As a Machine Learning Engineer, you will design and build cutting-edge AI/ML systems that drive meaningful business outcomes at scale. You will work cross-functionally to bring innovative machine learning solutions from research and experimentation through to robust, production-grade deployment.
The MLE will collaborate with other MLEs to build scalable, production-ready ML solutions, taking algorithms from initial concept through to deployment. This hire will design end-to-end AI/ML solutions with clear business impact, from concept to deployment, with a strong focus on feasibility, scalability, and performance. You will benchmark, adapt, and integrate AI/ML models into existing systems.
Minimum Qualifications
- 8 years of related experience building high-throughput, scalable applications or machine learning models in a production environment.
- Bachelor's Degree in Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field.
- Proficiency in one or more object-oriented programming languages such as Python, Java, or C++, with hands-on experience building distributed systems.
- Experience building large-scale machine learning systems using big data technologies such as Spark, SQL, Snowflake, or similar platforms.
- Experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Familiarity with MLOps practices including model versioning, CI/CD pipelines, and experiment tracking tools such as MLflow or similar.
- Experience building and deploying applications using large language models (e.g., GPT-4, Claude, Gemini, or open-source alternatives) via APIs or self-hosted inference.
- Hands-on experience with agentic frameworks such as LangChain, LlamaIndex, or AutoGen to build multi-step, tool-augmented AI workflows.
Preferred Qualifications
- 10 years of related experience building high-throughput, scalable applications or machine learning models in a production environment.
- Solid understanding of ML fundamentals including supervised/unsupervised learning, model evaluation, and feature engineering.
- Strong problem-solving skills with the ability to translate ambiguous business problems into well-defined ML solutions.
- Excellent cross-functional communication skills with the ability to collaborate effectively across engineering and data science teams.
- Familiarity with LLM evaluation practices including output quality assessment, hallucination detection, and latency benchmarking in production environments.