Senior Software & AI Engineer at Covalent in Sunnyvale, CA

Why Join Covalent

At Covalent, you’ll work alongside world-class scientists and engineers in a dynamic, collaborative environment. We empower our team members to take ownership of their work, innovate constantly, and engage directly with customers, shaping the future of technology.

Covalent helps the world’s most advanced technology companies measure, understand and improve their products. We work at the intersection of semiconductors, advanced materials and high-precision measurement. As our customers push the limits of physics and manufacturing, software and AI are becoming core to how we create value, scale operations, and differentiate in the market.

Our software, data, and AI team operates like a startup within a deep-tech company. We focus relentlessly on revenue growth, operational efficiency and profitability, and we ship systems that are used every day by internal teams and external customers. You will join a high-impact team dedicated to one mission: leveraging data, automation and AI to scale our business.

Role Overview

We are hiring a Senior Software & AI Engineer to build production-grade AI systems, with a strong emphasis on deep learning and generative AI. You will work on specific components and services, but you are expected to think at the system level—understanding how models, data, infrastructure, and users fit together to drive business outcomes.

If you enjoy rolling up your sleeves, making pragmatic tradeoffs, and seeing your work used in production, you’ll fit right in.

Key Responsibilities

Build & Own Systems

• Design and implement production software services used by internal teams and external customers.

• Own specific components or services end-to-end: design, implementation, deployment, monitoring, and improvement.

• Design and maintain scalable full-stack applications and APIs that serve both our internal scientists and external customers.

• Design and deploy deep learning and generative AI systems in production

• Build LLM-powered applications, including prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and agent-based workflows

• Evaluate tradeoffs between foundation models, fine-tuning, and custom architectures

• Ensure AI systems are reliable, cost-efficient, observable, and safe in real-world use

• Apply traditional ML techniques where appropriate (e.g., classification, regression, anomaly detection, optimization)

• Own the full ML lifecycle when ML is the right solution: data preparation, training, evaluation, deployment, and monitoring

• Make pragmatic decisions about when not to use ML or AI

• Build robust data pipelines to ingest, process, and store massive datasets from diverse metrology instruments. Also develop analysis and reporting workflows for that data.

• Develop and deploy machine learning models to automate data analysis, image recognition (SEM/TEM), and predictive maintenance for high-end lab equipment.

• Work with real-world data from scientific instruments, operations, and customer workflows.

Systems-Thinking

• Understand how your component fits into larger systems and workflows.
• Design for reliability, scalability, and long-term maintainability.
• Collaborate with teammates to evolve system architecture over time.

Business Impact

• Work directly with domain experts, operations, and leadership to solve high-impact problems.
• Help shape customer-facing software products and internal platforms.
• Focus on outcomes, not just implementations.

Required Qualifications

• 10+ years of experience building and owning production software systems.
• Strong programming skills in one or more modern languages (C# .NET, Python, Java, C++, Go, or similar).
• Hands-on experience deploying deep learning and/or generative AI systems in real products
• Experience building data-driven or AI-enabled applications used in real environments.
• Solid understanding of system design, APIs, databases, and cloud infrastructure.
• Hands-on experience with AWS or GCP, including serverless architecture and database management (SQL & NoSQL).
• Proven ability to take ownership and deliver with minimal supervision.

Preferred Qualifications

• Experience with LLM ecosystems (e.g., model APIs, fine-tuning, RAG, agents)
• Experience with deep learning frameworks (PyTorch, TensorFlow, scikit-learn).
• Experience with MLOps and AI infrastructure (training pipelines, inference optimization, monitoring)
• Experience working with scientific or industrial data.
• Experience building SaaS or enterprise software.
• A background in Physics, Materials Science, or Engineering alongside their CS skills.
• A portfolio of projects where they have used AI to solve real-world problems.

How We Work

• Small, senior team with high trust and high ownership.
• Pragmatic, outcome-driven engineering culture.
• Direct access to users, stakeholders, and decision-makers.
• Emphasis on clarity, speed, and long-term quality.

Why This Role Is Different

• Your work directly affects revenue and profitability.
• You’ll build systems that matter, not prototypes that die.
• You’ll have autonomy, but also real responsibility.
• You’ll help shape how software, data and AI are used across a growing, technically sophisticated company.