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About This Role
Business Area:
EngineeringSeniority Level:
Mid\-Senior levelJob Description:
At Cloudera, we empower people to transform complex data into clear and actionable insights. With as much data under management as the hyperscalers, we're the preferred data partner for the top companies in almost every industry. Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world’s largest enterprises.
Cloudera is looking for a Staff Software Engineer to join the Enterprise AI Platform team and help drive development of Cloudera’s next\-generation AI and machine learning platform. You will be responsible for helping design, build, and deliver a platform that not only accelerates machine learning \& AI from exploration to production but also enables enterprises to create \& deploy Generative AI applications using foundation models with enterprise data at scale. This role requires an empathetic mindset and close collaboration with front\-end/web UI engineers, data scientists, designers, and product management.
We look for "The Startup Spark", a desire to create new things, dive in wherever there's a need, eagerness to make an impact as an individual, and the willingness to learn new things. You must be self\-motivated, innovative, and proactive. The role offers significant opportunities for growth.
Read about the Forrester Wave Report on Cloudera's Machine Learning offerings here.
This role is not eligible for immigration sponsorship.
As a Software Engineer you will….
- Help build the leading platform for AI and Machine Learning in the enterprise.
- Design, code, and implement elegant, scalable, enterprise\-quality AI inference services powered by machine learning models.
- Design and Develop AI Model Registry and AI applications
- Work to enhance developer velocity and team agility.
- Build strong relationships and collaborate with platform and front\-end engineers, quality engineers, UX designers, as well as Product Management, Field, Professional Services, and other partners.
We are excited if you have….
- Experience building and deploying AI Inference and Generative AI applications.
- 8\+ years of experience building scalable microservices or applications using Go, Python, Node.js, C\# or Java
- Experience with foundation models, prompt engineering, fine\-tuning, semantic search and Retrieval\-Augmented Generation (RAG) using vector databases such as Pinecone, Milvus, etc.
- Experience with microservices design and development (Go, GRPC, SQL) on Kubernetes
- Demonstrate ability to go deep into technology and complex distributed systems
- Experience in crafting high\-level and low\-level design
- Experience in building highly scalable, robust and secure enterprise applications
- Self\-driven and motivated, with a strong sense of ownership and craftsmanship
- Strong written and verbal communication skills.
- Bsc/Msc in related field or equivalent experience
You may also have…
- Experience with HuggingFace, Nvidia AI frameworks and Nim models.
- Experience with AI/ML orchestration software KServe, Knative, Kubeflow.
- Experience with building AI applications with machine learning models using data science and machine learning tools (Python, Tensorflow, Spark, MLflow, R, etc.)
- Experience with at least one of the following Cloud technologies \- Google Cloud Platform (GCP), Amazon Web Services (AWS), Microsoft Azure
- Good to have some full\-stack experience with React, HTML, CSS.
- Experience with data science and machine learning tools (R, Python, Tensorflow, Spark)
- Deep understanding of cloud\-based networking
- Experience using Big Data technologies like Spark, Hive etc.
- Proven track record of collaborating with agile teams across geographically dispersed locations
What you can expect from us:
- Generous PTO Policy
- Support work life balance with Unplugged Days
- Flexible WFH Policy
- Mental \& Physical Wellness programs
- Phone and Internet Reimbursement program
- Access to Continued Career Development
- Comprehensive Benefits and Competitive Packages
- Paid Volunteer Time
- Employee Resource Groups
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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 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Cloudera, 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 $219,250 based on 424 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 $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.
Cloudera AI Hiring
Cloudera 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 $214,343 across 87 tracked positions.
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 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).
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 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.
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