Machine Learning Engineer

Skale; New York (NY); On-site; USD 120,000 - 160,000; Full time

Job summary

Skale is seeking a Machine Learning Engineer in New York to design, fine-tune, and evaluate large-scale transformer models. The role involves leading end-to-end ML pipelines and collaborating with product teams to deliver real-world solutions. Candidates should have 3+ years of hands-on experience with transformer models, strong Python proficiency, and deep familiarity with the Hugging Face ecosystem. This position offers a chance to impact the next generation of intelligent systems.

Qualifications

• 3+ years of hands-on experience with transformer-based models in NLP, CV, or multi-modal settings.
• Strong proficiency in Python; deep familiarity with PyTorch and the Hugging Face ecosystem.
• Solid understanding of attention mechanisms, positional encodings, and training dynamics.

Responsibilities

• Design, fine-tune, and evaluate large-scale transformer architectures (BERT, GPT, T5, and beyond).
• Lead end-to-end ML pipelines: data curation, training, optimisation, and serving.
• Apply techniques such as LoRA, RLHF, and quantisation to improve model efficiency and alignment.
• Collaborate with product and infrastructure teams to ship models that solve real-world problems.
• Stay current with research; translate papers into practical, production-ready implementations.

Skills

Transformer-based models

Python proficiency

PyTorch

Attention mechanisms

Model deployment

Tools

Hugging Face

ONNX

TorchServe

Job description

We're building intelligent systems that push the boundaries of what's possible with language and sequence modelling. As our new ML Engineer, you'll own the full lifecycle — from research and experimentation to production deployment — of transformer-based models at scale.

What you'll do

• Design, fine-tune, and evaluate large-scale transformer architectures (BERT, GPT, T5, and beyond)
• Lead end-to-end ML pipelines: data curation, training, optimisation, and serving
• Apply techniques such as LoRA, RLHF, and quantisation to improve model efficiency and alignment
• Collaborate with product and infrastructure teams to ship models that solve real-world problems
• Stay current with research; translate papers into practical, production-ready implementations

What we're looking for

• 3+ years of hands‑on experience with transformer-based models in NLP, CV, or multi‑modal settings
• Strong proficiency in Python; deep familiarity with PyTorch and the Hugging Face ecosystem
• Solid understanding of attention mechanisms, positional encodings, and training dynamics
• Experience deploying models to production (e.g. via ONNX, TorchServe, vLLM, or similar)
• Bonus: publications, open‑source contributions, or experience with distributed training (e.g. DeepSpeed, FSDP)