Senior GenAI & ML Engineer - Production AI Systems

Slate; United States; Sur place; USD 142 000 - 213 000; Plein temps

Avantages offerts par ce poste

Medical insurance

Dental insurance

Vision insurance

Life insurance

Disability insurance

Vacation

401k

Equity program

Discretionary incentive program

Résumé du poste

Slate is seeking an ML/GenAI engineer to build production AI features for manufacturing and supply chain operations. You will own data pipelines, model training, and evaluation, and collaborate with Vehicle Engineering and Manufacturing teams.

The role emphasizes practical AI applied to physical systems, with a focus on reliability, velocity, and cross-functional communication across the organization.

Qualifications

• Strong foundation in ML and AI with end-to-end project experience.
• Experience building production-grade ML systems and pipelines.
• Familiarity with supervised learning, NLP, CV, and time-series models.
• Proficient in evaluating model quality beyond accuracy on held-out data.
• Hands-on with cloud platforms and MLOps practices.
• Ability to work across manufacturing, logistics and supply chain domains.

Responsabilités

• Build and ship AI/ML features across the full model lifecycle.
• Contribute to GenAI systems, including RAG pipelines and LLM integration.
• Develop data and evaluation infrastructure with labeling workflows.
• Work on manufacturing, supply chain, and physical operations problems.
• Collaborate with Vehicle Engineering, Manufacturing and Operations.

Connaissances

Python proficiency

ML fundamentals

NLP

Computer vision

Time-series modeling

LLM APIs

RAG workflows

Embeddings/ retrieval

Model evaluation

Collaboration across disciplines

Formation

BS in CS/Math/Engineering

MS/PhD preferred

Outils

PyTorch

JAX

Hugging Face

pandas

scikit-learn

AWS

GCP

Azure

Version control

MLOps

Description du poste

Slate is seeking an ML/GenAI engineer to build production AI features for manufacturing and supply chain operations. You will own data pipelines, model training, and evaluation, and collaborate with Vehicle Engineering and Manufacturing teams.

The role emphasizes practical AI applied to physical systems, with a focus on reliability, velocity, and cross-functional communication across the organization.