Machine Learning Research Engineer

Location: 100% remote within the United States

Salary: $100,000–$150,000 annually

Company

hirify.global is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.

What you will do

• Design, prototype, and evaluate applied AI solutions across natural language, computer vision, recommendation, and structured data domains.
• Translate ambiguous business problems into well-scoped ML formulations with measurable success metrics and evaluation strategies.
• Implement rigorous experimentation workflows, including baselines, ablations, and statistically sound evaluations.
• Build production-quality training and inference pipelines using modern ML frameworks and orchestration tools.
• Optimize models for accuracy, latency, throughput, and cost while collaborating with ML platform engineers on compute, storage, and accelerator resources.
• Develop dataset tooling, data-quality monitoring, safety and fairness evaluations, documentation, and prototype-to-production practices; mentor engineers and contribute to the AI roadmap.

Requirements

• Master’s or PhD in Computer Science, Machine Learning, Statistics, or a related field, or equivalent applied experience.
• 6+ years of combined research and applied ML engineering experience.
• Strong proficiency in Python and modern ML frameworks such as PyTorch or JAX.
• Hands-on experience training, fine-tuning, and evaluating deep learning models at non-trivial scale.
• Experience taking ML models from research prototype to production with observability and safeguards, including distributed and mixed-precision training.
• Must be authorized to work in the United States; U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates are encouraged to apply. New H-1B visa petitions cannot be sponsored. Strong written and verbal English communication skills are required.

Nice to have

• Published research at top-tier AI/ML venues.
• Experience with large language model training, fine-tuning, or evaluation.
• Familiarity with retrieval-augmented generation, agentic systems, or multimodal architectures.
• Exposure to responsible AI, model evaluation, and alignment practices.
• Experience contributing to open-source ML projects.

Culture & Benefits

• Full-time direct W-2 employment.
• Opportunity to work across cloud, AI, data, and enterprise solutions.
• Focus on knowledge sharing, reading groups, and internal prototype-to-production playbooks.
• Equal employment opportunity and a workplace free from discrimination and harassment.