Applied AI Research Engineer

AppenArtificial intelligence

Remote IndiaFull-TimeMiddle

Salary not disclosed

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Job Details

Experience: 3+ years of professional engineering or relevant industry experience in AI/ML or software engineering.
Required Skills: Artificial IntelligenceMachine LearningSoftware EngineeringLLM

Hold a Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Machine Learning, or a related technical field.
Have 3+ years of professional engineering or relevant industry experience in AI/ML or software engineering.
Bring strong software engineering skills and experience building reliable, maintainable AI systems.
Have hands-on experience building agentic systems, reinforcement learning environments, LLM pipelines, or similar AI systems.
Have experience building evaluation harnesses, benchmarks, or model testing pipelines.
Be able to work independently on technical problems and move from an idea or research question to a working solution.
Have a strong understanding of experimentation, reproducibility, and technical documentation.
Nice to have experience developing synthetic data generation systems or datasets.
Nice to have published research papers, benchmarks, or other technical research.
Nice to have experience with SWE-bench or similar software engineering evaluation environments.
Nice to have built or deployed local inference, open-weight models, or self-hosted model environments.

Build reinforcement learning and agent environments for customer and Frontier lab use cases, including task specifications, scoring, and evaluation.
Develop benchmarks and evaluation harnesses to measure model and data quality across accuracy, robustness, safety, latency, and cost.
Build LLM pipelines and agentic systems to support research, evaluation, and customer trials.
Run fine-tuning, adapter, and other model experiments to evaluate how data and methods influence model behavior.
Deploy local or self-hosted models for evaluation, inference, and automation workflows.
Document experiments, configurations, data, results, and known limitations so other engineers can reproduce and build on the work.
Partner with the GenAI Research team and cross-functional stakeholders to turn technical work into reusable assets for customer engagements.

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