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About This Role
Staff Platform Engineer\- Materia AI
Are you excited about building cloud\-native platforms that power Agentic workloads? Join a dynamic and highly skilled team at Thomson Reuters, where we combine the agility and innovation of a startup with the stability and resources of a global leader.
As a Platform Engineer, you will contribute to leading the design, development, and evolution of our next\-generation cloud\-native platforms. You will contribute to technical strategy, champion engineering excellence, and build highly scalable, reliable, and secure infrastructure that empowers our product development teams.
You will be a hands\-on technical leader, influencing architectural decisions, mentoring team members, and championing best practices in a dynamic and fast\-paced environment.
About the Role:
*In this opportunity as a Staff Platform Engineer\- Materia AI you will:*
- Innovate with Startup Speed and Enterprise Scale: Be part of a team that maintains the innovation velocity of a startup while leveraging the operational maturity of a global enterprise. You'll work with top AI and software development experts, building intelligent and scalable systems for accounting professionals. This role is for the CoCounsel for Tax and Accounting team, evolved from the acquisition of Materia, now integrating best\-in\-class enterprise practices with rapid innovation cycles.
- Deliver AI\-Driven Systems: Launch backend services into production that power generative AI agents and orchestration systems. This role requires both creativity and rigor to ensure our AI\-driven solutions are secure, performant, reliable, and adaptable to real\-world use cases.
- Collaborate Across Functions: Work closely with AI/ML engineers, frontend developers, product managers, and SREs to build robust services and developer experiences. Help to build out AI native products for tax, accounting, and audit professionals.
- Developer Enablement : Build and maintain "golden path" reference implementations for automated build, test, and deploy workflows that development teams can adopt and customize.
- Design, implement, and manage reference CI/CD pipelines using tools such as GitHub Actions, Playwright, and Snyk.
- Develop Go command\-line tools and services that streamline and empower developer workflows, from local development to production deployment.
- Establish, measure, and continuously improve DORA metrics, for example deployment frequency, change failure rate to optimize the velocity and quality of product delivery.
- Production Engineering : Lead the design and implementation of scalable, resilient, secure, and cost\-effective production infrastructure using Kubernetes, Vercel, AWS, GCP, and AI platforms.
- Establish and enforce platform security best practices using multi\-cloud strategies and open\-source CNCF solutions, integrating with cloud\-native security tools and Thomson Reuters internal infrastructure.
- Design and implement comprehensive monitoring, logging, and alerting solutions using tools such as DataDog and LLM observability tools.
- Collaboration and Leadership : Set up the necessary processes and documentation to support a team of 150\+ product managers, researchers, and engineers. Align with product, development, and operations teams on business objectives.
- Foster a culture of experimentation and continuous improvement, exploring new AI technologies and methodologies.
Stay abreast of industry trends, emerging technologies, and best practices in cloud computing, platform engineering, and site reliability engineering, advocating for their adoption where appropriate.
- About You:
*You’re a fit for the role of Staff Platform Engineer\- Materia AI if your background includes:*
- Experience: 8\+ years of experience in backend development, platform engineering, production engineering, or site reliability engineering (SRE).
- Software Development: Experience developing, deploying, and maintaining a backend service and associated client interface for the benefit of other product and engineering staff developing products for customers.
- Problem Solving: Deep experience troubleshooting low level DNS, TCP, IP, and TLS protocols. Creative problem solver who enjoys exploring new technologies.
- Cloud\-Native Expertise: Extensive hands\-on experience with cloud\-native services, including deep proficiency with container registries, orchestration (Kubernetes/ECS), object storage (buckets), managed PostgreSQL, serverless functions, networking (VPCs/service mesh), identity and access management, infrastructure\-as\-code, secrets management, and pub/sub messaging systems.
- Communication : Excellent communication skills, capable of articulating complex technical concepts clearly and concisely to both technical and non\-technical stakeholders.
- Curiosity : Eager to explore and understand emerging agentic software paradigms.
- Quality : Dedicated to building high\-quality, reliable platform systems.
- Adaptability : Comfortable working in a fast\-paced, evolving environment.
- Education : Bachelor’s degree in computer science, computer engineering, related field, or equivalent experience
Preferred Skills \& Experience
- Fluent in Go, Python, Rust, or another systems level language.
- Experience with configuration management tools such as Terraform and CUE.
- Experience with AI\-driven systems, agent\-based architectures, LLM evaluation tools, and AI APIs from providers like OpenAI and Anthropic.
- Experience owning SOC 2 compliant systems end to end.
- Knowledge of vector databases, embeddings, or search\-based AI.
- Strong opinions on infrastructure design, end to end testing strategies, automation, middleware, and RPC/REST API designs patterns.
\#LI\-SM2
This posting is for proactive recruitment purposes and may be used to fill current openings or future vacancies within our organization.
What’s in it For You?
- Hybrid Work Model: We’ve adopted a flexible hybrid working environment (2\-3 days a week in the office depending on the role) for our office\-based roles while delivering a seamless experience that is digitally and physically connected.
- Flexibility \& Work\-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering employees to achieve a better work\-life balance.
- Career Development and Growth: By fostering a culture of continuous learning and skill development, we prepare our talent to tackle tomorrow’s challenges and deliver real\-world solutions. Our Grow My Way programming and skills\-first approach ensures you have the tools and knowledge to grow, lead, and thrive in an AI\-enabled future.
- Industry Competitive Benefits: We offer comprehensive benefit plans to include flexible vacation, two company\-wide Mental Health Days off, access to the Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and resources for mental, physical, and financial wellbeing.
