обновлено 9 дней назад
Member of Technical Staff, Machine Learning (AI)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Member of Technical Staff, Machine Learning (AI) (Python/PyTorch/JAX): Building and improving production machine learning components across data, training, evaluation, and inference with an accent on reliable long-running workflows, persistent context, and real-world task completion. Focus on fine-tuning models, maintaining data pipelines, debugging production incidents, and optimizing latency, cost, reliability, and safety on GPU-based systems.
Location: Remote, Singapore
Company
is building proactive AI-native applications that help people manage conversations, errands, organization, and workflows with minimal prompting.
What you will do
- Build and improve machine learning components across data, training, evaluation, and inference.
- Fine-tune and adapt models within larger production systems.
- Implement evaluation and testing to understand model behavior and improve reliability.
- Build and maintain pipelines for real-world and synthetic data.
- Debug model issues, performance problems, and production incidents.
- Ship iterative improvements while collaborating with senior ML engineers and product teams.
Requirements
- Strong foundations in machine learning and modern neural architectures.
- Hands-on experience training, fine-tuning, or deploying machine learning models.
- Production-quality Python programming skills and willingness to learn new tools quickly.
- Experience or familiarity with PyTorch, JAX, and production ML systems running on GPUs.
- Ability to work through ambiguity, develop ownership, and improve systems through iteration.
Culture & Benefits
- Work with a small, high-talent-density, hands-on team.
- Balance rapid execution with high-quality delivery and continuous learning.
- Operate under real production constraints, including latency, cost, reliability, and safety.
- Collaborate with engineering, product, and research teams.
Hiring process
- Complete three, or no more than four, interviews if there is a potential fit.
- Interviews are conducted through virtual meetings and/or onsite meetings.
- Applications are evaluated by technical team members, with decisions made promptly.
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