Senior Machine Learning Engineer / Researcher (AI)

TL;DR

Senior Machine Learning Engineer / Researcher (AI): Driving the design, development, and deployment of advanced ML models and systems for a desktop assistant with an accent on contextual retrieval, knowledge graphs, and productionizing state-of-the-art text and OCR models. Focus on building and optimizing data pipelines, MLOps, and ensuring scalability and low latency of AI features.

Location: On-site in NYC or San Francisco, requiring five days a week in our NYC headquarters. Must be based in or willing to relocate to NYC.

Salary: $250,000–$300,000

Company

hirify.global is a cutting-edge desktop assistant company focused on enhancing productivity by seamlessly integrating AI with user workflows through text and voice commands.

What you will do

• Design and implement knowledge graph or memory systems for contextual retrieval, reasoning, and persistent knowledge.
• Research, prototype, and deploy state-of-the-art machine learning text and OCR models (e.g., transformer architectures, computer vision, reinforcement learning).
• Build pipelines for data ingestion, feature engineering, model training, evaluation, and deployment.
• Work on productionizing models, focusing on monitoring, scalability, latency, retraining, A/B testing, and lifecycle management.
• Develop infrastructure and tooling to support ML experimentation and production, including model serving and MLOps.
• Collaborate with cross-functional teams to integrate ML features into product flows and participate in architecture discussions.

Requirements

• Master’s or PhD in Computer Science, Machine Learning, Statistics, Applied Math, or equivalent experience is preferred.
• Must have 5+ years of hands-on experience building machine learning models in production environments.
• Solid understanding of ML fundamentals: supervised & unsupervised learning, deep learning, model evaluation metrics, deployment, and inference latency trade-offs.
• Familiarity with knowledge representation and retrieval, including knowledge graphs, embeddings, and memory systems.
• Experience deploying models at scale in production (AWS/GCP/Azure) and with production-grade MLOps (CI/CD, monitoring).
• Experience with data engineering, including ETL pipelines, feature stores, and large datasets.

Nice to have

• Published research or open-source contributions in ML/AI.
• Experience with generative AI (LLMs, diffusion models), computer vision, or multimodal ML.
• Knowledge of prompt engineering, RAG, and embeddings.

Culture & Benefits

• Competitive salary and generous equity package.
• Health, dental, and vision insurance.
• Flexible PTO and parental leave.
• Paid team lunches during the week.
• Relocation package available.
• Work in a fast-paced, collaborative environment with a 100% in-office culture during the week.