Machine Learning Engineer

Platform Engineer (Reinforcement Learning Systems)

Overview

We are looking for a Platform Engineer to build the infrastructure, tooling, and systems that power large-scale Reinforcement Learning (RL) workflows. This role focuses on enabling researchers to train, evaluate, and deploy RL models efficiently by providing a scalable and reliable experimentation platform.

You will work at the intersection of distributed systems engineering and ML research, building platforms that abstract away infrastructure complexity and enable self-serve experimentation for research teams.

About Deccan AI

Deccan AI is a fast-growing, venture-backed AI infrastructure company focused on training, evaluating, and improving next-generation AI systems. Headquartered in the Bay Area, with a growing India hub in Hyderabad, the company was founded by alumni of IIT Bombay, IIM Ahmedabad, and former Google leaders.

We work with some of the world's leading AI frontier labs and research organizations, including Google DeepMind, Snowflake, and other cutting-edge AI teams. Backed by Prosus Ventures, Deccan AI recently raised $25M in Series A funding and is entering a significant growth phase.

With a global network of over 1 million experts, advanced automation systems, and vertically integrated platforms, we deliver the high-quality data and evaluation infrastructure that state-of-the-art AI models depend on. As the AI infrastructure market rapidly expands, Deccan AI is building the systems powering the future of AI.

What You'll Do

RL Training Infrastructure

• Design and maintain scalable RL training platforms that support large-scale experimentation
• Build infrastructure using modern cloud-native tools and container orchestration systems (e.g., Kubernetes)

Data & Simulation Pipelines

• Develop scalable data pipelines for training environments, including simulation and real-world data collection systems
• Enable efficient data flow across training, evaluation, and inference stages

Performance & System Optimization

• Identify and eliminate bottlenecks in RL training loops, including simulation latency, data loading, and compute inefficiencies
• Optimize distributed training performance for speed, stability, and cost efficiency

Observability & Experimentation

• Build monitoring systems, dashboards, and observability tools for tracking training metrics, rewards, and experiment progress
• Enable researchers to analyze and debug RL experiments effectively

Simulation-to-Real Support

• Support infrastructure and tooling for transferring policies from simulation environments to real-world systems (where applicable)

Required Skills & Experience

Technical Skills

• Strong programming experience in Python (mandatory)
• Experience with systems-level programming languages such as C++ or Go is a plus
• Solid understanding of distributed systems and scalable backend architecture

ML / RL Knowledge

• Strong understanding of Reinforcement Learning fundamentals (MDPs, policies, reward functions, reward shaping)
• Familiarity with deep learning frameworks such as PyTorch
• Exposure to RL training frameworks or large-scale ML systems (e.g., Ray)

Infrastructure Expertise

• Hands-on experience with Kubernetes, Docker, and cloud-native infrastructure
• Experience building and maintaining production-grade distributed systems
• Ability to develop automation tools, CLI utilities, and internal developer platforms

Preferred Qualifications

• Experience building ML or RL platforms in production environments
• Familiarity with simulation environments or robotics-based RL systems
• Experience with large-scale data processing pipelines
• Strong debugging, profiling, and performance optimization skills
• Exposure to MLOps tools and infrastructure automation frameworks

What We're Looking For

• Strong systems thinking and engineering fundamentals
• Ability to work closely with ML researchers and translate requirements into scalable systems
• High ownership mindset and ability to operate in fast-paced environments
• Strong problem-solving skills with attention to performance and reliability
• Passion for building infrastructure that enables research at scale

Why This Role Matters

This role is critical to enabling next-generation RL research. You will be building the foundational platform that allows researchers to run experiments faster, scale training efficiently, and iterate seamlessly — directly accelerating advancements in reinforcement learning systems.