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About This Role
Date: Jun 4, 2026
Location: US\-NY\-Rochester, US
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. Objective:
This role sits at the intersection of the plant floor and the data platform. We are building our data and AI capability from the ground up — replacing fragmented systems, manual CSV exports, and information gaps with reliable pipelines, clean data, and intelligent tools. The first mission is foundational: build the infrastructure that gives operations, quality, and maintenance teams the visibility they need to make faster, better decisions. From there, this role will lead the application of agentic AI and ML platforms to automate workflows, surface insights, and scale impact across the plant. Success is measured by solutions shipped, adopted, and making a measurable difference on the floor.
Key Responsibilities:
Data Foundation \& Pipeline Engineering
- Assess current plant data infrastructure — identify where data is missing, poorly structured, or trapped in manual processes.
- Design and build automated pipelines that replace manual CSV exports with sustainable, auditable data flows.
- Integrate data from ERP, MES, QMS, and shop floor systems into unified reporting environments.
- Ensure all pipelines and collection methods meet GxP and ISO documentation and validation requirements.
Analytics, Visualization \& AI
- Build dashboards and reports that translate manufacturing data into actionable insights for operators, supervisors, and leadership.
- Develop Power Apps and Power Automate workflows that reduce manual effort for quality, maintenance, and operations teams.
- Apply manufacturing knowledge — OEE, yield, cycle time, process variation — to ensure analytics are practically useful.
- Build AI\-powered agents and workflows using Microsoft Copilot Studio or Claude, starting with shift handover, production reporting, and maintenance tracking.
- Integrate Azure AI services (Azure OpenAI, AI Search, Document Intelligence) to create intelligent tools that reason, retrieve, and respond.
- Ensure all AI solutions are validated, documented, and auditable in a GxP/ISO environment.
Collaboration \& Continuous Improvement
- Work directly with plant supervisors, maintenance leads, quality engineers, and operations personnel to gather requirements and validate solutions.
- Champion data\-driven decision\-making by building trust in data quality through consistent, reliable delivery.
- Collaborate with IT Infrastructure Operations on hosting, access controls, data architecture, and compliance.
- Lead projects for implementing or improving QMS, analytics, and automation software from requirements through sustainment.
Education/Experience:
- Bachelor’s degree in Engineering, Computer Science, Information Systems, Industrial Engineering, or related field required.
- 3\+ years of hands\-on relevant experience strongly preferred; experience with manufacturing data systems, including ERP, MES, or QMS platforms.
- Demonstrated history of delivering working solutions \- dashboards, automations, integrations, or AI tools actively used in production.
Skills:
Required
- Manufacturing fluency — genuine understanding of manufacturing processes, quality systems, and operational workflows; able to translate floor requirements into technical solutions.
- Power BI, Power Apps \& Power Automate — experienced building workflow automation and plant\-facing applications.
- Data pipeline \& integration — design data collection structures, build integrations, and replace manual processes with reliable automated flows.
- Agentic AI — hands\-on experience building AI agents or Copilot solutions; not just using LLMs but building with them.
- Systems integration — connecting ERP, MES, QMS, and shop floor systems into unified reporting environments.
- Regulated environment — comfortable within GxP and/or ISO frameworks where solutions must be validated and documented.
- Project ownership — able to manage projects independently, set priorities, and deliver without waiting for complete direction.
- Communication — equally credible with a maintenance technician on the floor and with plant leadership.
Preferred
- SQL — writing and modifying queries for extraction, transformation, and validation.
- Azure AI services — Azure OpenAI, AI Search, Document Intelligence, or Data Factory.
- Python — basic scripting for data manipulation, pipeline automation, or API integration.
- Lean / Six Sigma — Green Belt or Black Belt.
- Shop floor data systems — historian platforms, SCADA, or similar real\-time sources.
- Life sciences / pharmaceutical manufacturing — familiarity with FDA\-regulated environments and data integrity requirements
This position may be available in the following location(s): Rochester, NY
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 $81,600 and $110,400\. 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 $81K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($96K) sits 47% below the category median. Disclosed range: $81K to $110K.
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.
Bausch + Lomb AI Hiring
Bausch + Lomb has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rochester, NY, US. Compensation range: $110K - $110K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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