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About Us
At BGBx, we're driven by a simple idea: breakthrough thinking creates breakthrough impact. As an independent commercial solutions partner to pharmaceutical and life science companies, we bring together strategists, scientists, communicators, creatives, technologists, and data experts to tackle some of healthcare's most important challenges.
Our teams work across consulting and communications to help clients shape strategy, launch innovations, engage stakeholders, and drive meaningful results throughout the product lifecycle. The work is complex, fast\-moving, and deeply connected to improving lives, creating opportunities for curious minds to make a real difference every day.
If you're energized by collaboration, inspired by innovation, and motivated by work that matters, you'll find a place to grow, contribute, and make a meaningful impact at BGBx.
SVP, AI Transformation
Position Overview
BGBx is putting AI at the center of how the firm works \- how we advise, how we create, how we deliver. This is the role that makes that real. We're looking for a leader who takes the AI agenda from ambition to landed outcome \- owning the hardest initiatives end to end and getting them across the line, not steering from the sidelines.
Reporting to and partnering closely with the EVP, AI Transformation, you lead the execution engine behind BGBx's AI\-first operating model \- turning strategy into reality by owning the delivery of the AI initiatives BGBx takes on, and closing the loop by feeding field\-level insight back into how the strategy and the broader transformation approach take shape.
This is a role for someone equally comfortable in three modes: contributing sharp thinking to the strategy, leading complex programs to completion, and reading the signal from how the work actually lands in order to sharpen what comes next. It is a rare seat for someone who wants the reach of shaping enterprise strategy and the satisfaction of personally leading the programs that bring it to life.
Key Responsibilities
- Own delivery of the AI programs and initiatives BGBx takes on, from mandate to deployment \- including planning, sequencing, execution, and outcomes across the enterprise
- Drive delivery individually and through others, coordinating internal teams, external partners, and vendors so initiatives ship on time, at quality, and with the adoption needed to stick.
- Contribute rigorous thinking to the AI transformation strategy and shape the options and recommendations that set its direction.
- Serve as the leadership team's read on what is actually happening in the field \- surfacing what is working, what is not, and what is emerging, and translating that signal into how BGBx refines its strategy and approach.
- Own the delivery relationship with AI platform and implementation partners, holding them to committed deliverables and ensuring their work aligns to BGBx's strategy and standards.
- Build and lead the team and operating rhythms needed to deliver AI initiatives repeatedly and reliably as the transformation scales.
Preferred Qualifications
- 12\+ years of professional experience, including at least 5 years leading complex programs, delivery, or transformation initiatives end to end.
- 3\+ years managing and developing teams or leading cross\-functional delivery groups.
- Bachelor's degree required; advanced degree (MBA, or a Master's in a business, technical, or scientific discipline) preferred.
- Demonstrated experience leading or delivering AI, data, or technology\-enabled initiatives in a complex organization.
- Experience managing external vendors or delivery partners against defined deliverables, timelines, and budgets.
- Experience in pharmaceutical, life sciences, medical communications, advertising, or another regulated industry strongly preferred.
- A builder\-operator who leads through programs and people, energized by owning execution and leading transformation from the front.
- Strong working fluency in AI and LLM ecosystems, agentic systems, and workflow automation \- enough to lead delivery credibly and challenge technical partners.
- Program, project, and change\-management expertise, including driving adoption of new ways of working.
- Sharp judgment for signal versus noise, and the discipline to translate ground\-level observation into actionable insight.
- Executive\-level communication skills and a bias toward ownership, follow\-through, and getting things landed.
Salary Range: $210,000 \- $250,000
BGBx is headquartered in New York City, and the salary range listed reflects the expected base compensation for this role in the New York City metropolitan area. This position may be performed remotely within the United States. For candidates located outside the New York City area, compensation will be adjusted to reflect the applicable market for the employee's primary work location. Final compensation will be determined based on geographic location, experience, qualifications, and other job\-related factors.
What We Offer
At BGBx, we invest in our people because exceptional talent drives exceptional work. We provide a competitive rewards package designed to support your health, well\-being, professional growth, and life outside of work.
Our benefits package typically includes health care insurance, retirement plans, generous paid time off and holidays, wellness benefits, life insurance, learning and development opportunities, and other resources that support our employees throughout their careers. Additional benefits may be available based on your location. Beyond benefits, you'll find a culture built on collaboration, innovation, inclusion, and the opportunity to make a meaningful impact on healthcare and the people it serves.
*BGBx is an equal opportunity employer. All applicants will be considered without regard to race, color, religion, sex, age, national origin, citizenship status, sexual orientation, disability, veteran status or any category or class of person protected by law.*
Salary Context
This $210K-$250K range is above the 75th percentile 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 BGBx, 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 in Demand for This Role
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. This role's midpoint ($230K) sits 7% above the category median. Disclosed range: $210K to $250K.
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.
BGBx AI Hiring
BGBx has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $250K - $250K.
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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