NLP Engineer at CLEAR Global, Remote, Uganda

Job Description

The NLP Engineer will execute the experimental methodology for generating and using synthetic data and inform this process, collaborating with CLEAR Global’s internal team and external partners. They will play a key role in the creation of synthetic voice data in up to 3 languages, as well as the assessment, training and evaluation of ASR models under step 3 of the project, in the same 3 languages. The NLP Engineer will also provide ad-hoc advisory support, feeding into decision making, supporting monitoring activities, and inputting into the final report and publications for the project. They will be expected to join occasional calls with CLEAR Global and partners and share regular progress updates within CLEAR Global’s workspaces.

Responsibilities

Key activities include:

Project Design and Planning Engaging with partners and team members to define work steps.
Researching specific questions as required, such as the role of non-standard transcripts.
Providing feedback where relevant to inform the selection of languages targeted in this project.
Tracking compute costs for later reporting.
Provide inputs, especially quantitative data and key learnings, for the final report and publications for this project.
Creating Synthetic Voices for selected languages.

Reviewing available open TTS/speech synthesis models and recommend 1-2 to use in this experiment.
Reviewing available data, e.g. on open.bible, for fine-tuning the TTS model.
Cooperating with other CLEAR Global team members to create 5-10 hours of speech data in one language to fine-tune TTS model if needed.
Running the TTS model to create synthetic voice data from the synthetic text created under step 1 and other text sources. We expect this to result in 100+ hours of synthetic voice data per language.
Training ASR Models and Evaluating PerformancePreparing evaluation datasets for the given languages.
Reviewing available ASR models and evaluating them based on available evaluation datasets.
Training/fine-tuning the ASR model with the synthetic voice data created in step 2.
Evaluating trained/fine-tuned ASR model.
Aggregating results, especially quantitative data on changes in model performance and costs for model training, and key learnings.

Deliverables

Documented review of open TTS/speech synthesis models with 1-2 recommended for use in this project.
Documented review of available data, e.g. on open.bible, for fine-tuning the TTS model 100+ hours of synthetic voice data created through running the TTS model to create voice data from the synthetic text.
Documented review and recommendation of available ASR models.
Selection of evaluation datasets for the agreed languages and baseline evaluation of ASR model
Fine-tuned ASR model and evaluation of fine-tuned ASR model.
Compute costs tracked and reported. This will be included in reports for cost estimates for partners looking to replicate the process.
Summary of results and findings, especially of quantitative data including changes in model performance and costs for model training and key learnings.

Qualifications and experience required

The right candidate is an energetic team player, flexible and dynamic in approach, who agrees with CLEAR Global’s basic beliefs and values and who can work remotely with team members based throughout the world.

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Natural Language Processing or related field.
Proven experience in NLP. This can be demonstrated by involvement in NLP projects, code repositories, paper authorship, other non-technical write-ups, and/or dataset/model/demo publications.
Experience working with under-resourced languages and non-Latin writing systems.
Strong analytical and problem-solving skills.
Excellent communication and collaboration skills.
Excellent working knowledge of written and spoken English.
Passion for making a positive impact in the world.