Machine Learning Engineer
Build and deploy AI-powered features for conversation intelligence, developing production ML pipelines and inference services for voice and messaging data. The role requires 2+ years of applied ML experience, Python, an ML framework, NLP familiarity, and cloud infrastructure experience.
About the job
Responsibilities
- Design and develop machine learning solutions for accuracy, performance, security, and scalability.
- Implement and maintain end-to-end AI/ML pipelines, including data ingestion, feature engineering, model development, validation, and deployment.
- Instrument AI/ML services with metrics, logging, and telemetry to monitor model performance and operational health against defined SLOs.
- Participate in on-call rotations, progressive rollouts, and mitigation strategies for production inference services.
- Collaborate during planning, design, and code review, contributing to product and technical discussions and improving code quality.
Requirements
- Bachelor's degree in Computer Science, Mathematics, Statistics, or a related quantitative field, or equivalent practical experience.
- 2+ years of experience in machine learning engineering or applied ML.
- Proficiency in Python or a similar object-oriented language.
- Experience with at least one ML framework: PyTorch, TensorFlow, or JAX.
- Familiarity with NLP libraries such as Hugging Face Transformers, NLTK, or SpaCy.
- Experience developing, testing, and deploying small-to-medium scoped ML services or features in a collaborative engineering environment.
- Experience with model versioning, experiment tracking, and cloud-based infrastructure.
- Experience utilizing large or small language models within software systems.
- Excellent written and verbal communication skills, with the ability to explain complex technical concepts to technical and non-technical audiences.
Nice-to-haves
- Hands-on experience with conversational AI, LLM fine-tuning, or prompt engineering in production.
- Exposure to agentic AI frameworks such as LangGraph, AutoGen, or CrewAI.
- Familiarity with MLOps/LLMOps tooling for testing, versioning, model registries, retraining, and production monitoring.
Compensation and Benefits
- Competitive pay.
- Generous time off.
- Parental and wellness leave.
- Healthcare.
- Retirement savings program.
- Additional benefits that vary by location.
- Occasional travel for project or team meetings may be required.
Skills
Python, PyTorch, TensorFlow, JAX, Hugging Face Transformers, Nltk, Spacy, LLMs, AWS, GCP, Microsoft Azure, LangGraph, Autogen, Crewai, MLOps
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