Machine Learning Engineer

Digital Waffle; New York (NY); Presencial; USD 140 000 - 190 000; Tempo integral

Resumo da oferta

Digital Waffle in New York is hiring an ML Engineer to take models from notebook to production and keep them performant and affordable at scale. You will own training, serving, evaluation and the plumbing underneath, sitting at the intersection of ML and engineering.

The role requires strong Python and PyTorch experience, experience with Hugging Face Transformers, and hands-on model serving with GPUs. This position is in-office 5 days/week in New York.

Qualificações

• Proficient Python and PyTorch with production ML experience.
• Experience with Hugging Face Transformers and distributed training.
• Hands-on with model serving stacks (vLLM, Triton, TensorRT) and GPU optimization.
• Familiarity with experiment tracking (MLflow, Weights & Biases) and reproducibility.
• Cloud experience (AWS/GCP/Azure) and modern infra tooling.

Responsabilidades

• Train, fine-tune and serve models in production using Python and PyTorch.
• Build and maintain the inference stack: model serving (vLLM, Triton, custom), batching, caching, cost control.
• Stand up training infrastructure using Ray, Modal, SageMaker or similar.
• Work with vector databases (pgvector, Pinecone, Weaviate) for retrieval-heavy workloads.
• Build evaluation pipelines so model quality is measurable rather than anecdotal.
• Partner with product and AI engineers to turn research or OSS models into shipped features.
• Own GPU utilization, latency and inference cost as first-class concerns.
• Use AI tooling (Claude, Cursor and similar) across every part of your workflow.

Conhecimentos

Python

PyTorch

Hugging Face Transformers

Distributed training

Experiment tracking

Ferramentas

vLLM

Triton

TensorRT

Ray

Modal

SageMaker

Weights & Biases

MLflow

AWS

GCP

Azure

Descrição da oferta de emprego

A high-growth AI-native startup is hiring an ML Engineer in New York to take models from notebook to production and keep them performant, reliable and affordable at scale.

This role sits at the intersection of ML and engineering: strong Python, strong PyTorch, and the engineering maturity to turn models into real product surfaces. You’ll own training, serving, evaluation and the plumbing underneath.

Location: New York, 5 days per week in office

What you’ll be doing:

• Train, fine-tune and serve models in production using Python and PyTorch
• Build and maintain the inference stack: model serving (vLLM, Triton, custom), batching, caching, cost control
• Stand up training infrastructure using Ray, Modal, SageMaker or similar
• Work with vector databases (pgvector, Pinecone, Weaviate) for retrieval-heavy workloads
• Build evaluation pipelines so model quality is measurable rather than anecdotal
• Partner with product and AI engineers to turn research or OSS models into shipped features
• Own GPU utilisation, latency and inference cost as first-class concerns
• Use AI tooling (Claude, Cursor and similar) across every part of your workflow

What you’ll need:

• Strong Python and PyTorch experience with real production ML systems behind you
• Experience with Hugging Face Transformers, modern training workflows and distributed training
• Hands-on with model serving stacks (vLLM, TensorRT, Triton or equivalent) and GPU-level optimisation
• Comfort with experiment tracking (MLflow, Weights & Biases) and reproducibility hygiene
• Cloud experience (AWS, GCP or Azure) and modern infra tooling
• Fluent in modern AI engineering tools and able to use them to raise output
• Happy working 5 days a week in-person with a small, high-intensity team

Nice to have:

• Experience fine-tuning or post-training LLMs (SFT, DPO, RLHF)
• Low-latency inference work or custom CUDA kernels

This role would suit an ML engineer who treats the inference bill, the latency graph and the eval harness as part of their job, not someone else’s.