It starts with you – a senior ML engineer responsible for building, training, evaluating, and operating machine learning systems in production. The role focuses on data pipelines, model training, experimentation, evaluation, and scalable deployment.
If you want to grow your skills building AI products for mission-critical AI, join mission – this role is for you.
:Responsibilities
Design, train, and evaluate ML models for production use.
Build and maintain data pipelines for training, validation, and inference.
Own experimentation workflows: feature engineering, training runs, and comparison.
Implement model evals, monitoring, and drift detection.
Package and deploy models to production systems.
Optimize training and inference performance, cost, and reliability.
Collaborate with data, platform, and product teams.
Mentor engineers and promote ML engineering best practices.
If you want to grow your skills building AI products for mission-critical AI, join mission – this role is for you.
:Responsibilities
Design, train, and evaluate ML models for production use.
Build and maintain data pipelines for training, validation, and inference.
Own experimentation workflows: feature engineering, training runs, and comparison.
Implement model evals, monitoring, and drift detection.
Package and deploy models to production systems.
Optimize training and inference performance, cost, and reliability.
Collaborate with data, platform, and product teams.
Mentor engineers and promote ML engineering best practices.
Requirements:
4+ years software engineering experience with 2+ years applied ML in production.
Strong foundations in machine learning, statistics, and data analysis.
Hands-on experience with model training frameworks (e.g., PyTorch, TensorFlow, JAX).
Experience with distributed training and large-scale datasets.
Experience building data pipelines, feature engineering, and dataset versioning.
Proven experience designing and operating ML evals, experiment tracking, and monitoring.
Familiarity with feature stores, model registries, and ML lifecycle management.
Experience with model serving patterns and production deployment.
Proficiency in Python and strong system design skills.
Experience deploying ML systems on Kubernetes or similar platforms.
Familiarity with GPU acceleration and performance optimization
4+ years software engineering experience with 2+ years applied ML in production.
Strong foundations in machine learning, statistics, and data analysis.
Hands-on experience with model training frameworks (e.g., PyTorch, TensorFlow, JAX).
Experience with distributed training and large-scale datasets.
Experience building data pipelines, feature engineering, and dataset versioning.
Proven experience designing and operating ML evals, experiment tracking, and monitoring.
Familiarity with feature stores, model registries, and ML lifecycle management.
Experience with model serving patterns and production deployment.
Proficiency in Python and strong system design skills.
Experience deploying ML systems on Kubernetes or similar platforms.
Familiarity with GPU acceleration and performance optimization
This position is open to all candidates.
