ML Engineer

Kindredventures; San Francisco (CA); On-site; USD 130,000 - 210,000; Full time

Job summary

Kindredventures is seeking an ML-focused engineer to build and ship real-time perception pipelines for our devices, from prototype to production.

You will work with Python data pipelines, ML frameworks like PyTorch or TensorFlow, and various data stores, and you'll own end-to-end lifecycle from data collection to deployment and monitoring.

Qualifications

• 3–6 years of experience working with AI models in production, ideally for real-time applications.
• Fluent in Python for data pipelines and backend features involving third-party model providers’ APIs and SDKs.
• Experience with relational DBs, No-SQL, and cloud object stores.
• Experience with PyTorch and TensorFlow; edge/embedded inference is a plus.
• Build data pipelines and evaluation frameworks to improve AI feature quality from real-world usage.
• Own the full lifecycle: data collection, dataset versioning, evaluation design, deployment and monitoring in the field, and feature improvement recommendations.
• Comfortable with cloud infrastructure.
• Move fast in an environment where AI meets physical products.

Responsibilities

• Build and ship machine learning pipelines that enable real-time perception and intelligence on devices.
• Develop data pipelines and evaluation frameworks to continuously improve AI feature quality from real-world usage.
• Own end-to-end lifecycle: data collection, dataset versioning, evaluation design, deployment and monitoring in the field, and feature improvement recommendations.
• Collaborate across teams and work with cloud infrastructure to deploy models at scale.

Skills

Python

Real-time ML

Data pipelines

Edge/embedded inference

Cloud infrastructure

Tools

PyTorch

TensorFlow

AWS

GCP

Job description

Focus

Build and ship the machine learning pipelines that give our devices real-time perception and intelligence, from prototype to production at scale.

• 3–6 years of experience working with AI models in production, ideally for real-time applications.
• Fluent in Python for data pipelines and backend features involving third-party model providers’ APIs and SDKs.
• Well-versed in a variety of data stores and their tradeoffs: relational DBs, No-SQL, cloud object stores, etc.
• Experience with modern ML frameworks (PyTorch/TensorFlow), and experience optimizing models for edge/embedded inference is a plus, but not required.
• Build data pipelines and evaluation frameworks to continuously improve AI feature quality from real-world usage.
• Own the full lifecycle: data collection, dataset versioning, evaluation design, deployment and monitoring in the field, and feature improvement recommendations.
• Comfortable with cloud infrastructure.
• Ability to move fast in an environment where things are sometimes loosely defined, and a genuine curiosity about where AI meets physical products.