Note: The job is a remote job and is open to candidates in USA. Slate is building safe, reliable, customizable vehicles in the USA as part of its commitment to reindustrialization. The Senior AI/ML Engineer will build and ship production AI/ML features, contribute to GenAI systems, develop data and evaluation infrastructure, and apply AI to manufacturing, supply chain, and physical operations while collaborating across the organization.
Responsibilities
- Build and ship AI/ML features. 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
- Contribute to GenAI systems. 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
- Build data and evaluation infrastructure. Write data pipelines, labeling workflows, and evaluation frameworks. Reliable evals are a first-class deliverable on this team
- Work on manufacturing, supply chain, and physical operations problems. 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
- Collaborate across the organization. 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
- Technically grounded in ML. 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
- A fast learner. The GenAI landscape moves quickly and so does Slate. You pick up new tools and domains without needing everything handed to you
- Hands-on. You write code, run experiments, and ship things. Research interest without engineering follow-through is not a fit for this role
- Collaborative and clear. 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
- Interested in physical and operational AI. 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.
Company Overview