Interested in this AI/ML Engineer role at Cabot Corporation?
Apply Now →About This Role
Cabot Corporation (NYSE: CBT) is a leading global specialty chemicals and performance materials company headquartered in Boston, Massachusetts, USA.
Our businesses deliver a broad range of products and solutions to customers in every corner of the globe, serving the transportation, infrastructure, environment and consumer industry sectors. We bring the power of innovative chemistry to solve customers' challenges today while preparing them to meet tomorrow’s needs. Our commitment to innovation is driven by a passion to advance our customers' businesses through our deep understanding of their applications and the global trends that impact their operations.
If you do not meet every requirement, or your experience is slightly different that what we have listed, we still encourage you to apply!
Your Role at Cabot
Cabot is establishing an enterprise AI Program Management Office (PMO) to bring structure, prioritization, and execution discipline to AI initiatives across the company.
The AI PMO Director will translate enterprise strategy into a prioritized AI portfolio and ensure the effective execution of high\-quality AI programs. The role serves as the central integrator across businesses, functions, and Digital—bringing clarity, rigor, and transparency to AI efforts across the enterprise.
This role works closely with Cabot’s AI SteerCo and, by extension, its Executive Committee (ExCO) to facilitate alignment on priorities while ensuring disciplined execution.
Accountability model:
- The AI PMO Director is accountable for evaluation, prioritization, and execution discipline of AI initiatives
- Business sponsors are accountable for AI initiative outcomes , including value realization, adoption, and sustained impact
- The Digital function is accountable for ensuring AI solutions are technically sound, secure and compliant, and aligned with enterprise architecture, data platforms, and standards including security standards . This group either builds the solution or orchestrates the build.
This is a Director \-level individual contributor role requiring strong execution leadership, enterprise perspective, and the ability to influence senior stakeholders.
How You Will Make an Impact
Enterprise AI Strategy Alignment and Facilitation
- Translate Cabot’s enterprise strategy into a structured, actionable AI portfolio aligned to company strategic priorities
- Facilitate structured discussions with ExCo and the AI SteerCo to drive alignment on priorities and sequencing
- Synthesize business needs and constraints into clear AI prioritization options
- Frame tradeoffs, value cases, and resource implications to support decision\-making
- Ensure proposed AI initiatives are credible, scalable, and aligned to enterprise value, constructively challenging assumptions where needed
Enterprise AI Portfolio Leadership
- Develop and maintain a prioritized portfolio of AI initiatives aligned to enterprise value
- Lead use\-case identification and shaping with business and functional leaders
- Maintain visibility into portfolio progress, risks, dependencies, and tradeoffs
- Identify duplication and opportunities to streamline AI activity across the organization
- Serve as Program Director for select enterprise\-critical AI initiatives
- Lead cross\-functional teams through deployment and scale
- Ensure delivery quality, consistency, and alignment to intended business use case s
Execution Leadership and Delivery
- Own execution accountability for prioritized AI initiatives, ensuring delivery to a high standard
- Establish and enforce program management disciplines, including governance, milestones, and stage gates
- Lead cross\-functional coordination across business sponsors, Digital, and support functions
- Proactively identify and resolve execution risks, bottlenecks, and resource constraints
- Ensure effective implementation and transition of solutions into business operations
Governance and Operating Model
- Establish and operate the AI SteerCo governance model and decision framework
- Enable structured prioritization, sequencing, and funding decisions
- Drive a consistent operating cadence, including:
- Annual portfolio and funding alignment with ExCo
- Quarterly prioritization and value reviews with SteerCo
- Monthly execution reporting to project sponsors
What You Will Bring to Cabot
Experience
- 10\+ years of experience in program management, transformation, or large\-scale initiative delivery
- Demonstrated success leading complex, cross\-functional programs in a global organization
- Experience working closely with senior executives and influencing enterprise\-level decisions
- Experience with digital, data, analytics, or AI\-enabled initiatives strongly preferred
Capabilities
- Strong execution leadership with a consistent track record of delivering complex initiatives successfully
- Ability to translate strategy into structured, actionable plans and execution roadmaps
- Excellent stakeholder management and influencing skills without direct authority
- Strong business and financial acumen, including value case development and tracking
- Demonstrated fluency in AI and advanced analytics, including the ability to:
- Evaluate and shape AI use cases in partnership with the business
- Differentiate high\-value, scalable initiatives from low\-value or experimental efforts
- Assess feasibility, including data readiness, integration complexity, and adoption challenges
- Engage effectively with technical teams while maintaining a business\-first perspective
Organization Context
- This role sits *outside* of the company’s Digital organization, reporting to the SVP who oversees Digital and Global Business Services (GBS)
- Operates as an enterprise role across all businesses and functions
- Works closely with:
- AI SteerCo ( comprised of ExCo members and subject matter experts)
- Executive Committee (ExCo)
- Business and functional leadership teams
- Digital and delivery teams
- Finance
How We Will Support Your Success
- The actual compensation offered to the successful candidate will depend on the candidate's skills, qualifications, experience and location. The anticipated pay for this position is between $228,700 to $331,600, with bonus based on personal and company performance.
