Applied AI Research Engineer

Appen is hiring an Applied AI Research Engineer for its AI Data Opportunities – Applied AI & Research team. This is a permanent, regular full-time position that can be performed remotely from India.

The role focuses on building practical AI research assets that support Frontier lab initiatives and customer engagements. It is an implementation-focused position for an engineer who enjoys turning research concepts into working systems.

The Applied AI Research Engineer will work with a high degree of autonomy, experiment with new approaches, and develop solutions that can be reused across customer opportunities. The role also involves working closely with the GenAI Research team and cross-functional stakeholders to turn technical ideas into practical applications.

About Appen

Appen has been a leader in AI training data for over 30 years. The company specializes in human-generated data used to train, fine-tune, and evaluate models across generative AI, large language models, computer vision, and speech recognition.

Appen’s AI-assisted data annotation platform and global crowd of more than 1 million contributors across over 200 countries support model pre-training, supervised fine-tuning, evaluation and benchmarking, safety and red teaming, and multilingual global expansion.

Appen describes its workplace culture as focused on innovation, collaboration, and excellence, with an emphasis on curiosity, accountability, and delivering high-quality AI solutions for frontier models.

What is the Applied AI Research Engineer Role?

The Applied AI Research Engineer role is centered on converting AI research ideas into practical, working systems.

The selected candidate will contribute to reinforcement learning environments, agentic systems, LLM pipelines, model evaluation, benchmarking, fine-tuning experiments, and local or self-hosted model deployment.

The role also requires documenting experiments, configurations, data, results, and known limitations so that other engineers can reproduce and build upon the work.

Key Responsibilities

1. AI Research & System Development

The engineer will build practical AI systems and research assets for customer and Frontier lab use cases.

Responsibilities include:

• Building reinforcement learning and agent environments.
• Creating task specifications, scoring systems, and evaluation processes.
• Developing practical solutions from research concepts.
• Experimenting with new approaches and methods.
• Creating reusable technical assets for customer opportunities.

2. Model Evaluation & Benchmarking

The role includes developing systems to evaluate model and data quality.

Key responsibilities include:

• Developing benchmarks and evaluation harnesses.
• Measuring model and data quality.
• Evaluating accuracy, robustness, safety, latency, and cost.
• Building model testing pipelines.
• Applying structured experimentation to assess AI system performance.

3. LLM Pipelines & Agentic Systems

The engineer will build LLM-based systems that support research, evaluation, and customer trials.

This includes:

• Building LLM pipelines.
• Developing agentic systems.
• Creating systems for research and evaluation.
• Supporting customer trials with practical AI implementations.
• Developing reusable solutions that can be applied across customer opportunities.

4. Fine-Tuning & Model Experiments

The position involves conducting experiments to understand how data and different technical approaches influence model behavior.

The engineer will:

• Run fine-tuning experiments.
• Work with adapter-based approaches.
• Evaluate different model methods.
• Analyze how data influences model behavior.
• Document experiment configurations and results.

5. Local & Self-Hosted Model Deployment

The role also involves deploying models for practical evaluation and automation.

Responsibilities include:

• Deploying local models.
• Working with self-hosted models.
• Supporting evaluation workflows.
• Supporting inference workflows.
• Using models within automation workflows.

6. Documentation & Reproducibility

A key part of the role is making technical work reproducible and understandable for other engineers.

This includes documenting:

• Experiments
• Configurations
• Data
• Results
• Known limitations

The documentation should allow other engineers to reproduce experiments and build upon completed work.

7. Cross-Functional Collaboration

The Applied AI Research Engineer will partner with the GenAI Research team and other cross-functional stakeholders.

The role involves turning technical research and engineering work into reusable assets that can support customer engagements and practical AI applications.

Eligibility Criteria

Candidates should have:

• A 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.
• Experience building reliable and maintainable AI systems.
• Hands-on experience building agentic systems, reinforcement learning environments, LLM pipelines, or similar AI systems.
• Experience developing evaluation harnesses, benchmarks, or model testing pipelines.
• Ability to work independently on technical problems.
• Ability to move quickly from an idea or research question to a working solution.
• Strong understanding of experimentation and reproducibility.
• Strong technical documentation skills.

Nice-to-Have Skills

The following experience is listed as an advantage:

• Developing synthetic data generation systems or datasets.
• Publishing research papers, benchmarks, or other technical research.
• Working with SWE-bench or similar software engineering evaluation environments.
• Building or deploying local inference environments.
• Working with open-weight models.
• Building or deploying self-hosted model environments.

Work From Home / Remote Work

This is a Remote India position. The job is listed as permanent, regular full-time work within Appen’s AI Data Opportunities – Applied AI & Research team.

Appen states that employees work on complex AI challenges across industries and geographies and are supported with tools, resources, and development opportunities to continue building their capabilities.

Career & Work Environment

Appen describes its working environment as one that values:

• Innovation
• Collaboration
• Excellence
• Curiosity
• Accountability
• Technical quality

The company states that employees work on challenges that contribute to the future of AI across industries and geographies.

Who Can Apply?

This opportunity is suitable for AI/ML and software engineering professionals with experience building practical AI systems.

It may be relevant to candidates who have worked with:

• Reinforcement learning
• AI agents
• LLM pipelines
• Model evaluation
• AI benchmarking
• Fine-tuning
• Local or self-hosted models
• AI/ML software engineering

Candidates should also be comfortable working independently, conducting experiments, documenting results, and collaborating with research and cross-functional teams.

Hiring Process Information

Appen states that it may use artificial intelligence tools to support parts of its hiring process. These tools may assist with reviewing applications, analyzing resumes, assessing responses, and identifying potential inconsistencies or verification signals in application materials.

The company states that these tools assist its recruitment team but do not replace human judgment, and that final hiring decisions are made by humans.

How to Apply

Interested candidates can apply directly by clicking the “Apply Now” button below. The button will take you to Appen’s official application page for the Applied AI Research Engineer position.

Candidates should review the listed education, experience, technical skills, and responsibilities before submitting their application.

Frequently Asked Questions

1. What is the job title?

The position is for an Applied AI Research Engineer at Appen.

2. Is this a remote job?

Yes. The position is listed as Remote India.

3. Is this a full-time position?

Yes. It is a Permanent – Regular Full-time position.

4. How much experience is required?

Candidates should have 3+ years of professional engineering or relevant industry experience in AI/ML or software engineering.

5. What educational qualifications are required?

Candidates should have a Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Machine Learning, or a related technical field.

6. What technical experience is required?

The role requires hands-on experience with agentic systems, reinforcement learning environments, LLM pipelines, or similar AI systems, along with experience in evaluation harnesses, benchmarks, or model testing pipelines.

7. Is reinforcement learning experience relevant?

Yes. Building reinforcement learning and agent environments is specifically included among the responsibilities.

8. What are some nice-to-have skills?

Synthetic data generation, research publications, SWE-bench or similar evaluation environments, local inference, open-weight models, and self-hosted model environments are listed as nice-to-have experience.

9. Does the role involve working with research teams?

Yes. The engineer will work closely with the GenAI Research team and cross-functional stakeholders.

10. How can I apply?

Click the “Apply Now” button below to visit Appen’s official application page and submit your application.