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Lead Machine Learning Engineer

NobleAIHouston, TX

$190,000 - $205,000 / year

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Overview

Schedule
Full-time
Career level
Senior-level
Remote
Remote
Compensation
$190,000-$205,000/year
Benefits
Health Insurance
Dental Insurance
Vision Insurance

Job Description

At NobleAI, we believe that energy, material science and chemistry are key to building a sustainable world and that artificial intelligence is essential to unlock this potential. NobleAI leverages innovative Science-Based AI technology to revolutionize energy workflows, materials development, and chemical designs. We enable companies to accelerate innovation and reduce costs in developing sustainable technologies and products. 

We're a team of excellence-driven individuals who value thoughtfulness and respect while focusing on delivering products that empower engineers and researchers to create better solutions faster.

At NobleAI, we are developing the next generation of intelligent chemical informatics platform. Our goal is to create a seamless, intuitive experience that empowers users to achieve unprecedented productivity in data processing, visualization, and model building. We are seeking a forward-thinking team member who thrives on innovation, collaboration, and rapid iteration, and has the ability to solve challenging problems in science and technology. 

As a Lead Machine Learning Engineer specializing in conversational and agentic systems at NobleAI, you will be responsible for architecting, building, and deploying intelligent features to our VIP platform. This role is ideal for individuals passionate about the cutting edge of LLMs and eager to build AI systems that can reason, plan, and act. 

Join us in building a more sustainable world through the power of AI and scientific innovation.

Requirements

  • Design domain specific AI systems and chatbots capable of complex dialogue management and workflow execution via tools, API calls and multi step tasks based on user goals, multi-agent orchestration.
  • Collaborate with scientists to assess, fine-tune, and deploy LLMs on domain specific data to support accuracy measurement for use cases
  • Build and maintain Retrieval-Augmented-Generation (RAG) systems, Reinforcement Learning frameworks, guardrail and assessment mechanisms for end to end lifecycle for customized models.
  • Collaborate with product and software engineers to integrate the features into our platform.
  • Establish prompt engineering and data management best practices for transparency and governance.
  • Establish best practices for monitoring and evaluation of data and models across the model lifecycle (development, testing, and production)
  • Keep a pulse on the latest advancements in NLP, LLMs, and agentic AI research, and act as a subject matter expert on architecture decisions on platform and use cases.

What We’re Looking For

  • MSc (preferred) or BSc in Computer Science, Artificial Intelligence, or a related quantitative field.
  • 5+ years of hands-on experience building and deploying AI/ML systems, with a strong focus on Natural Language Processing (NLP).
  • Proven experience designing and shipping chatbots, virtual assistants, or agentic systems using modern LLM-based architectures.
  • Strong programming proficiency in Python (5+ years) and deep experience with core ML/NLP libraries such as PyTorch, TensorFlow
  • Hands-on experience with LLM agent frameworks for building complex, tool-using applications, multi-agent orchestration, or establishing MCP services for platform capabilities.
  • Demonstrated experience with Retrieval-Augmented Generation (RAG), including the use of vector databases like Pinecone, Weaviate, or ChromaDB.
  • Familiarity with techniques for fine-tuning LLMs (e.g., LoRA/QLoRA) and experience working with open-source models (e.g., Llama, Mistral) or major model APIs (e.g., OpenAI, Anthropic).
  • 5+ years of experience with cloud platforms (Azure preferred) and familiarity with deploying AI models as scalable microservices using Docker and Kubernetes (KFP, KServe).
  • Solid software engineering fundamentals, including version control (Git), automated testing, and CI/CD principles.
  • Excellent communication skills with the ability to articulate complex technical ideas to both technical and non-technical stakeholders.

Benefits

We offer great pay & benefits. 

  • Top-tier health benefits coverage, including medical, dental, vision, disability and life insurance
  • Flexible paid time off & generous holidays
  • Remote-first with co-working access at Industrious offices
  • 401(k) with employer match 
  • Equity package
  • Salary Range $190,000 - $205,000 (Depending on experience & Geographic location) 
  • Performance-based bonus plan*

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FAQs About Lead Machine Learning Engineer Jobs at NobleAI

What is the work location for this position at NobleAI?
This job at NobleAI is located in Houston, TX, according to the details provided by the employer. Some roles may also include multiple work locations depending on the requirement.
What pay range can candidates expect for this role at NobleAI?
Candidates can expect a pay range of $190,000 and $205,000 per year.
What employment applies to this position at NobleAI?
NobleAI lists this role as a Full-time position.
What experience level is required for this role at NobleAI?
NobleAI is looking for a candidate with "Senior-level" experience level.
Does NobleAI allow remote work for this role?
Yes, this position at NobleAI supports remote work, giving candidates the flexibility to work outside the primary office location.
What benefits are offered by NobleAI for this role?
NobleAI offers following benefits: Health Insurance, Dental Insurance, Vision Insurance, Disability Insurance, Life Insurance, Paid Holidays, Flexible/Unlimited PTO, 401k Matching/Retirement Savings, and Health & Wellness Programs for this position. Actual benefits may vary depending on the employer's policies and employment terms.
What is the process to apply for this position at NobleAI?
You can apply for this role at NobleAI either through Sonara's automated application system, which helps you submit applications 10X faster with minimal effort, or by applying manually using the direct link on the job page.