Senior Machine Learning Engineer

Disruptive DataTech

United States

United States

About

Senior Machine Learning Engineer / AI Engineer Role Overview We are seeking an experienced Machine Learning Engineer to design, build, and deploy scalable AI systems powering intelligent products and internal platforms. This role combines applied ML, software engineering, and production infrastructure to deliver models into real-world environments. The ideal candidate has hands-on experience with deep learning, LLMs, data pipelines, and cloud deployment, and is comfortable working in a fast-paced engineering organization. Key Responsibilities Build and productionize machine learning models for recommendation, prediction, classification, ranking, or generative AI use cases Develop and fine-tune large language models, retrieval systems, and agent workflows Design scalable ML pipelines for training, evaluation, monitoring, and inference Collaborate with product, data, and engineering teams to define AI roadmaps Improve model performance, latency, reliability, and cost efficiency Implement MLOps best practices including CI/CD, experiment tracking, and model versioning Conduct A/B testing and model performance analysis in production Stay current on latest research and integrate relevant advances into product systems Required Qualifications BS/MS/PhD in Computer Science, Machine Learning, Statistics, Mathematics, or related field 5+ years of software engineering or ML engineering experience Strong Python proficiency Experience with: PyTorch or TensorFlow Scikit-learn SQL and distributed data systems Cloud platforms: Amazon Web Services, Google Cloud, or Microsoft Azure Containerization/orchestration: Docker, Kubernetes Experience deploying APIs and backend ML services Solid understanding of: Deep learning NLP / LLM architectures Feature engineering Model evaluation and monitoring Preferred Qualifications Experience with generative AI products and LLM application development Familiarity with: RAG systems Vector databases RLHF / fine-tuning workflows Model serving at scale Startup or high-growth company experience Open-source contributions or published ML research Nice-to-Have Tools LangChain / LlamaIndex Airflow / Dagster MLflow / Weights & Biases Spark / Ray Databricks Snowflake Benefits Full health, dental, vision 401(k) matching Equity participation Flexible PTO Learning & conference budget Commuter benefits Catered meals / office stipend Compensation Base Salary:
$180,000 – $260,000 Equity: Competitive startup/public company equity package Bonus: Performance-based annual bonus (10–20%)

United States

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English

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