- In addition to base salary, all positions are eligible for health benefits on the first day of employment, annual bonus based on company performance, local benefits, and opportunities for professional growth and development.
- Dynamic, Flexible, Hard Working, Team Environment – We are busy, collaborative, growing, and we are doing really meaningful work.
- Feedback – we are committed to giving and receiving feedback in a direct and open fashion.
- Support – you are part of a team and deserve to feel encouraged and supported. You will be part of a team that cares about you personally and professionally. Our success depends on your success.
At Cabot, we bring the power of innovative chemistry and a spirit of partnership with our customers to advance solutions that will enable a sustainable future. Our strength in research and development is a major reason why we have been an industry leader for more than 135 years in products such as reinforcing and specialty carbons, battery materials, aerogel, fumed metal oxides, inkjet colorants, masterbatches and conductive compounds.
Our employees around the world are united by our shared purpose: Creating materials that improve daily life and enable a more sustainable future. Through our corporate strategy, “Creating for Tomorrow,” we are focused on our core strengths to lead in performance and sustainability – today and into the future.
*EEO/AA* *Employer/Vet/Disabled/RC14001*
*Realizing we function better together than individually, Cabot Corporation is proud to be an equal opportunity employer. We are committed to fostering an inclusive culture that embraces our differences and to empower employees to achieve exceptional results, without consideration of sex, race, color, religion, national origin, citizenship, age, disability, marital or veteran status, sexual orientation, gender identity or expression, or any other legally protected categories. This includes providing reasonable accommodation for employees’ and applicants’ disabilities or religious beliefs and practices, in accordance with applicable law.*
*Cabot is committed to supporting and maintaining a safe and environmentally focused working environment. All employees are expected to uphold this commitment in every position.*
*Nondiscrimination Policy with Respect to Discussion of Pay*
*This is to advise that it is the policy of Cabot Corporation not to discharge or in any other manner discriminate against any employee or applicant for employment because such employee or applicant has inquired about, discussed, or disclosed the compensation of the employee or applicant or another employee or applicant. This policy, however, shall not apply to instances in which an employee who has access to the compensation information of other employees or applicants as part of that employee's essential job functions (such as in payroll or HR) discloses the compensation of such other employees or applicants to individuals who do not otherwise have access to such information, unless that disclosure is in response to a formal complaint or charge, in furtherance of an investigation, proceeding, hearing or other action, including an investigation conducted by Cabot, or otherwise as required by law.*
*Pre\-Employment Drug Testing and Background Checks*
*Cabot is proud to operate as a drug\-free workplace. All applicants conditionally offered a position must (a) complete and pass a background check and (b) complete a drug test and receive an acceptable test result. For certain manufacturing, quality and production positions, a pre\-employment physical may be required.*
Salary Context
This $228K-$331K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Cabot Corporation, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($280K) sits 28% above the category median. Disclosed range: $228K to $331K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Cabot Corporation AI Hiring
Cabot Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $331K - $331K.
Location Context
AI roles in Boston pay a median of $210,000 across 97 tracked positions. That's 3% below 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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