Machine Learning Engineer
Build and operate production machine learning systems for real-time conversational AI, spanning data pipelines, model training, deployment, monitoring, and MLOps. The role requires strong machine learning, deep learning, NLP, Python, and production systems experience.
About the job
Responsibilities
- Design, build, and maintain scalable machine learning systems, from data ingestion and preprocessing through training, testing, and deployment.
- Develop and optimize end-to-end machine learning pipelines, including data collection, labeling, training, validation, and monitoring.
- Implement MLOps practices such as model versioning, experiment tracking, machine learning CI/CD, and continuous production monitoring.
- Collaborate with product and engineering teams to integrate and deploy models in real-time products with a focus on efficiency and scalability.
- Ensure data quality, observability, and performance across AI systems.
- Stay current with AI infrastructure, tooling, and research.
Requirements
- Strong experience in machine learning, deep learning, and natural language processing.
- Background in MLOps and data pipelines, including model deployment, monitoring, and scaling in production.
- Proficiency in Python and familiarity with Go.
- Experience with machine learning lifecycle management tools such as MLflow, Kubeflow, and Weights & Biases.
- Ability to design robust, scalable, and automated machine learning systems.
- Strong coding, debugging, and data engineering skills.
- Passion for AI infrastructure and its real-world impact.
- Ownership, independence, and willingness to work deeply across the machine learning lifecycle.
Nice to Have
- Experience with speech recognition, text-to-speech, or audio processing.
- Familiarity with large language models, generative AI, or real-time inference systems.
- Hands-on experience with data orchestration frameworks such as Airflow, Prefect, or Dagster.
- Experience in startup environments with fast iteration cycles.
- Knowledge of cloud infrastructure such as AWS, Google Cloud, or Azure.
- Experience with Docker and Kubernetes.
Compensation and Benefits
- Competitive salary and equity in a high-growth startup.
- Healthcare, dental, and vision coverage.
- Opportunity to work at a high-growth AI startup with ownership and autonomy.
Skills
Machine Learning, Deep Learning, Natural Language Processing, MLOps, Python, Go, MLflow, Kubeflow, Weights & Biases, Data Engineering, Speech Recognition, LLMs, Airflow, Docker, Kubernetes
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