About Us

Qognitive is pioneering a novel machine learning paradigm inspired by quantum mechanics and quantum cognition. Our proprietary Quantum Cognition Machine Learning (QCML) technology addresses the limitations of traditional models by emulating human cognitive processes. Designed to operate efficiently on classical hardware, QCML delivers superior performance across various applications, including finance, genomics, material science, and time series forecasting. Our approach enables data inference with a vast number of inputs, offering a versatile solution for complex, real-world challenges.

As we scale, we seek a Director of Engineering to lead and grow our engineering team, build high-performance infrastructure, and support the transition from research-driven POCs to scalable, revenue-generating ML products.

Role Summary

As the Director of Engineering, you will be responsible for building and leading our engineering team, focusing on infrastructure, high-performance computing, and tooling to support cutting-edge machine learning research and model deployment. You will work closely with the Director of Research, the Director of Applied Engineering & Client Solutions and the leadership team to ensure that our technical roadmap aligns with the company's strategic vision.

This role requires a deep understanding of machine learning infrastructure, distributed computing, and performance optimization, with a strong ability to scale engineering efforts while maintaining a high level of technical rigor. You will also be responsible for maintaining and promoting best practices in software engineering, DevOps, and ML model deployment.

Key Responsibilities

1. Engineering Leadership & Team Growth

• Foster a culture of technical excellence, collaboration, and innovation within the engineering team.
• Define career growth paths for engineers, ensuring skill development and knowledge sharing across the organization.
• Hire, mentor, and lead a high-caliber engineering team, Applied ML engineers, including high-performance computing (HPC) engineers, and infrastructure specialists.

2. Infrastructure & Performance Optimization

• Oversee the design and implementation of the high-performance computing infrastructure supporting machine learning research and deployment.
• Ensure optimal use of GPU acceleration (CUDA), distributed computing, and parallel processing for large-scale model training.
• Build robust ML tooling and automation pipelines that enable researchers and applied ML engineers to efficiently experiment, test, and deploy models.

3. Cross-Functional Collaboration

• Collaborate closely with the Director of Research to translate cutting-edge research insights into scalable, production-ready engineering solutions.
• Work in tandem with the Director of Applied Engineering and Client Solutions to ensure that our infrastructure and tooling seamlessly support deployment into client environments, including onboarding workflows, integration pipelines, and long-term support.
• Align with business and product teams to ensure engineering efforts are tightly connected to productization and go-to-market strategies, serving as a key technical bridge across research, engineering, and client-facing operations.

4. Scalability & DevOps

• Implement best practices in CI/CD, version control, and infrastructure automation to support efficient ML experimentation and deployment.
• Ensure that engineering efforts are scalable, modular, and well-documented to support long-term growth.
• Develop and oversee monitoring and reliability systems for ML infrastructure.

5. Technical Strategy & Vision

• Define and own the engineering roadmap, ensuring alignment with company goals and research advancements.
• Identify and evaluate emerging technologies that can enhance our ML architecture, compute efficiency, and infrastructure scalability.

Qualifications

Required

• 10+ years of experience in software engineering, machine learning infrastructure, or high-performance computing, with at least 3-5 years in a leadership role.
• Prior experience scaling an ML-first startup to production-grade systems.
• Deep knowledge of ML infrastructure: experience in building scalable model training and deployment pipelines.
• Proficiency in Python, C++, and distributed computing frameworks (Ray, Dask, Spark, MPI, etc.).
• Experience with ML frameworks (PyTorch, TensorFlow, JAX) and MLOps tools (MLflow, Weights & Biases, Airflow, etc.).
• Strong understanding of DevOps principles, including CI/CD, Kubernetes, and containerization (Docker).
• Proven track record of leading, mentoring, and scaling engineering teams.
• Excellent communication and leadership skills, with experience working cross-functionally between research, engineering, and business teams.

Preferred

• Understanding of Quantum mechanics or complex linear algebra and statistics with a strong interest in exploring their applications to machine learning and real-world problem-solving.
• Familiarity with finance, healthcare, or chemistry-related ML applications.
• Expertise in high-performance computing (HPC), including CUDA, GPU programming, and parallel computing architectures.

What We Offer

• Competitive salary and benefits package.
• The chance to work on a novel ML paradigm and contribute to cutting-edge solutions in multiple high-impact verticals.
• A collaborative startup environment with significant room for professional growth and cross-functional learning.
• Bonus & Equity: Eligible for performance-based bonuses and stock incentives

How to Apply

If you are excited about applying machine learning solutions to real-world problems and bridging the gap between research innovation and product delivery, please send your resume/CV and a brief cover letter to careers@qognitive.io. We look forward to exploring how you can contribute to our vision.

The reasonably estimated yearly salary for this role at Qognitive is: $250,000—$300,000 USD (plus Stock Options and annual performance bonus)