AVOMIND

AVOMIND is a global recruitment firm based in Berlin, specializing in hiring commercial, strategy, and analytics & insights talent for companies and candidates worldwide. With a focus on executive search and embedded recruitment, AVOMIND connects high-...

Professional Services

Industrials

11-50 (30)

Founded 2019

99 open positions

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Principal Machine Learning Engineer

1 month ago

Singapore

Full-time

Lead

Machine Learning Engineer

Software Development

Apache Spark Deep Learning Machine Learning PyTorch

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AVOMIND

Professional Services

11-50

Founded 2019

View All Jobs 99

Description

Build and own end-to-end machine learning pipelines from data processing through training, evaluation, inference, and deployment.
Fine-tune and adapt models using modern techniques such as LoRA, QLoRA, SFT, DPO, and model distillation.
Design and operate scalable inference systems while balancing latency, cost, and reliability.
Develop and maintain data pipelines for synthetic and real-world training datasets.
Build evaluation frameworks to measure model performance, robustness, safety, and bias.
Optimize production deployments through GPU efficiency, memory usage, latency reduction, and scaling strategies.
Collaborate with application engineering teams to integrate ML systems into backend, mobile, and desktop applications.
Monitor production ML systems and iterate quickly based on real-world performance and constraints.

Requirements

Strong background in deep learning and transformer-based architectures.
Hands-on experience training, fine-tuning, or deploying large-scale machine learning models in production.
Proficiency with machine learning frameworks such as PyTorch or JAX.
Experience with distributed training and inference frameworks such as DeepSpeed, FSDP, Megatron, ZeRO, or Ray.
Strong software engineering skills and experience building robust, maintainable, production-grade systems.
Experience optimizing GPU workloads, including memory efficiency, quantization, and mixed precision.
Ability to independently own end-to-end machine learning systems in fast-moving environments.
Strong problem-solving skills with a focus on rapid iteration and continuous improvement.
Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer (preferred).
Open-source contributions to machine learning or systems libraries (preferred).
Scientific computing, compiler technologies, or GPU kernel development (preferred).
Experience with RLHF pipelines, including PPO, DPO, or ORPO (preferred).
Experience training or deploying multimodal or diffusion models (preferred).
Experience with large-scale data processing frameworks such as Apache Arrow, Spark, or Ray (preferred).

Interested in this position?

Apply directly on the company website

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