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Senior Machine Learning Engineer

Austin, TX · Washington D.C. Area · Austin, Texas, United States

Pay
Pay not listedSimilar roles state $165K–$240K
Work setting
Not stated
Employment
Full-time — employment source
Special Notice: This position is NOT contingent upon awarding of a project or needing a funding source. This is full-time employment with webAI. About the Role:
Employment type: Full-time
From the employer’s posting
Team
Public Sector — team source
Department: Public Sector
From the employer’s posting

Before you apply

Sponsorship
Visa sponsorship not confirmed — sponsorship source
Sponsorship Do you now, or will you in the future, require sponsorship for employment (e.g., H-1B visa, work permit, etc.) in the United States?
From the employer’s application form
Apply at Webai

What you’ll work on

Full posting

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.

  • Turn research prototype models into scalable, deployable production systems

  • Design and deploy agentic workflows for multi-step reasoning and tool use

  • Build and maintain RAG pipelines with vector database integration

  • Apply quantization, pruning, distillation and hardware acceleration to models

  • Optimize models for resource-constrained environments with variable connectivity

From the employer’s posting
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.
Productionize AI models from research prototypes into scalable, deployable systems
Design, develop, and deploy agentic workflows to orchestrate multi-step reasoning, tool use, and decision-making
Build and maintain Retrieval Augmented Generation (RAG) pipelines, including vector database integration for contextual retrieval
Implement model optimization techniques such as quantization, pruning, distillation, and hardware specific acceleration
Optimize model execution for distributed and resource constrained environments, ensuring reliability under variable connectivity conditions

What you’ll bring

All qualifications

Core experience

  • 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).
  • Experience building RAG pipelines and integrating vector databases (e.g., Quadrant, ChromaDB, FAISS, Milvus, Pinecone).
  • Familiarity with multi-modal models and synthetic data generation methods.

Preferred experience

  • 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.
Qualification wording
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).
Experience building RAG pipelines and integrating vector databases (e.g., Quadrant, ChromaDB, FAISS, Milvus, Pinecone).
Familiarity with multi-modal models and synthetic data generation methods.
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.

Tools in this posting

  • Hugging Face
  • PyTorch
  • TensorFlow
  • Transformers
Source — Tool mentions in context
- 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.
- 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.

Benefits in the posting

Full benefits wording
  • 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.
  • Competitive salary and performance-based incentives
  • Comprehensive health, dental, and vision benefits package
  • Including a high-deductible HSA option with company contribution
  • 401k match (US-based only)
  • Flexible PTO Policy
  • Parental leave for eligible employees
  • Continuing education support
  • Basic, company-paid life insurance
  • Supplemental life insurance
  • Company-paid short and long term disability
  • Free parking for in-office employees

From the employer’s posting.

Posting record

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Open 4 monthsWe first saw this posting 149 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

Status in our records
Live
First seen by us
Jun 2, 2026 · 149 days ago
Recorded sightings
85
Last seen by us
Oct 30, 2026
Employer says posted
Apr 16, 2026

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Job description

View original posting ↗

Special Notice:

This position is NOT contingent upon awarding of a project or needing a funding source. This is full-time employment with webAI.

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.

  • 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.

Qualifications:

  • Active US Security clearance

  • 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).

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

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

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

Preferred 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.

We at webAI are committed to living out the core values we have put in place as the foundation on which we operate as a team. We seek individuals who exemplify the following:

  • Truth - 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.

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.

  • Competitive salary and performance-based incentives

  • Comprehensive health, dental, and vision benefits package

    • Including a high-deductible HSA option with company contribution

  • 401k match (US-based only)

  • Flexible PTO Policy

  • Parental leave for eligible employees

  • Continuing education support

  • Basic, company-paid life insurance

  • Supplemental life insurance

  • Company-paid short and long term disability

  • Free parking for in-office employees


webAI is an Equal Opportunity Employer and does not discriminate against any employee or applicant on the basis of age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We adhere to these principles in all aspects of employment, including recruitment, hiring, training, compensation, promotion, benefits, social and recreational programs, and discipline. In addition, it is the policy of webAI to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works.

Source details

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Pay

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Location & working pattern

Austin, Texas, United States

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Work authorization
Qualifications: - Active US Security clearance - 4+ years of experience in applied AI, ML engineering, or production AI systems.

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