AI/ML Engineer

Summary

Slate Automotive is building safe, reliable, customizable vehicles in the USA and developing an AI-native vehicle platform. The AI/ML Engineer will build and deploy production AI/ML features, GenAI systems, data pipelines, and evaluation infrastructure while applying AI to manufacturing, supply chain, and physical operations. The role collaborates with Vehicle Engineering, Manufacturing, and Operations to deliver measurable AI-driven outcomes.

Responsibilities

• Work across the model lifecycle: data preparation, training or fine-tuning, evaluation, deployment, and monitoring. Own features end-to-end and iterate based on real production feedback
• Build RAG pipelines, work with LLM APIs and open-source models, design prompts for reliability, and contribute to agentic workflows. You will work on the full GenAI stack hands-on
• Write data pipelines, labeling workflows, and evaluation frameworks. Reliable evals are a first-class deliverable on this team
• Slate operates a factory with a real supply chain. You will be exposed to AI problems in both areas: computer vision for quality inspection, predictive maintenance, and sensor data on the physical side; demand forecasting, inventory planning, supplier risk, and logistics on the supply chain side. We are particularly interested in candidates who are drawn to this kind of work
• Work with Vehicle Engineering, Manufacturing, and Operations to understand requirements and translate them into AI systems that produce measurable output

Skills

• We are open to candidates early in their careers if they have the right foundation
• A PhD in a relevant field is a strong signal; engineers without one should bring equivalent depth through demonstrated project work, research, or professional experience
• What matters is that you can build things, learn fast, and are genuinely interested in applying AI to physical and operational systems
• You understand how models are trained and evaluated, not just how to call an API
• You have built something end-to-end — a project, a thesis, a production system — that demonstrates that
• You pick up new tools and domains without needing everything handed to you
• You write code, run experiments, and ship things
• Research interest without engineering follow-through is not a fit for this role
• You work well across disciplines and can explain technical decisions to non-technical stakeholders
• Be willing to directly interreact with stakeholders to build product without the need for a product manager
• A PhD in a relevant field is a strong foundation for someone early in their career and is treated as such
• Candidates without a PhD should bring 3+ years of professional or research experience working directly on ML systems
• In either case, the bar is the same: you need to demonstrate you can build
• Foundational understanding of ML: model training, loss functions, evaluation metrics, overfitting, and regularization
• Practical experience with: supervised learning, NLP, computer vision, and time-series modeling
• Familiarity with LLM APIs (OpenAI, Anthropic, Gemini, or similar) and how to build reliably on top of them
• Basic exposure to RAG, embeddings, or retrieval systems — including in a project or research context
• Ability to evaluate model quality rigorously, not just report accuracy on a held-out set
• Python proficiency: comfortable with the ML stack (PyTorch or JAX, Hugging Face, pandas, scikit-learn)
• Ability to write production-quality code, not just notebook code
• Familiarity with cloud platforms (AWS, GCP, or Azure) at a working level
• Version control, experiment tracking, and basic MLOps practices
• BS required
• Candidates without advanced degrees should bring equivalent depth through their project or professional work
• Candidates drawn to the intersection of AI and the physical world — manufacturing systems, robotics, logistics, industrial data — will find the most to work on and will ramp fastest
• Not a requirement, but a clear differentiator
• Academic or professional background in Mechanical Engineering, Electrical Engineering, Robotics, Industrial Engineering, or a related physical discipline
• Exposure to computer vision, sensor data, or real-time systems — including coursework or personal projects
• Familiarity with supply chain, logistics, or operations research problems
• Experience with simulation environments or physical hardware in a research or lab setting
• MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Mechanical Engineering, Industrial Engineering, or a related field preferred
• A PhD is treated as a strong signal for early-career candidates

Qualifications

Must Haves

• We are open to candidates early in their careers if they have the right foundation
• A PhD in a relevant field is a strong signal; engineers without one should bring equivalent depth through demonstrated project work, research, or professional experience
• What matters is that you can build things, learn fast, and are genuinely interested in applying AI to physical and operational systems
• You understand how models are trained and evaluated, not just how to call an API
• You have built something end-to-end — a project, a thesis, a production system — that demonstrates that
• You pick up new tools and domains without needing everything handed to you
• You write code, run experiments, and ship things
• Research interest without engineering follow-through is not a fit for this role
• You work well across disciplines and can explain technical decisions to non-technical stakeholders
• Be willing to directly interreact with stakeholders to build product without the need for a product manager
• A PhD in a relevant field is a strong foundation for someone early in their career and is treated as such
• Candidates without a PhD should bring 3+ years of professional or research experience working directly on ML systems
• In either case, the bar is the same: you need to demonstrate you can build
• Foundational understanding of ML: model training, loss functions, evaluation metrics, overfitting, and regularization
• Practical experience with: supervised learning, NLP, computer vision, and time-series modeling
• Familiarity with LLM APIs (OpenAI, Anthropic, Gemini, or similar) and how to build reliably on top of them
• Basic exposure to RAG, embeddings, or retrieval systems — including in a project or research context
• Ability to evaluate model quality rigorously, not just report accuracy on a held-out set
• Python proficiency: comfortable with the ML stack (PyTorch or JAX, Hugging Face, pandas, scikit-learn)
• Ability to write production-quality code, not just notebook code
• Familiarity with cloud platforms (AWS, GCP, or Azure) at a working level
• Version control, experiment tracking, and basic MLOps practices
• BS required
• Candidates without advanced degrees should bring equivalent depth through their project or professional work

Nice to Haves

• Candidates drawn to the intersection of AI and the physical world — manufacturing systems, robotics, logistics, industrial data — will find the most to work on and will ramp fastest
• Not a requirement, but a clear differentiator
• Academic or professional background in Mechanical Engineering, Electrical Engineering, Robotics, Industrial Engineering, or a related physical discipline
• Exposure to computer vision, sensor data, or real-time systems — including coursework or personal projects
• Familiarity with supply chain, logistics, or operations research problems
• Experience with simulation environments or physical hardware in a research or lab setting
• MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Mechanical Engineering, Industrial Engineering, or a related field preferred
• A PhD is treated as a strong signal for early-career candidates

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• Life insurance
• Disability insurance
• Vacation
• 401k
• The successful candidate may also be eligible to participate in the equity program, subject to the rules governing such programs.
• The successful candidate may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing such programs.