Machine Learning Engineer

About Qwerky AI

QWERKY AI is a human-centered artificial intelligence company focused on building practical and approachable AI tools for real-world use. Headquartered in Columbia, South Carolina, with a distributed team across the U.S., QWERKY is led by a founding team of tech entrepreneurs with over a decade of experience. The company is dedicated to creating AI that enhances — rather than replaces — human intelligence. QWERKY AI is currently developing an AI platform to empower knowledge workers, creatives and small businesses.

Job Description

QWERKY AI seeks an experienced and skilled Machine Learning Engineer (MLE) to join our dynamic Research & Development team. In this role, you will be responsible for designing, building, deploying, and maintaining robust and scalable machine learning systems that power QWERKY AI's innovative products. You will leverage your software engineering, MLOps, and machine learning expertise to translate cutting-edge models into production-ready solutions. The ideal candidate has a strong track record of operationalizing ML models, a passion for building efficient infrastructure, and a collaborative mindset to work effectively with data scientists and software engineers.

Responsibilities

• Design, develop, and maintain end-to-end machine learning pipelines, encompassing data ingestion, preprocessing, model training, evaluation, deployment, and monitoring.
• Contribute to the design and implement scalable and reliable infrastructure for serving machine learning models in production environments.
• Assist in the operationalization of machine learning models, ensuring they meet performance, scalability, and reliability requirements.
• Assist in the design and implementation of MLOps best practices, including model versioning, automated testing, CI/CD for ML, monitoring, and feedback loops.
• Help optimize machine learning models and inference code for low latency, high throughput, and efficient resource utilization.
• Collaborate with data scientists to understand model intricacies and help translate research prototypes into production-grade systems.
• Work with software engineering teams to integrate ML capabilities into broader applications and platforms.
• Troubleshoot and resolve complex issues in production ML systems, ensuring high availability and performance.
• Contribute to the design and evolution of our ML platform and tooling.
• Mentor intern or junior engineers and contribute to the team's technical growth and knowledge sharing.
• Stay current with the latest advancements in ML engineering, MLOps, and relevant cloud technologies.

Required Skills

• Bachelor's or Master's in Computer Science, Software Engineering, or a related technical field.
• Proven experience (typically 3- 5+ years) as a Machine Learning Engineer, Software Engineer working on ML systems, or in a similar role.
• Strong software engineering fundamentals, including proficiency in Python, data structures, algorithms, system design, and software testing.
• Hands-on experience designing, building, and deploying machine learning models into production environments.
• Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and understanding their operational characteristics.
• Proficiency with MLOps tools and practices (e.g., MLflow, Kubeflow, Docker, Kubernetes, CI/CD pipelines for ML).
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and their services for machine learning and data processing.
• Strong understanding of machine learning lifecycle, from data acquisition to model deployment and monitoring.
• Excellent problem-solving, debugging, and analytical skills.
• Strong communication and collaboration skills, with the ability to work effectively in a cross-functional team.

Bonus Skills

• Experience with big data technologies (e.g., Spark, Kafka, Hadoop).
• Knowledge of infrastructure-as-code tools (e.g., Terraform, CloudFormation).
• Experience optimizing ML models for specific hardware (e.g., GPUs, TPUs).
• Familiarity with various database technologies (SQL and NoSQL).
• Experience in building real-time ML inference services.
• Knowledge of C++ for performance-critical ML components.
• Experience with CUDA programming or GPU/Accelerator workloads.
• Contributions to open-source MLOps or machine learning projects.
• Experience in distributed systems design and development.

Pay / Benefits

• Salary or hourly rate
• Stock options plan (we are a private company, so this is not liquid) 
• If you are in the USA: healthcare, dental, vision, 401k 
• Unlimited time off policy
• Flexible working hours

Hiring Process

• Submit a resume to us for review.
• We will follow up with a technical screening (this will take approximately one hour).
• Following the successful completion of the technical screening, we will schedule an Onsite Interview.
• The Onsite Interview to meet more of the team, it will consist of the following (total time three hours):
• • Technical Screenings (1-2)
  • System Design
  • Behavioral Interview
• We’ll reach out with an offer if you're a great fit. 
• Once accepted, you start working with us!