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About This Role
### Drive meaningful impact across the life sciences industry.
As our next AI Senior Manager, you will contribute to solving meaningful challenges across the life sciences industry. We offer specialised management consulting services, working with top\-tier life sciences companies as well as biotech startups — locally and globally. From strategy development to hands\-on implementation, we support our clients were impact matters most.
About the role
As a AI Senior Manager, you will work closely with clients and colleagues to address complex challenges across the life sciences value chain. You will take ownership of your work and contribute to delivering solutions that create real, lasting impact.
Your responsibilities include:
- Partner with senior client stakeholders to define AI strategy and implementation roadmaps across clinical development functions
- Translate organisational goals and clinical priorities into actionable AI use cases, sequenced by value and feasibility
- Act as a trusted advisor to client leadership, guiding decisions on platform selection, build\-vs\-buy, and AI governance
- Lead discovery and assessment engagements to evaluate current\-state capabilities and define a path to AI\-enabled operations
- Own end\-to\-end delivery of AI \& tech implementation projects — managing scope, timeline, budget, risks, and stakeholder expectations
- Be hands\-on where it counts: reviewing solution design, validating AI outputs, configuring platforms, and quality\-assuring deliverables
- Lead cross\-functional delivery teams spanning business analysts, data scientists, engineers, and clinical SMEs
- Drive adoption of AI tools and platforms through structured change management, training, and hypercare support
- Ensure all implementations meet applicable quality, compliance, and regulatory standards (GxP, data privacy, AI validation frameworks)
- Mentor and develop junior team members, fostering a culture of high performance, curiosity, and continuous improvement
- Contribute to the growth of our AI practice through thought leadership, methodology development, and internal knowledge sharing
- Support business development by leading or contributing to proposals, client presentations, and pipeline conversations
- Help define and refine our delivery approach for clinical AI, including reusable assets, frameworks, and accelerators
You bring:
- Deep functional knowledge across one or more clinical areas: clinical operations, clinical data management, trial design, pharmacovigilance, or regulatory
- Strong project and programme management capability — able to run complex, multi\-workstream engagements with rigour and confidence
- Hands\-on experience with AI or ML platforms used in clinical settings (e.g. for trial optimisation, NLP, predictive analytics, document intelligence)
- The credibility and communication skills to engage C\-suite stakeholders and technical teams with equal effectiveness
- Understanding of GxP principles and the regulatory considerations relevant to AI deployment in clinical contexts
- Proven ability to lead, inspire, and develop high\-performing, geographically distributed teams
- Prior experience at a Life Sciences technology vendor, or management consultancy with a clinical AI focus
- Familiarity with leading clinical AI platforms and eClinical ecosystems (e.g. Veeva, Medidata, Oracle Health Sciences, emerging AI\-native tools)
- Experience designing or overseeing AI validation and qualification processes in regulated environments
- Knowledge of data standards relevant to clinical trials (CDISC, FHIR, HL7\)
- Bachelor’s degree in Life Sciences, Engineering, Computer Science, or related field; advanced degree is a plus
This position is offered in United States and Canada.
Who is BASE life science?
Founded in 2007, BASE life science is a business and technology consulting enterprise working exclusively in the life sciences industry.
With headquarters in Denmark and offices across Europe and the United States, we support clients globally. We work closely with our customers to identify, develop, and implement initiatives that drive growth, efficiency, and meaningful change. Through partnerships with leading solution providers such as Veeva, Salesforce, IQVIA, Benchling, and MediSpend, we help our clients unlock the full potential of modern technologies.
Why join BASE Life Science?
At BASE, you work on meaningful, complex challenges at the intersection of technology, science, and business. We trust our people with real responsibility and support them in turning ambition into impact.
You will be part of a collaborative, high‑performing environment where:
- Ambition is encouraged through ownership and continuous learning
- Execution matters — we focus on delivering solutions that work in practice
- Team spirit and trust guide collaboration across roles, countries, and disciplines
- Openness means your ideas are heard, you are challenged constructively, and supported as you grow
Alongside this, we offer:
- Support for health and wellbeing, covering physical, mental, and social needs
- Flexible ways of working, built on trust, autonomy, and balance
- Ongoing learning and professional development throughout your career
- A modern work setup, with the tools and equipment needed to do great work
- Recognition of performance and impact, linked to contribution and results
- The opportunity to work on challenges that make a meaningful difference for patients and healthcare systems worldwide
Explore our work, values, and people at www.baselifescience.com
Interested?
If you find this position intriguing, don’t delay—submit your application in English at your earliest convenience. We are continuously reviewing and assessing all incoming candidates and eagerly await your application.
At BASE Life Science, we value diverse backgrounds, experiences, and perspectives. Employment decisions are based solely on qualifications, merit, and business needs
By submitting your application, you consent to the processing of your personal data by BASE life science for the purposes of recruitment and selection. This includes the collection, storage, and use of your personal data as outlined in our Privacy Policy.
Apply#### Location
United States
#### Application Deadline
As soon as possible
#### Contact
People \& Culture,
Apply
#### Location
United States
#### Application Deadline
As soon as possible
#### Contact
People \& Culture,
Apply
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 BASE life science, 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.
BASE life science AI Hiring
BASE life science has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national median.
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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