AI Engineer

Senior AI / Machine Learning Engineer

Austin, TX | Hybrid | $180k-$200k base

Many current AI roles offer similar responsibilities:

"Build with LLMs"

"Work on cutting-edge AI"

"Join a fast-growing team"

This one is different.

This role is ideal for engineers who want to work directly with AI models, systems, performance constraints, deployment layers, and real-time decision-making.

The team is looking for candidates with a deep understanding of AI/ML systems in production, not candidates whose experience is limited to integrating third-party LLM APIs.

THE ROLE

You'll join an Austin-based AI company focused on building production-grade AI/ML systems at a deep technical level.

The work sits across:

- LLM architecture

- Custom model work

- Model fine-tuning and retraining

- Production ML

- Deployment and inference systems

- GPU-scale compute

- Low-latency decisioning

- Cloud-based AI infrastructure

- High-performance software engineering

This is a hands-on engineering position. While seniority is valued, the role requires active technical involvement rather than remote management.

The ideal candidate will be comfortable building, debugging, optimizing, and deploying complex AI systems from concept to production.

WHAT YOU'LL BE WORKING ON

You'll contribute to building AI/ML systems designed for real-world deployment, not just demonstrations.

That could include:

- Building and deploying production AI/ML systems

- Working with LLM architecture and model trade-offs

- Fine-tuning, retraining, or adapting custom models

- Optimizing inference and model performance

- Building systems for real-time decisioning

- Working in latency-sensitive environments where milliseconds matter

- Using Python for AI/ML engineering

- Working with C/C or CUDA where performance requires it

- Scaling AI/ML systems in cloud environments, primarily GCP

- Taking prototypes or research ideas into commercial production

WHAT MAKES THIS INTERESTING

This position requires AI experience that extends beyond prompt engineering.

You'll work on systems where performance, architecture, scalability, and deployment are critical.

The ideal person will be able to talk clearly about:

- Models they have worked with

- How those models were deployed

- How inference was handled

- What performance constraints existed

- What trade-offs were made

- What broke in production

- How they fixed it

If you enjoy solving complex technical challenges, this role will be engaging.

CORE REQUIREMENTS

You'll need:

- 5 years of relevant software engineering, AI engineering, or ML engineering experience

- Strong computer science fundamentals

- Commercial experience building or deploying AI/ML systems

- Production ML deployment experience

- Understanding of LLM architecture beyond API usage

- Experience with custom models, model adaptation, fine-tuning, or retraining

- Strong Python engineering experience

- Experience with ML frameworks such as PyTorch or similar

- Cloud deployment experience

- Ability to build reliable systems, not just prototypes

- Comfort working in a startup-style environment

- Ability to work onsite in Austin 4 days per week

HIGHLY VALUABLE EXPERIENCE

These qualifications are not required, but will help your application:

- C / C

- CUDA

- GPU programming

- GPU acceleration

- High-performance computing

- Low-latency systems

- Real-time decisioning

- Production inference systems

- Distributed systems

- High-throughput systems

- Model serving

- MLOps

- GCP

- AWS or Azure

- Experience moving research or data science work into production

- Experience in transaction-heavy, regulated, robotics, autonomous systems, defence, trading, infrastructure, or other performance-sensitive environments

TECH STACK

- Python

- LLMs / Large Language Models

- LLM architecture

- Custom models

- Model fine-tuning, retraining, or adaptation

- Production ML deployment

- Production inference

- C / C

- CUDA

- GPU programming

- Low-latency systems

- Real-time decisioning

- GCP

- AWS / Azure

- Cloud deployment

- AI/ML infrastructure

- MLOps

- Model serving

- Distributed systems

- High-performance systems

- Algorithms and data structures

- Systems architecture

WORKING MODEL

This position is based in Austin and follows a hybrid working model:

- 4 days per week onsite

- 1 day remote deep-work day

The remote day is intended for focused technical work, including algorithms, coding, optimisation, architecture, and complex problem-solving.

COMPENSATION

Base salary is expected to sit around: $180,000-$200,000

Compensation may be flexible for exceptional candidates with deep AI/ML expertise, production deployment experience, low-level engineering skills, and performance optimisation capabilities.

Sponsorship may be available.

WHO THIS WILL SUIT

This role is well-suited for candidates who:

- Want to work on deeper AI/ML engineering problems

- Have built systems that reached production

- Understand the difference between research, prototype, and commercial deployment

- Enjoy performance, infrastructure, and model-level challenges

- Are still hands-on

- Can operate in a startup-style environment

- Like building from zero to one

WHO THIS MAY NOT SUIT

This position may not be suitable if you:

- Have only built basic LLM wrappers

- Mainly use third-party APIs without deeper model or system knowledge

- Have no production ML deployment experience

- Want a purely research-only role

- Want a purely management role

- Need a fully remote setup

- Prefer heavily structured corporate environments