Machine Learning Engineer, Reinforcement Learning

XenonStack is a Data and AI Foundry for Agentic Systems, enabling enterprises to design, deploy, operate, and scale intelligent agents across digital and physical environments.

We build enterprise-grade platforms across the agentic stack:

• Akira AI — Reasoning and agent orchestration. Turn models into collaborative, policy-governed agents that learn and act together.
• ElixirData — Agentic analytics intelligence. Explainable, decision-centric analytics for measurable business outcomes.
• NexaStack — Agentic infrastructure automation. Secure, compliant AI deployment across cloud, edge, and on-prem.
• MetaSecure — Trust, compliance and defense. Continuous assurance with AI-BOMs, risk scoring, and agentic security.

Our mission is to accelerate the world’s transition to AI + Human Intelligence by making agentic systems reliable, responsible, and enterprise-ready.

We are seeking anMachine Learning Engineer, Reinforcement Learning (Specialized in Reinforcement Learning)with2–5 years of experiencein applying RL to enterprise-grade systems. This role involves designing and deployingadaptive AI agentsthat continuously learn, optimize decisions, and evolve in dynamic environments.

You’ll work at the intersection ofRL research, agentic orchestration, and real-world enterprise workflows— building agents that do more than automate, but trulyreason, adapt, and improve over time.

Reinforcement Learning Development

• Design, implement, and trainRL algorithms(PPO, A3C, DQN, SAC) for enterprise decision-making tasks.
  

• Developcustom simulation environmentsto model business processes and operational workflows.
  

• Experiment withreward function designto balance efficiency, accuracy, and long-term value creation.
  

Agentic AI System Design

• Buildproduction-ready RL-driven agentscapable of dynamic decision-making and task orchestration.
  

• Integrate RL models withLLMs, knowledge bases, and external toolsfor agentic workflows.
  

• Implementmulti-agent systemsto simulate collaboration, negotiation, and coordination.
  

Deployment & Optimization

• Deploy RL agents oncloud and hybrid infrastructures(AWS, GCP, Azure).
  

• Optimize training and inference pipelines usingdistributed computing frameworks(Ray RLlib, Horovod).
  

• Applymodel optimization techniques(quantization, ONNX, TensorRT) for scalable deployment.
  

Evaluation & Monitoring

• Develop pipelines forevaluating agent performance(robustness, reliability, interpretability).
  

• Implementfail-safes, guardrails, and observabilityfor safe enterprise deployment.
  

• Document processes, experiments, and lessons learned for continuous improvement.
  

Technical Skills

• 2–5 years of hands-on experience withReinforcement Learning frameworks(Ray RLlib, Stable Baselines, PyTorch RL, TensorFlow Agents).
  

• Strong programming skills inPython; proficiency withPyTorch / TensorFlow.
  

• Experience designing and trainingRL algorithms(PPO, DQN, A3C, Actor-Critic methods).
  

• Familiarity withsimulation environments(Gymnasium, Isaac Gym, Unity ML-Agents, custom simulators).
  

• Experience inreward modeling and optimizationfor real-world decision-making tasks.
  

• Knowledge ofmulti-agent systemsand collaborative RL is a strong plus.
  

• Familiarity withLLMs + RLHF (Reinforcement Learning with Human Feedback)is desirable.
  

• Exposure tocloud platforms (AWS/GCP/Azure), containers (Docker, Kubernetes), and CI/CD for ML.
  

Professional Attributes

• Strong analytical and problem-solving mindset.
  

• Ability to balanceresearch depthwithpractical engineeringfor production-ready systems.
  

• Collaborative approach, working across AI, data, and platform teams.
  

• Commitment toResponsible AI(bias mitigation, fairness, transparency).
  

At XenonStack, we believe inshaping the future of intelligent systems. We foster aculture of cultivationbuilt on bold, human-centric leadership principles, wheredeep work, simplicity, and adoptiondefine everything we do.

Our Cultural Values

• Agency– Be self-directed and proactive.
  

• Taste– Sweat the details and build with precision.
  

• Ownership– Take responsibility for outcomes.
  

• Mastery– Commit to continuous learning and growth.
  

• Impatience– Move fast and embrace progress.
  

• Customer Obsession– Always put the customer first.
  

Our Product Philosophy

• Obsessed with Adoption– Making AI agents accessible and enterprise-ready.
  

• Obsessed with Simplicity– Turning complex RL + agentic challenges into intuitive, reliable systems.
  

Be part of our mission toreimagine adaptive, enterprise-grade AI agentswith Reinforcement Learning and accelerate the world’s transition toAI + Human Intelligence.