ML Engineer – ASR
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Job Description
ML Engineer improving automatic speech recognition for Sophea Meet. Building speech benchmarks, optimizing production inference, and advancing KIEFER’s AI ecosystem.
Responsibilities:
- Improve automatic speech recognition (ASR) for Sophea Meet
- Own the improvement loop from data and model experiments through evaluation and production performance
- Train, fine-tune, adapt, and evaluate speech recognition models based on measured product needs
- Build representative benchmarks and regression tests for real meeting audio
- Prepare speech datasets and investigate difficult cases, including noise, overlapping speakers, long sessions, and specialized terminology
- Use error analysis to determine which model, data, or pipeline changes improve transcription quality
- Improve inference accuracy, latency, throughput, reliability, and cost
- Deploy and monitor model versions with Engineering
- Work with Product to set quality goals and prioritize improvements
- Prioritize additional languages, starting with Greek, based on usage and product needs
Requirements:
- 3+ years of relevant ML or research engineering experience, with clear technical ownership and measurable results
- Strong hands-on ASR or speech ML experience, including a speech recognition system you shipped or materially improved
- Experience training, fine-tuning, or adapting deep learning models using real-world audio data
- Strong evaluation skills, including WER/CER, benchmark design, error analysis, and regression testing
- Strong Python skills
- Hands-on experience with PyTorch, JAX, TensorFlow, or a similar framework
- Solid software engineering skills
- Experience deploying, monitoring, and maintaining production ML systems
- Multilingual ASR, accent robustness, domain adaptation, or diarization experience is nice to have
- Streaming speech recognition and low-latency inference experience is nice to have
- MLOps, evaluation, or data pipelines for speech systems experience is nice to have
- Experience with meeting transcription, voice agents, or enterprise speech products is nice to have
Benefits:
- Competitive package aligned with talent benchmarks
- Hands-on work shaping AI capabilities for Kiefer Tech’s products and real-world applications
- AI-native environment with challenges across model development, evaluation, and production AI systems
- Engineering-first culture
- High autonomy
- Low bureaucracy
- Space to shape meaningful AI products
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