The job, in the employer's words
About the Role
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.
You’ll work with a high degree of autonomy, experimenting with new approaches and developing solutions that can be reused across customer opportunities. You’ll partner closely with the GenAI Research team and cross-functional stakeholders to bring technical ideas into practical applications.
Your Impact
- 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.
What You Bring
- 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 Haves
- 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.
Why You'll Love Working Here
At Appen, we foster a culture of innovation, collaboration, and excellence. We value curiosity, accountability, and a commitment to delivering the highest quality AI solutions for frontier models.
You'll work on complex challenges that shape the future of AI across industries and geographies, alongside talented people in a culture that values humility over ego. You'll have the flexibility to deliver in a way that works for you and your team, supported by tools, resources, and development opportunities to continue to build your capability over time.
About Appen
Appen has been a leader in AI training data for over 30 years. We specialise in human generated data to train, fine tune, and evaluate models across generative AI, large language models, computer vision, and speech recognition. Our AI assisted data annotation platform and global crowd of more than 1 million contributors in over 200 countries support model pre training, supervised fine tuning, evaluation and benchmarking, safety and red teaming, and multilingual global expansion.
Requirements mentioned
- 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.
- 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.
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