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About This Role
About This Role
The Data Science and Applied Machine Learning (DSAML) team develops internal software products that support critical decision\-making in a highly regulated environment. We apply rigorous software engineering practices across design, development, testing, documentation, and operations to deliver reliable, audit\-ready solutions that users genuinely value. We are seeking a Front\-End Design Engineer who can both design and implement intuitive user experiences for complex data\-driven applications. This role combines UX design and front\-end engineering, allowing you to take ideas from discovery and prototyping through production deployment while partnering closely with data scientists, engineers, and business stakeholders.
What You'll Do
- Design and build front\-end experiences for internal web applications
- Conduct user research, shadowing sessions, and design workshops
- Create wireframes, mockups, and interactive prototypes
- Develop production\-ready UI components and application features
- Collaborate with engineers and scientists to integrate front\-end and back\-end solutions
- Write automated tests ranging from unit to end\-to\-end coverage
- Document design decisions, implementation details, and testing strategies
- Participate in code reviews and documentation reviews across the team
- Improve usability of tools that support complex clinical and operational workflows
- Contribute to establishing front\-end and user experience best practices
Who You Are
You are passionate about creating intuitive, high\-quality user experiences that enable users to concentrate on the right parts of complex data and processes at the right time. You enjoy working across both design and engineering disciplines and thrive in collaborative environments where quality, documentation, and continuous improvement are valued. You are motivated by building tools that help accelerate scientific discovery and improve patient outcomes.
Required Skills
- Web application design and front\-end development experience
- Portfolio demonstrating UX/UI and implementation work
- Experience designing interfaces for complex data workflows
- Strong written communication and documentation skills
- Transferrable expertise in at least two frontend development frameworks or languages, React/JavaScript/TypeScript preferred
- Experience writing automated UI test suites
- Familiarity with Python
- Experience with Git and source control best practices
- Ability to give and receive constructive design feedback
- User\-centered design and requirements gathering experience
Preferred Skills
- Experience migrating applications between frameworks
- Front\-end analytics and telemetry implementation
- Streamlit experience
- Experience working in regulated industries
- 3\-5 years of combined design and engineering experience
Mobile application development experience
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Job Level: Management
Additional Information
The base compensation range for this role is: $116,000\.00\-$155,000\.00
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 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.
Salary Context
This $116K-$155K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($135K) sits 37% below the category median. Disclosed range: $116K to $155K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Biogen AI Hiring
Biogen has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $155K - $155K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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