Applied AI Research Engineer
J
JobgetherApplied AI
Based in India; remote working arrangement in IndiaFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- At least 3 years of professional engineering or relevant industry experience in AI/ML, software engineering, or a closely related field.
- Required Skills
- Software Engineering
Requirements
- Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Machine Learning, or a related technical discipline.
- At least 3 years of professional engineering or relevant industry experience in AI/ML, software engineering, or a closely related field.
- Strong software engineering capabilities and experience building reliable, maintainable, reusable AI or software systems.
- Hands-on experience developing agentic systems, reinforcement learning environments, LLM pipelines, or comparable AI applications.
- Experience creating evaluation harnesses, benchmarks, model-testing pipelines, or systematic approaches to measuring AI system performance.
- Strong understanding of experimentation, reproducibility, evaluation methodologies, and technical documentation.
- Ability to work independently on complex technical problems and deliver working solutions from ideas or research questions.
- Experience with synthetic data generation systems or dataset development is a plus.
- Published research papers, benchmarks, or other technical research is advantageous.
- Experience with SWE-bench or comparable software engineering evaluation environments is desirable.
- Experience building or deploying local inference systems, open-weight models, or self-hosted model environments is a plus.
Responsibilities
- Build reinforcement learning and agent environments, including task specifications, scoring mechanisms, evaluation criteria, and testing workflows.
- Develop benchmarks and evaluation harnesses to assess model and data quality, including accuracy, robustness, safety, latency, and cost.
- Design and implement LLM pipelines and agentic systems for research, model evaluation, experimentation, and customer trials.
- Conduct fine-tuning, adapter, and other model experiments to assess how datasets, techniques, and configurations affect model behavior and performance.
- Deploy local and self-hosted models for evaluation, inference, experimentation, and automation workflows.
- Document experiments, configurations, datasets, results, methodologies, and known limitations so others can reproduce and extend the work.
- Collaborate with AI research teams and cross-functional stakeholders to turn technical concepts into reusable solutions and assets.
- Investigate technical problems, prototype approaches, and implement research ideas as functional, production-oriented systems.
- Apply strong engineering practices to develop reliable, maintainable AI systems.
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