About Slice

Issue equity to your teams anywhere, stay in compliance locally, optimize tax for your employees, and do it all easily & quickly.

Slice is a Global Equity Management platform for multinational companies. It handles the full equity lifecycle — from correctly setting up option plans around the globe to awarding grants and liquidity — while keeping companies and employees in compliance with each country’s local equity laws and away from tax penalties. Slice covers 25+ countries, including the US, UK, and Israel, and serves startups from seed to growth stage, plus several unicorns.

We're backed by leading investors like TLV Partners and leading international law firms such as Wilson Sonisi and Fenwick & West.

At Slice, you’ll work with a top-tier team (8200, Ex-Google, Ex-Meta).

Role

As a Senior Machine Learning Engineer at Slice, you will play a key role in developing the infrastructure and pipelines to power AI-driven solutions for legal, equity, and tax document management. You will be responsible for building and optimizing production-level machine learning systems, with a focus on scalability, reliability, and performance. This is an exciting opportunity for engineers passionate about applying machine learning in a rapidly growing startup, particularly within the fintech and legal tech industries.

Key Responsibilities

• Model Deployment and Optimization: Implement, deploy, and maintain machine learning models for document analysis, focusing on scalability, performance, and reliability in production environments.
• Pipeline Engineering: Design and build robust ML pipelines for automated data ingestion, processing, and model deployment. Ensure the systems are efficient and can scale as the company grows.
• Infrastructure Ownership: Manage and optimize the ML infrastructure, ensuring smooth integration with existing cloud systems (GCP, Firebase) and ensuring the environment supports large-scale document processing.
• Collaboration with Engineering Teams: Work closely with backend and DevOps teams to integrate machine learning solutions into the broader platform architecture, ensuring alignment with the company’s technical vision.
• Model Performance Monitoring: Establish continuous monitoring systems for production models, ensuring they perform well under varying conditions, and make adjustments to improve their robustness.
• Research Integration: Keep up with advancements in ML engineering tools and practices, integrating modern techniques into the development process to enhance system performance and maintainability.
• Mentorship and Leadership: Mentor junior engineers, fostering a culture of engineering excellence, and guide the team on best practices in ML deployment and infrastructure design.

Qualifications

• Experience: 5+ years of hands-on experience in building and deploying machine learning systems in production, with a strong focus on infrastructure, scalability, and reliability.
• Programming Skills: Strong proficiency in Python (Node.js is a plus), with extensive experience in ML-related libraries and frameworks such as TensorFlow, PyTorch, or similar. Strong understanding of infrastructure-as-code practices.
• ML Engineering Expertise: In-depth knowledge of machine learning pipelines, production model lifecycle management, and experience with large-scale data processing systems.
• Cloud Expertise: Familiarity with deploying ML models and pipelines in cloud environments, with a strong preference for experience on GCP (Firebase experience is a plus).
• Problem-Solving: Strong engineering mindset, with the ability to independently manage complex projects, optimize infrastructure, and ensure reliable performance in a fast-paced startup environment.
• Collaboration and Communication: Strong collaboration skills with other engineering teams and the ability to articulate technical solutions clearly to stakeholders.