Machine Learning Architect (LLMs)

✨ About Neuroscale.ai (arbiAI Team):

Neuroscale AI is reshaping talent screening, decision-making, and acquisition with cutting edge generative agentic AI. Our MVP has shown strong promise and we are ready to scale. We are currently seeking a qualified Machine Learning Architect to spearhead the evolution of our ML engine, ensuring that it remains innovative, efficient, and scalable. If you are passionate about leveraging AI to driven meaningful change in HR tech space and beyond, we want to hear from you.

📌 Role Details:

As an ML Architect, you will drive the design and development of our core AI models, focusing on optimizing our talent screening and acquisition software and evolution of core platform to new use cases. You’ll play a pivotal role in both evolving our generative AI model capabilities and setting the technical strategy to ensure scalability and accuracy as we grow. This is a high-impact role that combines hands-on model and software development/integration, with strategic planning.

This role focuses on leveraging cutting-edge AI technologies to design, develop, productize, deploy and evolve state-of-the-art machine learning solutions that leverage large language models (LLMs) across various platforms. The ideal candidate has a deep LLM/NLP background and experience in iterating quickly in a startup environment and is skilled in the art of translating proof of concept to proof of value, striking an optimal balance between time-to-market urgency and crafting architecturally elegant solutions. You embrace new generative AI/ML technologies and take charge of solving any challenges we will face. You will have complete, end-to-end ownership of our core models, working closely with our data engineers and iterating based on our customers’ feedback. Depending on your interest, this role can be an individual contributor or have a team built around it.

🌐 Responsibilities:

• Architect and Design: Define and evolve the ML architecture that aligns with the company’s goals, ensuring model scalability, efficiency, and security in production environments. Define technical requirements, solution design, and deployment pipelines for the AI systems. Own the architecture and development of the working product/models and beyond.
• Model Development: Develop, refine, and optimize generative models (e.g., LLMs, transformers) to ensure high-performance talent screening and candidate matching capabilities, and additional use cases. Oversee creation, training, and optimization of ML models and other advanced techniques in NLP. Strategize and articulate tradeoffs between speed, accuracy, and scalability. Develop and fine-tune LLMs for various applications and use-cases across multiple domains.
• Technical Strategy: Collaborate with product and engineering teams to translate business needs into actionable ML roadmaps; prioritize model improvements, integrations, and scalability.
• Data Pipeline and Workflow Design: Build and maintain robust data pipelines and model training workflows, ensuring data quality, version control, and experiment tracking.
• Production Readiness: Lead efforts to move ML models into production, including model monitoring, drift detection, and retraining workflows to maintain model relevance and accuracy.
• Model Evaluation: Implement rigorous performance metrics and A/B testing frameworks to assess and improve model effectiveness and bias reduction.
• Program Management: Be the technical spokesperson for the team and product and provide weekly updates and progress report to the executive team, ensuring engineering development alignment with the business goals and objectives, and recommendations for solving the prioritized challenges.
• Innovation: Stay current on emerging AI/ML research and leverage such knowledge to improve our models and solutions.

🗝️ Skills and Qualifications:

• 5 years in machine learning roles, with at least 3 years in a lead/architect role; experience in startups or fast-growing tech environments preferred.
• Strong expertise in generative models, large language models, and fine-tuning techniques.
• Expert level proficiency in Python and frameworks like TensorFlow, PyTorch, and Hugging Face.
• Solid understanding of containerization, and deployment (Docker, Kubernetes, MLOps, LLMOps, version control, etc.)
• Experience with cloud ML services and distributed training workflows.
• Experience with development of hybrid on-premises and cloud solutions.
• Proficiency in natural language processing (NLP) and domain-specific adaptation techniques for generative models.
• Proficiency in data engineering, data quality, and preprocessing techniques relevant to NLP.
• Strong problem-solving abilities, collaborative mindset, and excellent communication skills to work effectively with cross-functional teams.

💎 Preferred Experience & Education:

• Prior experience in HR tech or talent acquisition platforms.
• Familiarity with generative AI interpretability, bias mitigation, and responsible AI practices.
• Knowledge of data privacy and compliance regulations (e.g., GDPR, CCPA) relevant to talent screening solutions.
• Certification in AI/ML systems and cloud platforms.
• Bachelor’s or Master’s in Computer Science, Machine Learning, or related field. A PhD or relevant research experience is a plus.
• Experience in high-impact domains such as document and data processing (e.g. RFI/RFP/…)
• Publications or patents in AI/ML field, particularly in HRTech or Workflow Automation.

✨ Role Benefits & Outlook:

• Competitive Salary: Reflecting experience and expertise. Quarterly Bonuses (performance based)
• Equity Options: (Will be awarded when we go for the capital raise): Become a core contributor and stakeholder in the company’s success.
• Flexible Work Environment: Preferred location for this role is in Sterling, VA or Reston, VA. Temporary remote option will only be considered for exceptionally talented individuals with a very strong fit to the role. Relocation assistance will be considered.
• Healthcare and PTO: Flexible Paid Time Off (PTO) & 50% health/vision/dental coverage
• Professional Growth: Opportunities to attend industry-leading conferences, workshops, and further education. Fully funded IT Certifications & Internal Education. Spearhead and drive product architecture evolution/vision, design, and implementation. Set own engineering and development team agenda, milestones, and deliverables. Full access to state-of-the-art AI/ML Lab including Nvidia, DGX, and Dell/HPE infrastructure.