Research Engineer, Applied AI

HeyMilo AI

Research Engineer building reproducible RL environments and evaluation systems for HeyMilo’s conversational AI hiring workflows. Testing and improving proprietary and open-weight models across HR and recruiting.

Posted 8/5/2026full-timeColombo • 🇱🇰 Sri LankaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing reinforcement learning environments and evaluating model performance within HR and recruiting workflows. Proficient in Python programming, LLM fine-tuning, and creating reproducible experiments in cloud and Docker environments.

Highest-signal resume keywords

PhD In AI Or Machine LearningReinforcement Learning ExpertisePython Software EngineeringLLM Fine-Tuning ExperienceDocker And Cloud Environments

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Reinforcement LearningReward DesignPolicy OptimizationEvaluation MethodologyModel TrainingEvaluation FrameworksOpen-Weight ModelsSimulated EnvironmentsTask GenerationRecord Matching

Soft Skills

Problem OwnershipAdaptabilityCollaboration

Tools & Technologies

DockerLinuxCloud Environments

Certifications & Qualifications

PhD In AI Or Machine Learning

Industry Keywords

HR TechnologyApplicant Tracking SystemsRecruiting WorkflowsStaffing Operations

Tech Stack

Tools & technologies

CloudDockerLinuxPython

About the role

Key responsibilities & impact

Design and build reinforcement learning environments for HR and recruiting workflows, including realistic simulators, tool interfaces, and reproducible episodic task generation
Evaluate model performance across the HR/recruiting stack, including multi-step recruiter workflows, integrations, and record matching
Design verifiable reward functions for intermediate actions and end states that resist reward hacking
Build and maintain evaluation harnesses for frontier and open-weight models, including scoring and cost tracking
Run post-training experiments such as RLVR-style fine-tuning of open models
Package environments and results for reproducibility and contribute to research write-ups and published evaluations
Collaborate with analysts to turn task and rubric ground truth into running environments
Take workflows from problem definition through reproducible environments for model training and evaluation

Requirements

What you’ll need

PhD in AI, Machine Learning, or a closely related field (required)
Solid grounding in reinforcement learning and LLM post-training, including reward design, policy optimization, and evaluation methodology
Strong software engineering skills in Python
Hands-on experience with LLMs, including evaluations, agentic loops, tool calling, and fine-tuning
Comfortable with Docker, Linux, and cloud environments for reproducible experiments
Ability to operate in ambiguity, move quickly, and own problems end to end
Published research or open-source contributions in ML, RL, or evaluation (bonus)
Experience with RL/evaluation frameworks and simulated or sandboxed environments (bonus)
Experience training or fine-tuning open-weight models at any scale (bonus)
Familiarity with HR technology, applicant tracking systems, recruiting workflows, or staffing operations (bonus)

Benefits

Comp & perks

Competitive pay and benefits
Work reviewed by and published alongside experienced researchers
High visibility, fast-paced, execution-driven environment
Ground-floor role with real influence over how the applied AI team builds