ML Engineer – ASR

• 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

• 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

• 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