Design, build, and optimize end-to-end infrastructure for AI-driven gameplay, simulation, analytics, and security. Focus on C++ systems, machine learning infrastructure, and large-scale data pipelines. Enable fast iteration, scalable training, and reliable deployment.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
About A5 Labs
A5 Labs is a US-based, fully remote AI and gaming technology company.
We build large-scale systems behind competitive online games, focusing on AI-driven gameplay, simulation, analytics, and security (anti-cheat).
Our work sits at the intersection of C++ systems, machine learning infrastructure, and large-scale data pipelines, enabling researchers and engineers to iterate quickly and deploy models reliably in production.
Role Summary
We are looking for a senior ML systems engineer to design, build, and optimize the end-to-end infrastructure that connects:
- C++ game environments and inference serving
- Python-based training, evaluation, and analytics pipelines
This role is systems- and performance-oriented, not an algorithm research position.
Your impact will be enabling fast iteration, scalable training, and reliable deployment across the entire ML/RL stack.
What You’ll Do
- Design and maintain end-to-end ML/RL systems spanning:
- C++ game environments and real-time inference serving
- Python-based training, evaluation, and data pipelines
- Build and optimize high-throughput data pipelines that serve:
- RL training
- evaluation and visualization
- gameplay analytics
- anti-cheat models and research workflows
- Profile and optimize system performance (latency, throughput, memory, GPU utilization) across C++ and Python components.
- Design infrastructure that enables fast iteration from small-scale experiments to large-scale training and production.
- Improve observability by building tools for:
- logging and metrics
- replaying problem cases
- debugging model and system behavior
- Establish engineering standards for ML systems:
- testing
- CI/CD
- coding and operational guidelines
Ideal Experience
- Strong experience with C++ systems, including implementation, profiling, and optimization.
- Experience building ML training and evaluation pipelines in Python.
- Experience designing systems that bridge data generation (env / serving) and model training.
- Solid understanding of ML/RL workflows (training, evaluation, inference); algorithm research is not the focus.
- Experience with production ML systems, CI/CD, and test automation.
- Bonus:
- experience with simulation, game engines, or real-time inference systems
- experience supporting researchers with scalable infrastructure
Typical Problems You’ll Work On
- How do we design a system where a C++ game environment efficiently feeds data to training, evaluation, analytics, and anti-cheat pipelines?
- How do we profile and optimize the end-to-end loop from environment → data → training → inference?
- How do we enable researchers to explore new ideas without heavy infrastructure overhead?
- How do we build observability tools that expose both model behavior and system bottlenecks?
- How do we ensure fast, reliable, and scalable C++ inference serving in production?
Tech Stack
- C++ (game environments, inference serving)
- Python (training, evaluation, data pipelines)
- TypeScript / React (internal tools and visualization)
Location & Compensation
- Fully remote
- Competitive salary + performance-based bonuses