Posted today · be early
Applied AI Research Engineer
Appen Butler Hill Inc
IndiaremotePosted 1 day ago
Skill Required
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Key highlights
- Required experience: 3+ years professional engineering
- Company: Appen
- Focus: Implementation and AI research assets
- Key benefit: Flexibility in work style
Role overview
As an Applied Research Engineer, you’ll build practical AI research assets that support Frontier lab initiatives and customer engagements. This is an implementation-focused role for someone who enjoys turning research concepts into working systems, working with a high degree of autonomy to develop solutions that can be reused across customer opportunities in partnership with the GenAI Research team.
Responsibilities
- Build reinforcement learning and agent environments for real customer and Frontier lab use cases, including task specifications, scoring, and evaluation.
- Develop benchmarks and evaluation harnesses to measure model and data quality across areas such as accuracy, robustness, safety, latency, and cost.
- Build LLM pipelines and agentic systems that 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 your work.
- Partner with the GenAI Research team and cross-functional stakeholders to turn technical work into reusable assets for customer engagements.
Requirements
- Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Machine Learning, or a related technical field.
- 3+ years of professional engineering or relevant industry experience in AI/ML or software engineering.
- Strong software engineering skills and experience building reliable, maintainable AI systems.
- Hands-on experience building agentic systems, reinforcement learning environments, LLM pipelines, or similar AI systems.
- Experience building evaluation harnesses, benchmarks, or model testing pipelines.
- Ability to work independently on technical problems and move quickly from an idea or research question to a working solution.
- Strong understanding of experimentation, reproducibility, and technical documentation.
Nice to have
- Developed synthetic data generation systems or datasets.
- Published research papers, benchmarks, or other technical research.
- Worked with SWE-bench or similar software engineering evaluation environments.
- Built or deployed local inference, open-weight models, or self-hosted model environments.
Benefits
- Culture of innovation, collaboration, and excellence.
- Opportunity to work on complex challenges that shape the future of AI.
- Culture that values humility over ego.
- Flexibility to deliver in a way that works for you and your team.
- Support via tools, resources, and development opportunities to continue to build capability over time.
Additional details
- Appen has been a leader in AI training data for over 30 years.
- Specialise in human generated data to train, fine tune, and evaluate models across generative AI, large language models, computer vision, and speech recognition.
- Platform supports model pre training, supervised fine tuning, evaluation and benchmarking, safety and red teaming, and multilingual global expansion.
- Originally posted on Himalayas.