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About This Role
About This Opportunity
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This is a general application for Full\-Stack Applied AI Engineers across Infinity Constellation’s platform and portfolio of companies and the companies we build and support. Rather than applying to a specific role, you’ll be joining our talent network for current and future opportunities across the businesses we build and support.
Because our hiring needs evolve quickly, applying does not necessarily mean there is an immediate opening that matches your background today. When a relevant opportunity arises, our team may reach out to discuss the specific company, project, scope, and timing.
About Infinity
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Infinity Constellation is a holding company building full\-stack AI businesses. Our engineering teams work across a range of zero\-to\-one and growth\-stage challenges, from rapidly prototyping new product ideas and building AI\-native platforms to embedding directly with companies and clients to solve complex, real\-world problems.
We also build and evolve Gravity, our in\-house software factory and agent hub used to accelerate how software is created across the Infinity ecosystem.
We’re not a dev shop. We’re a focused network of builders who move quickly, reduce ambiguity, and create leverage across companies with AI at their core.
About The Role
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We’re looking for sharp, resourceful, execution\-focused engineers who thrive in high\-autonomy environments and enjoy building at the intersection of full\-stack software engineering, applied AI, product, and systems design.
Depending on the opportunity, you might spend one week validating a new product idea through a rapid prototype and the next building production\-grade AI workflows, internal platforms, or client\-facing applications. Some engineers work directly on shared Infinity infrastructure, while others embed with portfolio companies or clients to solve high\-impact technical problems.
The common thread: you’ll be expected to turn ambiguity into working software, make pragmatic technical decisions, and own outcomes—not just code.
What You’ll Do
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- Build and ship full\-stack, AI\-native products, internal platforms, prototypes, and production applications.
- Design and implement agentic AI applications, LLM workflows, RAG systems, document\-processing pipelines, and other applied AI solutions.
- Translate ambiguous business, product, or client needs into scalable technical systems.
- Build modern frontend experiences using frameworks such as React and Next.js.
- Develop backend systems and APIs using Python, Node.js, FastAPI, Django, or similar technologies.
- Design data models and work across relational, graph, vector, and hybrid database architectures.
- Integrate commercial APIs, open\-source models, automation platforms, and third\-party tools to create leverage and accelerate execution.
- Build asynchronous workflows, internal dashboards, admin tools, approval systems, and automation infrastructure.
- Experiment quickly across frameworks and technologies while knowing when to optimize for speed versus long\-term scalability.
- Contribute to shared internal libraries, Infinity Commons, and Gravity, our software factory platform.
- Collaborate directly with founders, product leaders, senior engineers, and, in some cases, clients to define problems and ship solutions.
- Occasionally embed with an Infinity company or client team to support launches, unblock execution, or build zero\-to\-one products.
What We’re Looking For
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We’re building a broad pipeline and recognize that the right level of experience may vary by opportunity. In general, we’re interested in engineers who bring:
- Experience and comfort working directly with stakeholders and customers to translate business problems into working solutions
- Strong software engineering fundamentals and experience shipping real products used by real users.
- Fluency in Python and experience building AI\-powered applications, workflows, or systems.
- Full\-stack development experience with modern web technologies such as React, Next.js, Node.js, FastAPI, Django, or equivalent frameworks.
- Experience rapidly building and iterating on prototypes, proof\-of\-concepts, MVPs, or zero\-to\-one products.
- Practical experience working with LLMs, AI APIs and agent frameworks including demonstrating your ability to measure the quality and performance of these systems.
- Comfort integrating APIs, cloud services, databases, open\-source tools, and automation platforms.
- Strong systems thinking and an ability to design solutions that can evolve without unnecessary complexity.
- The ability to translate non\-technical, product, or client needs into pragmatic technical solutions.
- Comfort working in ambiguity, making tradeoffs, and taking ownership without waiting for perfectly defined specifications.
