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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?
##### Responsibilities
- Build and operate the data and AI platform behind TeamViewer’s agentic products, including ingestion, transformation, embeddings, indexing, retrieval, warehouses, lakes, and vector stores.
- Own production infrastructure for model workloads, covering deployment, versioning, routing, caching, rate limiting, cost governance, and provider management.
- Build observability for AI systems, including agent traces, tool calls, quality signals, latency, token cost, failure\-mode analysis, and AI\-specific alerting.
- Implement CI/CD for AI systems so prompts, tool definitions, retrieval configuration, evaluation suites, and model versions are tested, deployed, monitored, and rolled back safely.
- Run evaluation infrastructure for AI engineers, including dataset management, harness execution, regression tracking, and release reporting inside the delivery pipeline.
- Define and enforce data quality, governance, security, privacy, access control, encryption, and sensitive\-data handling across structured and unstructured sources.
- Design experimentation platforms that allow the team to test AI and retrieval changes safely against real traffic.
- Collaborate with AI engineers, software engineers, product, security, and platform teams to turn requirements into reliable production systems.
##### Requirements
- 8\+ years of industry experience with strong Python expertise, solid SQL knowledge, sound software engineering fundamentals, and a proven track record of building production\-grade data pipelines and platform services.
- Hands\-on experience operating model\-based applications in production, including retrieval pipelines, embeddings, vector databases, retrieval optimization, deployment, and observability.
- Strong understanding of MLOps and LLMOps practices, including model and prompt versioning, evaluation frameworks, tracing, regression tracking, and AI\-specific quality monitoring.
- Proven ability to optimize AI workloads across providers and architectures by balancing cost, latency, reliability, and quality through effective caching and deployment strategies.
- Experience with CI/CD, automated testing, and major cloud platforms, with Azure or GCP preferred.
- Solid understanding of data governance, security, privacy, and GDPR requirements within AI\-powered systems and workflows.
- Regular use of AI coding agents, combined with a critical review mindset and accountability for the correctness, security, and maintainability of delivered software.
- Practical experience with agentic development environments and extension models, including custom tools, MCP servers, repository\-level instruction files, and sub\-agents.
- Deep understanding of common AI failure modes, including hallucinations, context degradation, prompt injection, non\-determinism, and silent regressions, along with effective mitigation approaches.
- Strong problem\-solving skills, the ability to work independently, experience debugging complex systems, and a pragmatic approach to engineering trade\-offs, coupled with clear communication and fluency in English.
##### 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.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States. Please understand TeamViewer is unable to provide sponsorship for employment or work authorization now or in the future.
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
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 4,317 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At TeamViewer, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $203,000 based on 85 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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 MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
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).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
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.
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