Head of AI

As Head of AI, you will be the primary technical driver of all AI/ML initiatives. You’ll report directly to the CEO/CTO and own the full lifecycle of our AI roadmap—from research and proof-of-concept to scalable production. We’re looking for a “doer” who can rapidly prototype models, optimize for performance, and mentor junior engineers, all while helping define product strategy.

In this role, you will:

• Lead AI strategy and execution in a high-ambiguity environment.
• Build, train, and deploy state-of-the-art models (e.g., deep learning, NLP, computer vision, reinforcement learning, or relevant domain-specific architectures).
• Design infrastructure for data ingestion, annotation, experimentation, model versioning, and monitoring.
• Collaborate closely with product, design, and DevOps to integrate AI features into our platform.
• Continuously evaluate new research, open-source tools, and emerging frameworks to keep us at the forefront.
• Recruit, mentor, and grow an AI/ML team as we scale beyond our seed round.

Key Responsibilities

• Define and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference.
• Rapidly prototype novel models (e.g., neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent.
• Productionize models in cloud/on-prem environments (AWS/GCP/Azure) with containerization (Docker/Kubernetes) and ensure low-latency, high-availability inference.
• Develop a multi-quarter AI roadmap aligned with product milestones and fundraising milestones.
• Identify and evaluate opportunities for AI-driven competitive advantages (e.g., proprietary data, unique model architectures, transfer/few-shot learning).
• Collaborate with business stakeholders to translate “big problems” into technically feasible AI solutions.
• Oversee the creation and maintenance of scalable data pipelines (ETL/ELT) and data lakes/warehouses.
• Establish best practices for data labeling, versioning, and governance to ensure high data quality.
• Implement ML Ops processes: CI/CD for model training, automated testing, model–drift detection, and continuous monitoring.
• Hire and mentor AI/ML engineers, data scientists, and research interns.
• Set coding standards, model-development guidelines, and rigor around reproducible experiments (e.g., clear Git workflow, experiment tracking).
• Conduct regular code/model reviews and foster a culture of “learn by doing” and iterative improvement.
• Stay abreast of state-of-the-art AI research (e.g., pre-training, fine-tuning, generative methods) and evaluate applicability.
• Publish or present whitepapers/prototype demos if appropriate (keeping stealth constraints in mind).
• Forge partnerships with academic labs or open-source communities to accelerate innovation.

Minimum Qualifications

• Experience (7 + years total; 3 + years in senior/lead role): Demonstrated track record of shipping AI/ML products end-to-end (from prototype to production).
• Hands-on expertise building and deploying deep learning models (e.g., CNNs, Transformers, graph neural networks) in real-world applications.
• Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.).
• Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git).
• Experience designing scalable ML infrastructure on cloud platforms (AWS SageMaker, GCP AI Platform, Azure ML, or equivalent).
• Solid understanding of data-engineering concepts: SQL/noSQL, data pipelines (Airflow, Prefect, or similar), and batch/streaming frameworks (Spark, Kafka).
• Proven ability to lead cross-functional teams in ambiguous startup settings.
• Exceptional written and verbal communication skills—able to explain complex concepts to both technical and non-technical stakeholders.
• Experience recruiting and mentoring engineers or data scientists in a fast-paced environment.
• Bachelor’s or Master’s in Computer Science, AI/ML, Electrical Engineering, Statistics, or a related field. (Ph.D. in AI/ML is a plus but not required if hands-on experience is extensive.)

Preferred (Nice-to-Have)

• Prior experience in a stealth-mode or early-stage startup, ideally taking an AI product from 0 → 1.
• Background in a relevant domain (e.g., healthcare AI, autonomous systems, finance, robotics, computer vision, or NLP).
• Hands-on experience with large-scale language models (LLMs) and prompt engineering (e.g., GPT, BERT, T5 family).
• Familiarity with on-device or edge-AI deployments (e.g., TensorFlow Lite, ONNX, mobile/embedded inference).
• Knowledge of MLOps tooling (MLflow, Weights & Biases, Kubeflow, or similar) for experiment tracking and model governance.
• Open-source contributions or published papers in top-tier AI/ML conferences (NeurIPS, ICML, CVPR, ACL, etc.).

Soft Skills & Cultural Fit

• “Doer” Mindset: You thrive in scrappy, ambiguous environments. You’ll roll up your sleeves to write production code, prototype research ideas, and iterate quickly.
• Bias for Action: You favor shipping an MVP quickly, measuring impact, and iterating—over striving for perfect academic proofs that never see production.
• Ownership Mentality: You treat the startup as your own: you take responsibility for system uptime, data integrity, and feature adoption, not just model accuracy.
• Collaborative Attitude: You value cross-functional teamwork and can pivot between “researcher mode” and “software engineer mode” depending on the task at hand.
• Growth-Oriented: You continually learn new algorithms, architectures, and engineering best practices; you encourage team members to do the same.

What We Offer

• Equity Package: Meaningful ownership stake, commensurate with an early leadership role.
• Competitive Compensation: Salary aligned with early-stage startup benchmarks; a large portion of the upside is in equity.
• Autonomy & Impact: You’ll shape the technical direction of our AI stack and lay the groundwork for a market-leading product.
• Flexible Work Environment: Remote-friendly with occasional in-person retreats or team meetups.
• Learning Budget: Funds for conferences, courses, or publications to ensure you stay at the bleeding edge.
• Fast-Track Growth: As our first AI hire and eventual team leader, you’ll rapidly expand your responsibilities—and the team you build—within months.

How to Apply

Please send your resume/CV and a brief cover letter to jobs@stealth-ai.com with the subject line: Head of AI Application – [Your Name]

In Your Cover Letter, Highlight:

• A recent project where you built and deployed an AI/ML system end-to-end (include technical stack and impact).
• Any leadership or mentoring experience guiding other engineers or data scientists.
• Why you’re excited to join a stealth startup and move quickly from prototype to production.

We will review applications on a rolling basis and aim to schedule initial calls within two weeks of receipt.

Equal Opportunity

We are committed to building a diverse team and welcome applicants of all backgrounds. We celebrate differences and encourage individuals who thrive in a fast-paced, collaborative, and impact-driven culture to apply.

Ready to build world-class AI from day one? Come join us and help shape the future.