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About This Role
Senior Software Engineer – (Go/Rust/C++)
Austin, TX · Hybrid (3 days onsite/week) · Full-time
Context & Impact:
Lansweeper is the AI Cyber Asset Intelligence platform helping IT and Security teams gain full visibility, reduce cyber risk, and scale automation with confidence. We transform raw asset data into a continuously validated, trusted source of truth — so teams can move faster and act with certainty.
The Recognition & DataCore team owns the core intelligence layer of our platform: the systems that discover, identify, classify, and enrich millions of assets across IT, OT, cloud, and IoT environments. This is high-stakes, high-scale backend engineering — the kind of work where precision, performance, and reliability directly affect the security posture of our enterprise customers worldwide.
We are looking for a Senior Software Engineer who writes production-quality Go or Rust, thinks deeply about distributed systems, and treats AI‑assisted development as a daily practice — not an experiment.
AI Fluency
At Lansweeper, we are building an AI-native engineering culture. We expect all engineers to actively use AI tools to enhance productivity and quality across the software development lifecycle, including coding, testing, documentation, and system design. Candidates should be comfortable working with AI-assisted workflows and continuously evolving their approach as tools and practices advance.
Challenge
The main challenges you'll face are:
- Enhancing and maintaining high-scale distributed backend systems handling millions of assets
- Solving complex data processing, enrichment, and classification problems at scale
- Maintaining performance, reliability, and engineering rigor in an AI‑assisted development environment
Key Responsibilities
- Design and build scalable backend services and data pipelines for asset discovery, recognition, and enrichment
- Work on distributed systems problems: concurrency, fault tolerance, and high-throughput processing
- Integrate AI and ML capabilities into product workflows
- Contribute to architecture decisions and technical standards
- Use AI-assisted development tools as part of your daily workflow
- Mentor engineers and raise the technical bar
- Participate in code reviews, design discussions, and continuous improvement
Key Requirements
Hard Skills
- Primary language: Go or Rust
- Proven experience building distributed systems at scale
- Strong experience with AI-assisted development tools (Claude Code, Cursor, Copilot, etc.)
- Hands-on experience with AWS, Kubernetes, and Terraform
- Strong understanding of backend systems and data-intensive platforms
- Active engineering practice (GitHub, open-source, or personal projects — we will look)
Soft Skills
- Strong systems thinking and problem-solving ability
- High ownership and accountability
- Ability to work effectively in AI-augmented environments
AI Specific Skills
Lansweeper is an AI-native company. In this role, you are expected to actively use AI-assisted development tools such as Claude Code, agents, subagents, and MCP-based workflows as a core part of your engineering workflow. You should be comfortable prompting, validating, and refining AI-generated code, and using AI to improve development speed while maintaining production-quality standards.
Team Info
You will join an enthusiastic team of 40 engineers based in Spain (global engineering team is 120 people)
Our Offer
Salary: Competitive compensation aligned with industry benchmarks
- Benefits: ｿ • Time off — 20 vacation days + 3 sick days + 6 personal days + 12 public holidays.• Health & protection — Medical, dental, vision, life, and disability insurance, plus EAP mental health support.• Pension — 401(k) with 4% employer match.• Work setup — Hybrid working with full telework equipment and a monthly internet allowance.• Opportunity to lead within an AI-forward sales organization.• Career growth and learning opportunities within a fast-scaling SaaS company.• Hybrid working model: in our Austin, Texas office.• Engaging company culture with team events and international collaboration.
- Career growth and learning opportunities within a fast-scaling SaaS company
- Flexibility in working hours and hybrid work options in the Austin, TX office.
- Company events and international collaboration
We welcome applicants of all backgrounds, regardless of gender, religion, ethnic origin, age, sexual orientation, or disability.
About Lansweeper
Lansweeper is the AI Cyber Asset Intelligence platform helping IT and Security teams gain full visibility, reduce cyber risk, and scale automation with confidence.
In today's complex IT, OT, cloud, and IoT environments, fragmented asset data slows decisions and increases risk. We transform raw asset data into a continuously validated, trusted source of truth — so teams can move faster and act with certainty.
With Lansweeper, organizations can:
See – Truly complete visibility across hybrid environments
Know – Enriched asset intelligence with lifecycle and risk context
Act – Automate workflows, coordinate remediation, and enforce policy at scale
From universal asset discovery to AI-powered intelligence, we provide the shared foundation modern IT Operations, Cybersecurity, and Digital Transformation teams rely on.
Our Culture
We're built on four core values:
One Team – United across boundaries
We Care – Customers and people at the center
We Grow – Learning, sharing, improving
We Deliver – Focusing on what truly matters
How AI is used during Hiring Process
We use AI to support and not replace during the hiring process. HR decisions are made by our talent acquisition team. You can request more info at any time via [email protected]
Diversity Statement
It is Lansweeper's policy to provide equal employment opportunity to all applicants and employees.
Lansweeper disapproves of, and will not tolerate, unlawful discrimination against any applicant or employee because of race, color, national origin or ancestry, gender (including pregnancy, childbirth, or related medical conditions), gender identity, age, religion, disability, family care status, veteran status, marital status, sexual orientation, or any other basis protected by local, state, or federal laws.
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Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 11,520 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Lansweeper, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,688 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Lansweeper AI Hiring
Lansweeper has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $208,000 across 317 tracked positions. That's 3% below the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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