our educational employees, including, but not limited to:
- Company paid holidays
- Commuter benefits
- Employee Resource Groups participation
80 hours of sick time per calendar year *
Why Biogen?
We are a global team with a commitment to excellence, and a pioneering spirit. As a mid-sized biotechnology company, we provide the stability and resources of a well-established business while fostering an environment where individual contributions make a significant impact. Our team encompasses some of the most talented and passionate achievers who have unparalleled opportunities for learning, growth, and expanding their skills. Above all, we work together to deliver life-changing medicines, with every role playing a vital part in our mission. Caring Deeply. Achieving Excellence. Changing Lives.
At Biogen, we are committed to building on our culture of inclusion and belonging that reflects the communities where we operate and the patients we serve. We know that diverse backgrounds, cultures, and perspectives make us a stronger and more innovative company, and we are focused on building teams where every employee feels empowered and inspired. Read on to learn more about Biogen.
All qualified applicants will receive consideration for employment without regard to sex, gender identity or expression, sexual orientation, marital status, race, color, national origin, ancestry, ethnicity, religion, age, veteran status, disability, genetic information or any other basis protected by federal, state or local law. Biogen is an E-Verify Employer in the United States.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Biogen, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Biogen AI Hiring
Biogen has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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