AIML Engineer Model Dev & Data Pipeline

Seattle, OR - USA

Hourly Salary: $ 65 - 75

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Department:

Engineering

Job Summary

Description

AI/ML Engineer - Model Dev & Data Pipeline

Type:Temporary contract role with the potential to become a full-time position.

Source:Direct individual contract only; agencies firm uplift rates not accepted

Location: Seattle WA or San Francisco CA
Contract/Hourly: DOE
Experience: 7 years

Dates of Engagement: Oct 1 to Dec 15 2025
Hours Per Week Available: 25hrs(min) > to 35hrs(max) per week

Start Date: ASAP
Rate: Projected: $65-75/hour

About The Role

Join ourclientsAI/ML team to build and deploystate-of-the-artmachine learning models that power intelligent experiences for millions of users.Youllwork on everything from training custom models tooptimizinginference pipelines while collaborating with world-class researchers and engineers.

What Youll Build

Model Development & Research

Design and train custom neural networks for human-centered AI usingPyTorch/TensorFlow

Fine-tune and adapt large language models (Llama Claude GPT) for domain-specific tasks

Implement novel architectures from recent papers (attention mechanisms retrieval augmentation)

Conduct rigorous A/B testing and evaluation of model performance in production

Production ML Systems

Build andmaintainscalable ML pipelines processing 10M daily inferences

Implement real-time model serving with <100ms p95 latency requirements

Design andoptimizevector similarity search systems for multi-modal embeddings

Create robust data ingestion and feature engineering pipelines

MLOps& Infrastructure

EstablishMLflow/Weights & Biases workflows for experiment tracking and model versioning

Build automated training and deployment pipelines using Kubernetes and Docker

Implement model monitoring drift detection and automated retraining systems

OptimizeGPUutilizationand cost efficiency for training and inference workloads

Requirements

Technical Expertise

7 years of hands-on ML experience with production model deployment

Expert-levelproficiencyin Python and ML frameworks (PyTorch/TensorFlow/JAX)

Deep understanding of transformer architectures attention mechanisms and modern NLP

Experience with large-scale distributed training (multi-GPU model/data parallelism)

Strong background in statistics linear algebra and optimization theory

Production ML Skills

Experience withMLOpstools:MLflow Weights & Biases Kubeflow or similar platforms

Proficiencywith cloud ML services (AWS SageMaker GCP Vertex AI Azure ML)

Docker and Kubernetes experience for containerized ML workloads

Knowledge of model serving frameworks (TorchServe TensorFlow ServingTritonServer)

AI/LLM Specialization

Hands-on experience with LLM fine-tuning RLHF and prompt engineering

Understanding of retrieval-augmented generation (RAG) and vector databases

Experience with multimodal models (vision-language audio processing)

Knowledge of model compression techniques (quantization distillation pruning)

Preferred Experience

PhD in ML/AI Computer Science or equivalent industry experience

Publications in top-tier conferences (NeurIPS ICML ICLR EMNLP)

Experience at AI-first companies or research labs (OpenAI Anthropic DeepMind etc.)

Contributions to open-source ML projects with significant community adoption

Experience with edge deployment and mobile ML optimization

Tech Stack

ML Frameworks:PyTorch Transformers (Hugging Face) JAX TensorFlow

Data & Compute:RayDask Apache Spark CUDA Triton

MLOps:MLflow Weights & Biases DVC Feast Great Expectations

Infrastructure:Kubernetes Docker AWS/GCP Terraform

Databases:PostgreSQL Redis PineconeWeaviateClickHouse

If this sounds like you send your resume for immediate consideration toemailprotected

DescriptionAI/ML Engineer - Model Dev & Data PipelineType:Temporary contract role with the potential to become a full-time position.Source:Direct individual contract only; agencies firm uplift rates not acceptedLocation: Seattle WA or San Francisco CAContract/Hourly: DOEExperience: 7 yearsDates of E...