Senior Machine Learning Engineer

webAI; Ashington; Remoto; GBP 113.000 - 173.000; Tempo pieno

Vantaggi offerti da questo lavoro

Equity options

Descrizione del lavoro

webAI seeks a Senior Machine Learning Engineer to build production-ready AI systems for public sector initiatives, spanning secure government cloud infrastructure to edge devices in restricted environments.

You will transform prototype models into scalable, reliable production systems, design agentic workflows for multi-step reasoning, and productionize models for real-world applications across cloud, edge, and mobile hardware.

Competenze

• Experience with multi-modal models and synthetic data generation methods.
• Proficiency in PyTorch, TensorFlow, or Hugging Face Transformers.
• Active US Security clearance.
• Experience deploying AI models across cloud, edge, and mobile hardware.
• 4+ years of experience in applied AI, ML engineering, or production AI systems.
• Experience building RAG pipelines and integrating vector databases.

Mansioni

• Transform prototype models into scalable, production systems.
• Design, develop, and deploy agentic workflows for multi-step reasoning, tool use, and decisions.
• Productionize AI models for real-world applications across cloud, edge, and mobile environments.
• Engineer adaptive ML systems with on-device inference and hardware acceleration.
• Build and maintain RAG pipelines with vector databases for contextual retrieval.

Conoscenze

Multi-modal ML

Distributed systems

Edge AI

Technical documentation

Public sector awareness

Strumenti

PyTorch

TensorFlow

Hugging Face Transformers

FAISS

Milvus

ChromaDB

Pinecone

Quadrant

Descrizione del lavoro

• We are seeking a Senior Machine Learning Engineer to support our Public Sector initiatives focused on building and optimizing production ready AI systems for secure and distributed environments


• You will be responsible for transforming prototype models into scalable, efficient, and reliable production systems that operate seamlessly across a spectrum of hardware from government cloud infrastructure to edge devices in restricted or disconnected environments


• Design, develop, and deploy agentic workflows to orchestrate multi-step reasoning, tool use, and decision-making across production systems


• Productionize AI models from research prototypes into scalable, deployable systems used in real world applications


• Engineer adaptive ML systems using LoRA, PEFT, and on-device inference strategies, leveraging PyTorch, TensorFlow, and Hugging Face Transformers for model development, fine-tuning, and optimization


• Implement model optimization techniques such as quantization, pruning, distillation, and hardware specific acceleration


• Build and maintain Retrieval Augmented Generation (RAG) pipelines, including vector database integration for contextual retrieval


• Work with multi-modal AI systems across computer vision, audio, and natural language domains


• Optimize model execution for distributed and resource constrained environments, ensuring reliability under variable connectivity conditions

Benefits

• Grow with us: Equity options so you share in our growth. Build your future as we build ours.


• Family first: Parental leave and fertility support for every stage of life. Your family matters here.


• Plan your future: A 401(k) to help you save and invest. Secure what's ahead with confidence.


• Stay well: Comprehensive health coverage for body and mind. Because balance fuels great work.


• Keep learning: A stipend to explore, study, and sharpen your skills. Growth never stops.


• Work anywhere: Remote-first with flexible time off to recharge. Find balance wherever you are.

Requirements

• Familiarity with multi-modal models and synthetic data generation methods


• Deep proficiency in PyTorch, TensorFlow, or Hugging Face Transformers


• Active US Security clearance


• Strong algorithmic and problem solving skills, especially in distributed or constrained compute environments


• Proven experience deploying AI models across cloud, edge, and mobile hardware environments


• 4+ years of experience in applied AI, ML engineering, or production AI systems


• Expertise in model compression and optimization (quantization, pruning, distillation)


• Experience building RAG pipelines and integrating vector databases (e.g., Quadrant, ChromaDB, FAISS, Milvus, Pinecone)


• Ownership - Taking full responsibility for one's actions and decisions, demonstrating commitment to the success of our clients


• Truth - Emphasizing transparency and honesty in every interaction and decision


• Humility - Maintaining a respectful and learning-oriented mindset, acknowledging the strengths and contributions of others


• Tenacity - Persisting in the face of challenges and setbacks, continually striving for excellence and improvement


• We seek individuals who exemplify the following:


• Strong understanding of GPU computing, CUDA, and performance profiling


• Excellent communication and technical documentation skills for collaboration across multi disciplinary teams


• Understanding of AI governance and compliance frameworks relevant to public sector deployments


• Experience with edge AI, federated learning, or offline inference systems


• Experience integrating models into large scale distributed systems or microservice architectures