ML Engineer — ASR

Kiefer; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

KIEFER is seeking an ML Engineer to advance automatic speech recognition for Sophea Meet. You will work with real meeting audio, own the data-to-production loop, and iteratively improve models with product-aligned quality goals.

The role emphasizes English-language ASR initially, with Greek and other languages prioritized later. You will design experiments, assess model performance using WER/CER, and collaborate with Engineering and Product teams to deliver robust, low-latency speech solutions

Qualifications

• 3+ years of ML or research engineering experience with clear ownership and measurable results.
• Strong hands-on ASR or speech ML experience, including a model you shipped or significantly improved.
• Experience training, fine-tuning, or adapting deep learning models using real-world audio data.

Responsibilities

• Train, fine-tune, adapt, and evaluate speech recognition models based on product needs.
• Build benchmarks and regression tests for real meeting audio.
• Prepare speech datasets and investigate difficult cases like noise, overlapping speakers, long sessions, and terminology.
• Use error analysis to decide model, data, or pipeline improvements for transcription quality.
• Improve inference in accuracy, latency, throughput, reliability, and cost.
• Deploy and monitor model versions with Engineering and Product to set quality goals.

Skills

Python

ASR

PyTorch

JAX

TensorFlow

Tools

PyTorch

TensorFlow

JAX

Job description

Описание: KIEFER is building Greece's integrated AI ecosystem. From renewable energy infrastructure and AI systems to robotics and enterprise applications, we connect the technologies that power Europe's intelligent future. Founded in 2014, KIEFER has delivered 600MW+ of energy projects and is now developing sovereign AI infrastructure, enterprise AI products and physical AI systems for Greece and Southeast Europe. We are looking for an ML Engineer to improve automatic speech recognition (ASR) for Sophea Meet. You will work with real meeting audio and own the improvement loop from data and model experiments through evaluation and production performance. The initial language focus is English. Additional languages, starting with Greek, will be prioritized with Product based on usage and product needs.

Задачи:

• 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 decide which model, data, or pipeline changes will improve transcription quality.
• Improve inference across accuracy, latency, throughput, reliability, and cost.
• Deploy and monitor model versions with Engineering, while working with Product to set quality goals and prioritize improvements.

Требования: 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, hands‑on experience with PyTorch, JAX, TensorFlow, or a similar framework, and solid software engineering skills. Experience deploying, monitoring, and maintaining production ML systems. Multilingual ASR, accent robustness, domain adaptation, or diarization. Streaming speech recognition and low-latency inference. MLOps, evaluation, or data pipelines for speech systems. Experience with meeting transcription, voice agents, or enterprise speech products.

Compensation: Competitive package aligned with talent benchmarks.

Impact: Hands-on work shaping AI capabilities for Kiefer Tech’s products and real-world applications.

AI-native environment: Real challenges across model development, evaluation, and production AI systems.

Culture: Engineering-first, high autonomy, low bureaucracy, and space to shape meaningful AI products.