Machine Learning Software Engineer II (AI)

Location: Remote-first; remote work may be occasional or permanent. Reliable cable or fiber-optic broadband with minimum speeds of 10 Mbps download and 5 Mbps upload is required for remote work.

Company

hirify.global develops educational assessment and scoring solutions using machine learning and software systems.

What you will do

• Lead the transition of machine learning models from research prototypes into scalable, high-performance production systems.
• Architect and deploy containerized and serverless ML workloads using AWS ECS and AWS Lambda.
• Optimize PyTorch models for ONNX deployment using quantization, pruning, ONNX Runtime, and Flash Attention to improve latency and throughput.
• Establish reliable CI/CD, secure, reproducible deployments, testing, debugging, and documentation practices.
• Design and evaluate algorithms that generate descriptive, diagnostic, predictive, and prescriptive insights from structured and unstructured data.
• Collaborate with mathematicians, computer scientists, psychometricians, and statisticians on requirements, research discussions, system design, and scoring solutions.

Requirements

• 2–5 years of industry experience in machine learning engineering, software engineering, or data science, including architecting and deploying models to production.
• Hands-on AWS experience with AWS ECS and AWS Lambda, plus containerization with Docker and event-driven architectures.
• Strong proficiency in modern ML programming languages such as Python, C++, or Java, together with industry-standard coding practices.
• Hands-on experience with PyTorch and other ML libraries such as Scikit-Learn or TensorFlow.
• Deep understanding of model optimization pipelines, including PyTorch-to-ONNX conversion, ONNX Runtime, and scaling attention mechanisms such as Flash Attention.
• Experience with large-scale computing frameworks, data analysis systems, and relational or non-relational databases.

Nice to have

• Experience with AWS SageMaker for managed model training and hosting.
• Experience applying parameter-efficient fine-tuning methods such as LoRA and qLoRA to large language models.
• Experience building, integrating, and deploying autonomous or semi-autonomous AI agents that connect ML models with external tools or APIs.
• Experience with deep learning for Natural Language Processing and complex text-based modeling.
• Experience collaborating with psychometricians, statisticians, or other specialized researchers to operationalize complex mathematical concepts.
• Experience implementing infrastructure as code with Terraform or AWS CloudFormation.
• Experience setting up model monitoring for data drift, concept drift, and model degradation in production AWS environments.

Culture & Benefits

• Remote-first work environment designed to support flexibility and work-life balance regardless of individual locations.
• Remote-office setup reimbursement is available.
• Employees are expected to participate actively in video-based interviews and ongoing virtual meetings with cameras on.
• Reasonable accommodations are available during the recruiting process for qualified individuals with disabilities.

Hiring process

• Recruiting includes video-based interviews with active camera participation.
• Use of note-taking tools, reference materials, or AI-powered tools during interviews or selection activities is prohibited unless prior written approval is obtained; accommodation exceptions may be discussed in advance.