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About This Role
As a Principal Machine Learning Engineer focused on Clinical Trial Forecasting and Optimization, you will be an integral part of our Quantitative Sciences Development team, dedicated to transforming patient lives through advanced disease research. Your role involves leveraging applied machine learning to develop innovative tools and methodologies, improving data\-driven decision\-making for clinical operations. You will lead projects that enhance Biogen's capacity to bring products to market efficiently, working closely with cross\-functional teams. This position is pivotal in driving our strategic vision to establish Biogen as a leader in neuroscience, neurology, immunology, and rare diseases. Your contributions will directly influence our corporate vision and strategy, helping to advance research and patient empowerment while fitting seamlessly into our collaborative environment.
What You’ll Do:
· Design, develop, and implement complex machine learning pipelines for clinical trial forecasting and optimization.
· Engineer forecasting models and features to enhance clinical operations.
· Conduct technical, analytical, and performance validation analysis using CI/CD and MLOps pipelines.
· Review code and code artifacts to ensure high\-quality algorithmic designs compliant with industry standards.
· Integrate and develop novel sensor\-based measurement solutions.
· Improve tools and applications for clinical operations forecasting.
· Serve as a key stakeholder and subject matter expert in internal and external alliances.
· Collaborate with diverse teams to advance clinical trial optimization strategies.
Who You Are: You possess a deep passion for advancing human disease understanding through digital technologies. Your intrinsic motivation drives you to solve complex problems, offering innovative perspectives and exercising sound judgment. You thrive in collaborative environments, effectively communicating and building consensus across diverse teams. Your expertise in data science and machine learning, coupled with your ability to manage interdisciplinary projects, positions you as a valuable asset to our organization.
Required Skills:
· PhD in biomechanical engineering, data science, bioinformatics, computer science, or applied mathematics; or Master’s degree with additional experience.
· At least 5 years of experience in Pharma/Biotech/Tech industry or significant academic/clinical experience.
· Expertise in data visualization, application development with machine learning endpoints, and user experience design.
· Proficiency in programming with Python and relevant libraries (numpy, pandas, scipy, statsmodels, scikit\-learn, TensorFlow, matplotlib, Plotly).
· Experience managing complex interdisciplinary research and development projects.
Preferred Skills:
· Strong background in temporal forecasting methodologies.
· Experience in building consensus within technology and business teams.
· Familiarity with emerging technologies in clinical trials.
Job Level: Management
Additional Information
The base compensation range for this role is: \-
Base salary offered is determined through an analytical approach utilizing a combination of factors including, but not limited to, relevant skills \& experience, job location, and internal equity.
Regular employees are eligible to receive both short term and long\-term incentives, including cash bonus and equity incentive opportunities, designed to reward recent achievements and recognize your future potential based on individual, business unit and company performance.
In addition to compensation, Biogen offers a full and highly competitive range of benefits designed to support our employees’ and their families *physical, financial, emotional,* and *social well\-being* ; including, but not limited to:
- Medical, Dental, Vision, \& Life insurances
- Fitness \& Wellness programs including a fitness reimbursement
- Short\- and Long\-Term Disability insurance
- A minimum of 15 days of paid vacation and an additional end\-of\-year shutdown time off (Dec 26\-Dec 31\)
- Up to 12 company paid holidays \+ 3 paid days off for Personal Significance
- 80 hours of sick time per calendar year
- Paid Maternity and Parental Leave benefit
- 401(k) program participation with company matched contributions
- Employee stock purchase plan
- Tuition reimbursement of up to $10,000 per calendar year
- Employee Resource Groups participation
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 our DE\&I efforts.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
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 $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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