webAI

webAI

Senior Machine Learning Engineer

Austin, TX · Senior · Contract

No sponsorshipDetected 96 days ago
Distributed SystemsVector DatabasesCloud PlatformsMachine LearningTensorFlowPyTorchNLPComputer VisionRAGCybersecurityResearchCommunicationCollaborationProblem Solving

About the role

  • 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.

Responsibilities

  • Design, develop, and deploy agentic workflows to orchestrate multi-step reasoning, tool use, and decision-making across production systems.
  • 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.
  • Optimize model execution for distributed and resource constrained environments, ensuring reliability under variable connectivity conditions.

Requirements

  • 4+ years of experience in applied AI, ML engineering, or production AI systems.
  • Deep proficiency in PyTorch, TensorFlow, or Hugging Face Transformers.
  • Proven experience deploying AI models across cloud, edge, and mobile hardware environments.
  • Expertise in model compression and optimization (quantization, pruning, distillation).
  • Familiarity with multi-modal models and synthetic data generation methods.
  • Active US Security clearance
  • Experience building RAG pipelines and integrating vector databases (e.g., Quadrant, ChromaDB, FAISS, Milvus, Pinecone).
  • Strong algorithmic and problem solving skills, especially in distributed or constrained compute environments.

Nice to have

  • Preferred Skills:

Skills

  • Experience with edge AI, federated learning, or offline inference systems.
  • Understanding of AI governance and compliance frameworks relevant to public sector deployments.
  • Experience integrating models into large scale distributed systems or microservice architectures.
  • Excellent communication and technical documentation skills for collaboration across multi disciplinary teams.
  • Strong understanding of GPU computing, CUDA, and performance profiling.
  • Emphasizing transparency and honesty in every interaction and decision.
  • Ownership
  • Taking full responsibility for one's actions and decisions, demonstrating commitment to the success of our clients.
  • Tenacity
  • Persisting in the face of challenges and setbacks, continually striving for excellence and improvement.
  • Humility
  • Maintaining a respectful and learning-oriented mindset, acknowledging the strengths and contributions of others.

Compensation

  • Competitive salary

Benefits

  • We strive to provide competitive benefits to all employees.
  • The benefits listed in this posting generally apply to U.S.-based employees.
  • For employees hired outside the United States, benefits may vary based on local law, country-specific requirements, and the employment platform or entity through which the employee is hired.
  • Comprehensive health, dental, and vision benefits package
  • $200/month Health & Wellness stipend
  • Continuing Education support
  • $500/year Function Health subscription (U.S.-based employees only)
  • Flexible Time Off (FTO)
  • Parental leave for eligible employees
  • Supplemental life insurance
  • We adhere to these principles in all aspects of employment, including recruitment, hiring, training, compensation, promotion, benefits, social and recreational programs, and discipline.
  • Work with multi-modal AI systems across computer vision, audio, and natural language domains.

Visa & Work Authorization

  • Active US Security clearance

This listing is sourced directly from webAI's careers page and normalized into a canonical job model.