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Senior Machine Learning Engineer, Speech Recognition (ASR)
Отличная позиция в быстрорастущем MedTech стартапе с сильной миссией и современным стеком технологий. Работа в Копенгагене и гибридный формат делают предложение очень привлекательным для Senior-специалистов.
Сложность вакансии
Роль требует глубокой экспертизы в узкой области ASR (распознавание речи) и умения работать с высоконагруженными ML-системами в продакшене. Высокая планка ответственности из-за специфики медицинской сферы.
Анализ зарплаты
Зарплата в объявлении не указана, но для позиции Senior ML Engineer в Копенгагене рыночный диапазон составляет от 650,000 до 900,000 DKK в год. Учитывая сложность сферы (Healthcare) и требования к ASR, можно ожидать предложение по верхней границе рынка.
Сопроводительное письмо
I am writing to express my strong interest in the Senior Machine Learning Engineer position at Corti. With a deep background in speech recognition and a passion for applying AI to healthcare, I am impressed by Corti's mission to provide medical expertise globally through high-precision ASR and reasoning models.
In my previous roles, I have gained extensive experience training and fine-tuning end-to-end ASR models at scale using PyTorch, and I am well-versed in optimizing inference latency using NVIDIA Triton and Kubernetes. I particularly appreciate Corti's focus on medical-grade precision and real-world validation, as I believe that robust evaluation frameworks and domain adaptation are critical for the safety and reliability of clinical AI.
I am excited about the opportunity to take technical ownership of core ASR components and collaborate with your talented team in Copenhagen. My experience in building production-grade ML services and my commitment to engineering excellence align perfectly with the requirements of this role.
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Откликнитесь в corti уже сейчас
Присоединяйтесь к Corti, чтобы создавать медицинский ИИ нового поколения и спасать жизни с помощью технологий распознавания речи!
Описание вакансии
Overview
We are on a mission to ensure everyone has access to medical expertise, no matter where they are.
Half the world still lacks access to quality healthcare. Even in advanced systems, outcomes are uneven, and clinicians are overwhelmed. Medical knowledge grows faster than human capacity can keep up.
Corti is building the infrastructure to close that gap. Our AI platform expands access to medical expertise, reducing errors, restoring time to clinicians, and making care more affordable, accessible, and human again.
There is no quality healthcare without a quality dialogue, and no reliable AI without a strong foundation. Help us build both.
Why Corti?
Corti is building the intelligence layer for global healthcare. We give every developer, product team, and healthcare innovator access to medical-grade AI, so the world can deliver care that is faster, safer, and more human.
Built entirely for healthcare and adjacent industries, Corti’s models are trained on real-world data and optimized for precision, safety, and regulatory trust.
Through modular APIs, teams can embed medical speech recognition, summarization, reasoning, and much more directly into healthcare products without reinventing the foundation.
We power the builders who are redefining how healthcare works, from startups creating new patient experiences to enterprises modernizing the systems that care depends on.
If you believe that AI purpose-built for medicine will define the next century of healthcare, you belong at Corti.
The Role
As a Senior Machine Learning Engineer focused on speech recognition, you will build and operate Corti’s medical-grade ASR systems. You will work on training and fine-tuning speech models at scale, building strong validation practices, and deploying low-latency inference services that are dependable in real-world conditions.
This role includes ownership across the ASR lifecycle, from data strategy and training to evaluation, serving, and monitoring. The team values careful measurement, practical engineering, and collaboration with product and platform partners.
What you’ll be doing
- Train and fine- tune ASR models at scale, including dataset strategy, augmentation, and domain adaptation to real-world clinical audio.
- Build and improve validation and evaluation frameworks, including WER and targeted analysis across speakers, environments, devices, and clinical terminology.
- Deploy and operate ASR inference services with focus on reliability, latency, and efficiency in production.
- Optimize inference latency and throughput, including batching strategies, model export choices, and hardware-aware profiling.
- Build and maintain APIs and services in frameworks like FastAPI, Kafka, and NVIDIA Triton, and deploy and run them on Kubernetes.
- Take technical ownership of core ASR components, shaping best practices for modelling, evaluation, and production reliability across the team supporting the growth of engineers working on speech systems.
- Work closely with product and platform teams on safe rollouts, monitoring, and continuous improvement based on real-world feedback.
Technologies you may work with
- PyTorch
- Apache Kafa
- NVIDIA Triton Inference Server
- FastAPI for ML APIs and services
- Kubernetes for deployment and operations
- Common MLOps tooling for experiment tracking, model versioning, and monitoring
What you bring
- Strong programming skills in Python and the ability to contribute to production-grade codebases.
- Hands-on experience in speech recognition and ASR, including at least some of the following:
+ Training or finetuning end-to-end ASR models (CTC/TDT/AED) at meaningful scale.
+ Designing evaluation and validation approaches, including WER and deeper error analysis.
+ Deploying and operating ASR services in production, including performance and reliability work.
- Experience building ML systems that can be deployed and operated, including pipelines, CI and CD practices, and monitoring.
- Clear communication and collaboration skills across research, engineering, and product.
- A Master’s degree in computer science, engineering, mathematics, statistics, physics, or a related field, or equivalent professional experience.
- Nice to have: Experience with multilingual ASR, streaming inference, noisy audio conditions, or healthcare privacy and compliance constraints. Experience with Go.
Life at Corti
- You will be reporting to the Director of Engineering
- The position is full-time and starts as soon as possible
- Hybrid working environment in our Copenhagen Office
- Equipment provided by Corti
Ready to dive into the world of Corti? Hit that 'Apply' button, and let's start working together on reshaping the dialogue in healthcare, making a real difference for millions of patient outcomes around the world.
🤝 Bringing in top talent from all backgrounds is crucial in our pursuit to improve the world of healthcare. We encourage applications from all people and do not discriminate based on race, religion, national origin, gender, sexual orientation, age, and/or disability status.
At Corti, experience comes in many forms, and we’re passionate about creating teams with a multitude of perspectives! If you believe your experience is close to what we’re looking for but not an exact match, we still hope you’ll consider applying!
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Навыки
- Python
- PyTorch
- Kubernetes
- CI/CD
- MLOps
- Docker
- Kafka
- Go
- FastAPI
- ASR
- NVIDIA Triton
Возможные вопросы на собеседовании
Проверка понимания специфики доменной адаптации в медицине.
Как бы вы подошли к задаче адаптации общей ASR-модели к специфической медицинской терминологии при ограниченном количестве размеченных данных?
Оценка навыков оптимизации производительности.
Какие стратегии вы используете для минимизации задержки (latency) при стриминговом распознавании речи в продакшене?
Проверка опыта работы с инфраструктурой.
Опишите ваш опыт работы с NVIDIA Triton Inference Server: как вы решали проблемы с пропускной способностью и управлением ресурсами GPU?
Оценка методологии тестирования.
Помимо стандартного WER, какие метрики вы считаете критически важными для оценки качества ASR в условиях шумной клинической среды?
Проверка навыков командного взаимодействия.
Расскажите о случае, когда вам пришлось принимать сложное архитектурное решение в ML-проекте. Как вы аргументировали свой выбор перед командой?
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