Interested in this AI/ML Engineer role at Bausch + Lomb?
Apply Now →About This Role
Date: Aug 13, 2026
Location: US\-NJ\-Bridgewater, US
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Company: Bausch\+Lomb Companies Inc.
Bausch \+ Lomb (NYSE/TSX: BLCO) is a leading global eye health company dedicated to protecting and enhancing the gift of sight for millions of people around the world—from the moment of birth through every phase of life. Our mission is simple, yet powerful: helping you see better, to live better.
Our comprehensive portfolio of over 400 products is fully integrated and built to serve our customers across the full spectrum of their eye health needs throughout their lives. Our iconic brand is built on the deep trust and loyalty of our customers established over our 170\-year history. We have a significant global research, development, manufacturing and commercial footprint of approximately 13,000 employees and a presence in approximately 100 countries, extending our reach to billions of potential customers across the globe. We have long been associated with many of the most significant advances in eye health, and we believe we are well positioned to continue leading the advancement of eye health in the future.
Position Overview
Bausch \+ Lomb’s Global Digital and AI organization seeks a passionate and highly motivated AI and Data Standards and Enablement Lead. In this role, you will help scale Bausch \+ Lomb’s enterprise AI and data agenda responsibly, consistently, and practically by defining enterprise AI policies and standards, leading data governance alignment, operationalizing AI governance and use\-case approvals, enabling business adoption, and tracking measurable adoption and governance outcomes. This role will partner closely with Digital, AI, Enterprise Data Products, IT, Legal, Privacy, Security, Compliance, and business stakeholders to embed AI and data governance into day\-to\-day execution. This role reports to the Head of Enterprise Data Products.
Key Responsibilities
Define and operationalize enterprise AI policies, standards, playbooks, templates, and governance practices that support safe, scalable, and responsible AI adoption across the organization.
Lead AI use\-case intake, risk assessment, approval workflows, documentation requirements, and compliance monitoring to ensure alignment with enterprise governance expectations.
Partner with data owners, stewards, and Enterprise Data Products teams to establish governance frameworks, certified data product standards, and data lifecycle management practices.
Drive data quality, metadata, lineage, access governance, data contracts, and catalog adoption to improve trust, discoverability, and readiness of governed data assets.
Translate complex AI and data governance requirements into practical toolkits, training materials, decision frameworks, and business\-friendly guidance that accelerate adoption.
Develop and execute change management, communications, and stakeholder enablement programs that promote responsible AI practices and governed data usage.
Define, monitor, and report governance KPIs, adoption metrics, certification status, compliance trends, and data quality performance through dashboards and scorecards.
Facilitate governance forums, working groups, and cross\-functional decision\-making while serving as a trusted advisor to business teams adopting AI and data capabilities.
Required
Bachelor's degree in Information Systems, Data Management, Analytics, Computer Science, Business, or a related field.
4\+ years of experience in AI governance, data governance, data quality, data stewardship, analytics, enterprise enablement, digital transformation, or related disciplines.
Demonstrated experience building, managing, or supporting data governance, data stewardship, data quality, master data management, or data product governance programs.
Strong understanding of Responsible AI principles, including transparency, accountability, privacy, fairness, explainability, human oversight, security, and risk management.
Experience influencing and collaborating across business, technology, legal, privacy, security, compliance, data, and analytics stakeholders.
Proven ability to design governance processes, enablement programs, and metrics\-driven adoption strategies that support enterprise transformation initiatives.
Excellent communication, facilitation, and problem\-solving skills with the ability to simplify complex concepts for both technical and business audiences.
Specialized Training \& Skills
Working knowledge of AI governance frameworks, data governance disciplines, data ownership and stewardship models, RACI structures, metadata management, lineage, data quality controls, data contracts, and access governance.
Experience with AI use\-case intake processes, risk assessments, approval workflows, governance documentation standards, and monitoring practices.
Hands\-on familiarity with data catalog and governance platforms, including Microsoft Purview preferred; experience with Collibra, Alation, Atlan, or similar tools is a plus.
Ability to develop governance scorecards, dashboards, certifications, adoption metrics, and leadership reporting that measure effectiveness and business value.
Preferred
Advanced degree in Information Systems, Data Management, Analytics, Computer Science, Business, or a related field.
Experience supporting governance for analytics, machine learning, generative AI, large language models (LLMs), automation, and agentic AI use cases.
Experience in regulated industries such as pharmaceuticals, medical devices, life sciences, healthcare, financial services, or other compliance\-focused environments.
Relevant certifications in data governance, data management, analytics, AI governance, or related disciplines.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
For U.S. locations that require disclosure of compensation, the starting pay for this role is between $125,000\.00 and $155,000\.00\. The estimated salary range reflects an anticipated range for this position. The actual base salary offered may depend on a variety of factors.
U.S. based employees may be eligible for short\-term and/or long\-term incentives. They may also be eligible to participate in medical, dental, vision insurance, disability and life insurance, a 401(k) plan and company match, a tuition reimbursement program (select degrees), company holidays, and well\-being benefits, among others. U.S. based employees are also eligible to receive sick time, floating holidays and paid vacation.
Job Applicants should be aware of job offer scams perpetrated through the use of the Internet and social media platforms.
To learn more please read Bausch \+ Lomb's Job Offer Fraud Statement.
Our Benefit Programs: Employee Benefits: Bausch \+ Lomb
Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.
Salary Context
This $125K-$155K range is below the median 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 Bausch + Lomb, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 35% below the category median. Disclosed range: $125K 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.
Bausch + Lomb AI Hiring
Bausch + Lomb has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bridgewater, NJ, US. Compensation range: $155K - $155K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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