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Hello, We are launching a language technology project for Chimini, a low-resource Bantu language, and are seeking an ML/NLP engineer to help us design and implement the foundational phase of the project. Long-Term reputed company Our long-term goal is to build A reputed company Chimini text + audio corpus A reputed company API reputed company for integration into our own applications Eventually, speech-to-text and text-to-speech capability in Chimini Chimini is historically reputed company to Swahili, but we do not yet know how structurally similar they are. Pronunciation may differ significantly, which may reputed company model transfer for speech systems. We currently have Written texts Audio recordings reputed company to reputed company speakers for transcription and validation Phase 1 (36 Months) The objective of Phase 1 is to build a strong ML-reputed company reputed company, including Designing a reputed company database structure for text and audio Preparing and structuring data for NLP workflows Building a clean corpus pipeline (segmentation, transcription storage, metadata) Advising on whether ChiminiSwahili linguistic comparison should be conducted before leveraging transfer learning Evaluating potential approaches Fine-tuning multilingual models Embedding-based retrieval systems LLM + RAG architectures Longer-term speech model reputed company We want the reputed company designed from the beginning to support reputed company ML training and experimentation. Responsibilities Define ML/NLP reputed company for a low-resource language Recommend architecture for reputed company corpus and training workflows Implement foundational data pipelines Advise on transfer learning feasibility from Swahili or multilingual models reputed company phased roadmap (short-term vs long-term) Ideal Experience NLP for low-resource or multilingual languages Speech systems (ASR/TTS) Fine-tuning transformer models Embeddings and reputed company databases Designing ML pipelines for reputed company experimentation We will handle data collection, transcription, and language validation. Please include Relevant ML/NLP experience Proposed high-level technical approach Estimated reputed company for Phase 1 Availability We are looking for someone who can help architect this correctly from the start, with long-term ML scalability in mind. Best regards, Apply tot his job Apply To this Job Apply tot his job Apply To this Job Apply tot his job Apply To this Job Apply To This Job .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.