- Strong communication skills and the ability to collaborate with both technical and non\-technical stakeholders.
Technical Experience We Value
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Depending on the specific opportunity, relevant experience may include:
- Frontend: React, Next.js, or equivalent modern frameworks.
- Backend: Python, FastAPI, Django, or similar technologies.
- AI \& LLMs: Agentic workflows, multi\-agent systems, prompt engineering, structured outputs, RAG, GraphRAG, embeddings, vector databases, and LLM orchestration.
- Data \& Databases: PostgreSQL, Neo4j, BigQuery, Supabase, Pinecone, Weaviate, relational databases, graph databases, and hybrid schemas.
- Cloud \& Infrastructure: AWS, GCP or other major cloud platforms, containerized services, task queues, and distributed processing.
- Integrations \& Automation: REST APIs, webhooks, n8n, Temporal, and other workflow or automation tools.
- Production Systems: Authentication, permissions, role\-based access, asynchronous workflows, observability, and handling real\-world edge cases.
You do not need experience with every technology listed above. We care more about your ability to learn quickly, make sound technical decisions, and use the right tools to solve the problem at hand.
Bonus Points
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- Experience building AI agents and a deep understanding of what it takes to make them reliable in production.
- Experience with RAG, GraphRAG, vector databases, knowledge retrieval, or document\-processing pipelines.
- Experience building internal platforms that evolved into external\-facing products.
- Previous experience as a founder, founding engineer, or early employee at a venture\-backed startup.
- Experience working directly with clients in a forward\-deployed engineering environment.
- Experience in regulated or technically complex industries.
- Experience building products for agencies, creative teams, knowledge workers, or operational teams.
- Contributions to open\-source AI or ML projects.
- A background in a STEM discipline, data science, or quantitative engineering.
How We Work
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- High ownership: We expect engineers to own outcomes, not just assigned tickets.
- Bias toward shipping: We scope ruthlessly, ship quickly, learn from real users, and iterate.
- Pragmatic engineering: We care about leverage, simplicity, and compounding value over unnecessary complexity.
- Comfort with ambiguity: Requirements evolve. We expect you to help shape the solution, not wait for perfect specifications.
- Asynchronous by default: We value clear communication, tight feedback loops, and autonomy.
- AI\-native: We actively experiment with how AI changes what and how we build, while knowing when AI is—and isn’t—the right solution.
- Small teams, meaningful impact: You’ll work alongside experienced builders across AI, product, infrastructure, and company creation.
This Environment May Be a Fit for You If
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- You enjoy turning ambiguous ideas into working products.
- You think in systems, not just individual features.
- You want autonomy and responsibility more than rigid structure.
- You enjoy zero\-to\-one building and fast iteration.
- You’re comfortable making pragmatic tradeoffs without paralysis.
- You want your work to have visible, direct impact.
This environment may be less suited to you if you prefer highly defined specifications, long planning cycles, narrow ownership, or primarily maintaining mature systems.
What’s In It For You
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Infinity Constellation is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity.
During the interview process, your Talent Acquisition Partner will confirm compensation tier applicable to your location. For candidates outside the U.S., compensation is aligned with local market conditions and cost of living.
Total compensation is determined by factors such as location, relevant experience, skills, internal pay equity, and market conditions. While every offer is unique, our compensation philosophy is designed to ensure fairness, consistency, and competitiveness across the Organization. Additional details on total compensation and benefits will be discussed during the hiring process.
*Equal Opportunity Statement:*
*We’re an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or veteran status, or any other basis protected by law.*
*E\-Verify Notice:*
*Infinity Constellation participates in the federal E\-Verify program. This program electronically verifies the identity and employment eligibility of all individuals hired to work in the United States. To learn more about your rights and responsibilities, please review the official* *E\-Verify Participation Poster* *and the* *Right to Work* *Poster in both English and Spanish.*
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 Infinity, 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. 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.
Infinity AI Hiring
Infinity has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in 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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