Interested in this AI/ML Engineer role at Chemours?
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About This Role
As we create a colorful, capable and cleaner world through chemistry, we invite you to join our team to harness the power of chemistry to shape markets, redefine industries and improve lives for billions of people around the world.
CREATING ESSENTIAL CHEMISTRY THAT THE WORLD NEEDS
At Chemours, our people are redefining how the world thinks of chemistry by approaching everything we do with a commitment to delivering Trusted Chemistry that creates better lives and helps communities thrive. That begins with how we use our science, data, and unmatched technical expertise to develop market\-leading products with the highest levels of performance, sustainability, and safety in the industry.
Powered by chemistry, our products are used in applications that make the products we rely on, processes, and new technologies possible. In key sectors such as clean energy, advanced electronics, high\-performance computing and AI, climate friendly cooling, and high\-quality paints and coatings for homes and industrial infrastructure\-sustainable solutions and more modern living depend on Chemours chemistry.
Chemours is seeking a Senior AI Engineer to join our growing AI \& Data Science team. This is a remote role and will report directly to the Head of Data Science \& AI.
The role will identify, solution, build, and launch GenAI products \& traditional apps in all areas of the company, including Manufacturing, Legal, Finance, R\&D, Supply Chain, and Sales .
You’ll join a highly collaborative team that values speed and quality of execution, putting a high volume of high\-impact ML models/GenAI agents into production. As a Senior AI Engineer, your experience as a mentor and your ability to developing clean, scalable code efficiently will be valuable as our team grows.
Successful candidates will be able to thrive in a fast paced , ambiguous environment with a continuous learning mindset. Role will also require heavy stakeholder/end\-user engagement throughout the scoping \& build process .
Must be able to work US eastern time zone working hours, typically 7:30am – 5pm.
The responsibilities of the position include, but are not limited to, the following:
- Identify, design, and integrate Generative AI solutions into existing business workflows to deliver measurable value
- Design, architect, implement, and productionize traditional software applications and GenAI agents.
- Partner with users and stakeholders to scope the use case, gather requirements, establish success criteria, and deliver an intuitive, high\-quality UX.
- Lead high\-impact initiatives across multiple business functions, including Manufacturing, Legal, Finance, R\&D, Supply Chain, Procurement, and Sales.
- Mentor \& help accelerate the work of junior developers; foster collaboration and teamwork and pair\-programming culture.
- Collaborate with related teams (ie. Cybersecurity, Infrastructure, Data Engineering, Office 365\) to go thru approvals, architecture, security \& governance reviews.
- When assigned to an opportunity, you will lead end\-to\-end delivery.
- You will serve as product owner to define and maintain a phased roadmap, incorporating continuous user feedback, backlog prioritization, and release planning into your recommendations.
- Lead software development from design through deployment.
- Communicate progress to non\-technical but key stakeholders on a proactive \& regular basis.
- For existing codebases, address technical debt and build scalable, reusable architectures that are well documented and conform to security and compliance standards.
- Manage the full lifecycle of deployed applications, including debugging, enhancements, upgrades, and continuous improvement driven by user feedback and evolving technology.
The following is *required* for this role:
- D egree in Computer Science or Computer Engineering
- 8\+ years advanced backend development with Python AND Javascript
- 2\+ years designing, coding (in Python), and deploying custom and complex GenAI solutions and multi\-agent GenAI systems in production. We need to see evidence beyond building simple recommender systems or chatbots.
- Minimum 2 years UI \& frontend development with React.js, Typescript, and HTML
- Strong hands\-on experience designing and building GenAI architectures and multi\-agent systems, including embeddings, advanced Retrieval\-Augmented Generation (RAG) techniques, and a variety of search architectures. Candidates should be able to clearly articulate solutions they have built using hybrid search, agentic search, vector search, and full\-text search architectures.
- Demonstrated experience developing, deploying, and evaluating GenAI solutions, with a strong understanding of evaluation frameworks (evals) and a proven ability to optimize the cost\-to\-performance ratio of production AI applications.
- Strong proficiency with Pydantic models, including knowing when and how to leverage them to design robust, scalable, and maintainable GenAI architectures and workflows.
- Excellent English communication skills, in particular with non\-technical users; proven ability to translate user needs into technical specs and regularly communicate progress to a wide range of technical \& non\-technical stakeholders.
The following is *preferred* for this role:
- Experience with PyTorch and iterating on model performance using task\-specific data and empirical evaluation.
- Strong experience building and operating CI/CD pipelines for AI and software systems in Azure\-based environments, including automated testing, deployment, and secure, reproducible releases.
- Demonstrated enthusiasm for keeping current with advances in AI and machine learning, with the ability to translate new research, models, and techniques into practical, production\-ready solutions (we’d love to see your Github portfolio!)
- Experience integrating machine learning models into custom applications
- Azure Kubernetes experience
- Experience designing, creating, and applying knowledge graphs to strengthen custom GenAI solutions.
Benefits:
Competitive Compensation
Comprehensive Benefits Packages
401(k) Match
Employee Stock Purchase Program
Tuition Reimbursement
Commuter Benefits
Learning and Development Opportunities
Strong Inclusion and Diversity Initiatives
Company\-paid Volunteer Day
We’re a different kind of chemistry company because we see our people as our biggest assets. Instead of focusing just on what our employees do each day, we look at how they do it—by taking a different approach to talent development, employee engagement, and culture. Our goal is to empower employees to be their best selves, at Chemours and in life.
Learn more about Chemours and our culture by visiting Chemours.com/careers.
*Chemours is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, race, religion, color, gender, disability, national or ethnic origin, ancestry, marital status, family status, sexual orientation, gender identity or expression, or veteran status. Jurisdictions may have additional grounds for non\-discrimination, and we comply with all applicable laws.*
*Chemours is an E\-Verify employer*
*Candidates must be able to perform all duties listed with or without accommodation*
*Immigration sponsorship (i.e., H1\-B visa, F\-1 visa (OPT), TN visa or any other non\-immigrant status) is not currently available for this position*
*Don’t meet every single requirement? At Chemours we are dedicated to building a diverse, inclusive, and authentic workplace for our employees. So if you’re excited about this role, but your past experience doesn’t align perfectly with every qualification in the position description, we encourage you to apply anyways. You may just be the right candidate for this or other opportunities.*
*In our pursuit to be the greatest place to work, we know that a critical element to enhancing our employee experience is to assure we’re operating with a solid foundation of trust. At Chemours, this means being transparent about how we pay our employees for the work that they do.*
Pay Range (in local currency):
$126,067\.00 \- $196,980\.00
Chemours Level:
27
Annual Bonus Target:
14%
*The pay range and incentives listed above is a general guideline based on the primary location of this job only and not a guarantee of total compensation.* *Factors considered* *in extending a compensation offer include (but are* *not limited to)* *responsibilities of the* *job, experience,* *knowledge, skills, and abilities, as well as internal equity, and alignment with market data. The incentive pay is dependent on business results and individual performance and subject to the terms and conditions of the specific plans.*
At Chemours, you will find sustainability in our vision, our business and your future. If you want to work on the leading edge of your field and have a desire to make a difference, join Chemours and discover what it means when we say "We Are Living Chemistry."
Salary Context
This $126K-$196K range is below the median 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 Chemours, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($161K) sits 26% below the category median. Disclosed range: $126K to $196K.
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.
Chemours AI Hiring
Chemours has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $196K - $196K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 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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