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Augmented Engineer (AI)
Eudia
Software Engineering, Data Science
Palo Alto, CA, USA
Posted on Apr 22, 2025
About Eudia: Eudia is redefining the future of legal work with AI-powered Augmented Intelligence. Enabling Fortune 500 legal teams to move faster, manage risk more effectively, and unlock new business value. Backed by $105M in Series A funding led by General Catalyst, we’re building a category-defining platform that blends AI-driven automation with human expertise, transforming legal from a cost center into a strategic growth driver.
At Eudia, we move fast. Unlike traditional enterprise software, our teams ship solutions in days, not months—delivering real impact for some of the world’s largest companies, including Cargill, Coherent, DHL, and DuPont. We’re solving one of the most complex, unsolved challenges in AI: bringing trust, accuracy, and security to legal automation.
We’re a team of builders, operators, and problem-solvers who are passionate about reshaping an industry that has long been resistant to change. If you’re looking for a place where you’ll be challenged, take ownership from day one, and work alongside some of the brightest minds in AI and legal—we’d love to meet you.
Augmented Engineer (AI) As anAugmented AI Engineer, you will work hands-on with clients to deploy, optimize, and scale AI-powered solutions that address mission-critical problems. Combining expertise in AI engineering with exceptional problem-solving and client-facing skills, you will bridge the gap between our cutting-edge AI technologies and real-world applications. This role demands technical proficiency in machine learning, adaptability, and a passion for delivering impactful AI solutions in dynamic environments.
Key Responsibilities:
- AI Solution Deployment: Deploy, configure, and fine-tune AI models, algorithms, and platforms to meet client-specific use cases, ensuring seamless integration with existing systems.
- Model Development & Optimization: Build, train, and optimize machine learning models (e.g., LLMs, computer vision, predictive analytics) tailored to client data and goals.
- Data Pipeline Engineering: Design and implement robust data pipelines to process and transform structured and unstructured data for AI model training and inference.
- Technical Problem-Solving: Diagnose and resolve complex AI-related issues, such as model performance bottlenecks or data quality challenges, often under tight deadlines.
- Client Collaboration: Partner with clients to understand their business objectives, data challenges, and operational needs. Translate these into AI-driven technical requirements.
- Feedback Loop: Collaborate with internal AI research and engineering teams to relay client feedback, driving improvements to models and platforms.
- AI Innovation:Identify opportunities to enhance client outcomes through advanced AI techniques, such as reinforcement learning, generative AI, or real-time inference.
Qualifications:
- Education: Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
- Technical Skills:
- Proficiency in Python and AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Experience with large language models (LLMs), computer vision, or other advanced AI techniques.
- Strong knowledge of data engineering (SQL/NoSQL, ETL pipelines, data lakes).
- Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps tools (e.g., Kubeflow, MLflow).
- Experience with APIs, microservices, and deploying AI models at scale.
- Problem-Solving: Ability to tackle ambiguous AI challenges and deliver practical, high-impact solutions.
- Communication: Exceptional ability to explain complex AI concepts and model outputs to non-technical stakeholders.
- Adaptability: Thrives in fast-paced, client-facing environments with evolving requirements.
- Travel: Willingness to travel to client sites as needed (up to 20% of the time, depending on role requirements).
- Experience: 4+ years in AI engineering, machine learning, or client-facing technical roles. Experience deploying AI solutions in production is a plus.
Preferred Qualifications:
- Experience in industries like healthcare, finance, defense, or logistics, where AI drives decision-making.
- Familiarity with generative AI, reinforcement learning, or real-time AI inference systems.
- Prior experience in consulting or deploying AI solutions in a SaaS/enterprise environment.
- Knowledge of DevOps practices for AI (e.g., CI/CD for ML models, containerization with Docker/Kubernetes).
If you’re ready to take on the challenge and make an impact in a rapidly evolving industry, we want to hear from you. Apply today with your resume and a cover letter explaining why you’re the perfect fit for this role.