- Culture: Globally recognized, award\-winning reputation for inclusion and belonging, flexibility, work\-life balance, and more. We live by our values: Obsess over our Customers, Compete to Win, Challenge (Y)our Thinking, Act Fast / Learn Fast, and Stronger Together.
- Social Impact: Make an impact in your community with our Social Impact Institute. We offer employees two paid volunteer days off annually and opportunities to get involved with pro\-bono consulting projects and Environmental, Social, and Governance (ESG) initiatives.
- Making a Real\-World Impact: We are one of the few companies globally that helps its customers pursue justice, truth, and transparency. Together, with the professionals and institutions we serve, we help uphold the rule of law, turn the wheels of commerce, catch bad actors, report the facts, and provide trusted, unbiased information to people all over the world.
Our use of AI within the recruitment process Thomson Reuters utilizes Artificial Intelligence (AI) to support parts of our global recruitment process. Unless you opt\-out, our AI system will assess the information provided by you and compare it to the requirements listed for the role, and present the result to our recruitment personnel for further review. The AI system acts as a supporting tool, but there is always a human making the decision if you will be considered for the role.
In the United States, Thomson Reuters offers a comprehensive benefits package to our employees. Our benefit package includes market competitive health, dental, vision, disability, and life insurance programs, as well as a competitive 401k plan with company match. In addition, Thomson Reuters offers market leading work life benefits with competitive vacation, sick and safe paid time off, paid holidays (including two company mental health days off), parental leave, sabbatical leave. These benefits meet or exceeds the requirements of paid time off in accordance with any applicable state or municipal laws. Finally, Thomson Reuters offers the following additional benefits: optional hospital, accident and sickness insurance paid 100% by the employee; optional life and AD\&D insurance paid 100% by the employee; Flexible Spending and Health Savings Accounts; fitness reimbursement; access to Employee Assistance Program; Group Legal Identity Theft Protection benefit paid 100% by employee; access to 529 Plan; commuter benefits; Adoption \& Surrogacy Assistance; Tuition Reimbursement; and access to Employee Stock Purchase Plan.
Thomson Reuters complies with local laws that require upfront disclosure of the expected pay range for a position. The base compensation range varies across locations.\&\#xa;\&\#xa;For any eligible US locations, unless otherwise noted, the base compensation range for this role is $102,200 USD \- $189,800 USD.\&\#xa;For Ontario, Canada, the base compensation range for this role is $118,200 CAD \- $168,200 CAD.\&\#xa;\&\#xa;Base pay is positioned within the range based on several factors including an individual’s knowledge, skills and experience with consideration given to internal equity. Base pay is one part of a comprehensive Total Reward program which also includes flexible and supportive benefits and other wellbeing programs.\&\#xa;This role may also be eligible for an Annual Bonus based on a combination of enterprise and individual performance.\&\#xa;
About Us
Thomson Reuters informs the way forward by bringing together the trusted content and technology that people and organizations need to make the right decisions. We serve professionals across legal, tax, accounting, compliance, government, and media. Our products combine highly specialized software and insights to empower professionals with the data, intelligence, and solutions needed to make informed decisions, and to help institutions in their pursuit of justice, truth, and transparency. Reuters, part of Thomson Reuters, is a world leading provider of trusted journalism and news.
We are powered by the talents of 26,000 employees across more than 70 countries, where everyone has a chance to contribute and grow professionally in flexible work environments. At a time when objectivity, accuracy, fairness, and transparency are under attack, we consider it our duty to pursue them. Sound exciting? Join us and help shape the industries that move society forward.
As a global business, we rely on the unique backgrounds, perspectives, and experiences of all employees to deliver on our business goals. To ensure we can do that, we seek talented, qualified employees in all our operations around the world regardless of race, color, sex/gender, including pregnancy, gender identity and expression, national origin, religion, sexual orientation, disability, age, marital status, citizen status, veteran status, or any other protected classification under applicable law. Thomson Reuters is proud to be an Equal Employment Opportunity Employer providing a drug\-free workplace.
Thomson Reuters makes reasonable accommodations for applicants with disabilities, including veterans with disabilities, and for sincerely held religious beliefs in accordance with applicable law. If you reside in the United States and require an accommodation in the recruiting process, you may contact our Human Resources Department at HR.Leave\[email protected] . Disability accommodations in the recruiting process may include things like a sign language interpreter, making interview rooms accessible, providing assistive technology, or other relevant accommodations. Please note this email is not intended for general recruitment questions and we will promptly respond to inquiries regarding accommodations. More information on requesting an accommodation here.
Learn more on how to protect yourself from fraudulent job postings here.
More information about Thomson Reuters can be found on thomsonreuters.com
Salary Context
This $102K-$189K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 951 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 1,809 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Thomson Reuters, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($146K) sits 21% below the category median. Disclosed range: $102K to $189K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Thomson Reuters AI Hiring
Thomson Reuters has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Safety. Positions span Frisco, TX, US, New York, NY, US. Compensation range: $152K - $424K.
Location Context
Across all AI roles, 16% (294 positions) offer remote work, while 1,505 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 1,809 open positions tracked in our dataset. By seniority: 34 entry-level, 797 mid-level, 728 senior, and 250 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (294 positions). The remaining 1,505 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 1,809 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,274), Data Scientist (145), AI Software Engineer (132). 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 (34) are outnumbered by mid-level (797) and senior (728) 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 250 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (294 positions), with 1,505 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (877 postings), Aws (592 postings), Azure (458 postings), Rag (380 postings), Gcp (364 postings), Pytorch (277 postings), Prompt Engineering (266 postings), Claude (250 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.
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