Senior Machine Learning Engineer
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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 TensorFlowScikit-learnSQL 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 learningNLP / LLM architectures
- Feature engineering
- Model evaluation and monitoring
Preferred Qualifications:
Experience with generative AI products and LLM application development
Familiarity with:
- RAG systems
- Vector databasesRLHF / fine-tuning workflows
- Model serving at scale
- Startup or high-growth company experience
- Open-source contributions or published ML research
- Nice-to-Have ToolsLangChain / LlamaIndexAirflow / DagsterMLflow / Weights & BiasesSpark / RayDatabricksSnowflakeBenefitsFull health, dental, vision 401(k) matching
- Equity participation
- Flexible PTOLearning & 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%)