Staff Software Engineer, Applied ML

About the Role

DigitalOcean's Agentic AI organization provides a powerful inference cloud, Managed Agents, and robust Agent Learning Services that enable customers to run AI inference confidently at scale. We are looking for a Staff Software Engineer to serve as a technical leader within our Agent Learning Services team, driving the Applied ML strategy to build a robust Continuous Learning Loop for our customers.

This is a high-impact leadership role where you will define the machine learning architectures that allow AI agents to learn from historical data, simulate synthetic data, and continuously self-improve. You will partner closely with our Infrastructure and product teams to translate complex ML concepts into scalable, production-ready continuous learning platforms.

What You’ll Be Doing

• Continuous Learning Architecture: Designing and implementing the core Applied ML pipelines for a Continuous Learning Loop, enabling AI agents to autonomously evaluate, learn, and adapt based on user interactions and synthetic data.
• Agent Optimization & Fine-Tuning: Developing the machine learning models and methodologies behind the Agent Optimizer, leveraging techniques like Supervised Fine-Tuning (SFT), Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF), and targeted optimization strategies to improve agent performance.
• Evaluations & Simulations: Building robust ML frameworks for Simulations and Evaluations, creating deterministic and non-deterministic scoring mechanisms, reward models, and objective metrics to benchmark agent behavior in complex multi-turn scenarios.
• Signals Processing: Architecting ML systems to extract high-quality, actionable "Signals" from noisy data, user feedback, and chat logs to feed back into the training and optimization loops.
• Model Application & Deployment: Bridging the gap between ML research and production by ensuring models are designed for efficient inference, working closely with RL Infrastructure engineers to deploy optimization algorithms at scale.
• Technical Strategy: Driving the long-term Applied ML roadmap for the Agentic AI organization, evaluating emerging research in continuous learning and agentic frameworks to keep DigitalOcean at the cutting edge.
• Cross-Functional Influence: Partnering closely with Product Managers, Data Scientists, and Infrastructure engineers to define product capabilities, experimental setups, and performance benchmarks for our agent learning services.
• Mentorship & Elevation: Mentoring senior engineers and ML practitioners, leading architectural reviews, and establishing best practices for model evaluation, ML pipeline observability, and reproducible experimentation.

What We Expect From You

• Extensive Experience: 10+ years of software engineering and machine learning experience, with a proven track record operating at a Staff, Principal, or Architect level designing, building, and scaling production-grade ML systems.
• Applied ML & Domain Mastery: Proven ability to decompose complex, ambiguous business goals into manageable machine learning problems that deliver strong & measurable impact. Deep expertise in Reinforcement Learning (RL), LLM fine-tuning methodologies (e.g., SFT, DPO, PPO), and continuous learning loops.
• Evaluation Frameworks & Guardrails: Deep experience building evaluation frameworks for non-deterministic AI systems, including establishing robust reward models, automated red-teaming, safety guardrails, scoring rubrics, and continuous benchmarking platforms.
• Research Contributions & Thought Leadership: Demonstrated leadership in the broader AI community through a track record of top-tier academic publications, high-impact open-source contributions, or technical patents in Applied ML and AI agent architectures.
• Cross-Functional AI Leadership: Proven expertise driving technical vision and execution across engineering, product, and infrastructure teams to build scalable, production-ready AI solutions from concept to deployment.

*This job is located in Hyderabad, India

JR: 2026-7876

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