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About This Role
TeamViewer provides a leading Digital Workplace platform that connects people with technology—enabling, improving and automating digital processes to make work work better. Our software solutions harness the power of AI and shape the future of digitalization.
We believe that our diverse teams and strong company culture are key to the success of our products and technologies, that hundreds of millions of users around the world and around 645,000 customers across all industries rely on. With more than 1,900 employees worldwide, we celebrate the unique perspectives and talents that each individual brings to the table and foster a dynamic work environment where new ideas thrive. Are you ready to join our team and make an impact?
At TeamViewer, we build technology that helps organizations connect people, devices, and digital workflows securely and efficiently. Our products support teams that need to manage complex IT environments, resolve disruptions faster, and automate work with confidence.
The Agentic Ecosystem team is building the foundation for AI\-native enterprise software. We create platform capabilities that allow AI agents to understand context, discover tools, invoke capabilities, and execute governed actions across TeamViewer products and connected third\-party systems.
The work sits at the core of agentic experiences: MCP servers and clients, tool catalogs, context assembly, orchestration, evaluation, observability, secure integration patterns, and production\-grade guardrails. The goal is to make agent behavior reliable, measurable, auditable, cost\-aware, and safe for enterprise use.
You will help define how autonomous agents become trusted components of real\-world software, not experiments or demos. This means combining strong engineering standards, AI\-native development practices, human accountability, and customer trust. Join us if you want to build the ecosystem that will power the next generation of intelligent digital work.
##### Responsibilities
- Build the services behind TeamViewer's agentic ecosystem, including MCP servers and clients, tool integrations, context assembly, orchestration, and connections to external agent frameworks.
- Design and operate enterprise\-grade AI systems with authentication, authorization, tenancy isolation, auditability, rate limiting, and cost controls.
- Create agent\-friendly tool interfaces, schemas, and workflows, and validate their effectiveness through systematic evaluation.
- Build and maintain evaluation frameworks that assess prompts, tools, models, and retrieval strategies, using quality gates to support release decisions.
- Engineer and optimize production AI workflows with telemetry, monitoring, cost management, latency tracking, and quality drift detection.
- Implement safeguards against prompt injection, tool misuse, and other AI\-specific security risks in collaboration with security stakeholders.
- Work across the full stack, building Python services and partnering on frontend experiences that bring AI capabilities to end users.
- Use AI coding agents as a core development mechanism and continuously improve the context, tooling, and engineering practices that enable them to perform effectively.
##### Requirements
- 8\+ years of experience building production software with Python, combined with sufficient TypeScript expertise to contribute across the stack.
- Hands\-on experience delivering model\-based or agentic systems, including tool calling, structured outputs, retrieval, context management, and orchestration.
- Strong understanding of MCP or comparable agent\-tool protocols, including the challenges of authorization, discovery, versioning, and multi\-tenant environments.
- Proven ability to define quality criteria, build evaluation frameworks, interpret results, and continuously improve AI systems based on evaluation outcomes.
- Solid working knowledge of OAuth, service identities, event\-driven architectures, and enterprise SaaS platforms.
- Regular use of AI coding agents, paired with a critical review mindset and accountability for software correctness, security, and maintainability.
- Deep understanding of AI\-specific security risks, including prompt injection, tool abuse, excessive permissions, and data leakage through model context.
- Familiarity with common AI system failure modes such as hallucinations, context degradation, non\-determinism, and silent regressions, along with effective mitigation strategies.
- Excellent English communication skills and the ability to collaborate effectively across globally distributed engineering teams.
##### What we offer
- Competitive compensation and bonuses
- Flexible PTO and paid holidays
- 401(k) with employer matching
- Comprehensive Health insurance package including 100% employer\-paid medical coverage
- Up to 12 weeks of Parental Leave
- Basic Life Insurance, Short\-Term \& Long\-Term Disability, 100% employer\-paid
- Quarterly teambuilding events, leadership luncheons, and companywide “All Hands” meetings
- Open door policy and business casual dress code
- We celebrate diversity as one of our core values. Join c\-a\-r\-e and lead change initiatives together with us!
Work location for this position is Austin, TX.
TeamViewer is an equal opportunities employer and is committed to building an inclusive culture where everyone feels welcome and supported. We C\-A\-R\-E and understand that our diverse, values\-driven culture makes us stronger. As we continue to grow as a company, we also focus on enabling our employees to grow both personally and professionally. We are proud to have an open and embracing workplace environment that will empower you to be your best no matter your gender, civil or family status, sexual orientation, religion, age, disability, education level, or race.
If you require any accommodations or adjustments during the application or hiring process, including interview or onboarding support, please reach out to your Talent Acquisition Partner, who will be happy to support you.
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 TeamViewer, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
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.
TeamViewer AI Hiring
TeamViewer has 10 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, AI Product Manager. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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