Machine Learning Engineer

At Neostella, our mission is simple: empower legal teams to work smarter, faster, and more reliably. We deliver advanced technology solutions and satellite team support that streamline operations, boost efficiency, and transform the way firms and corporate legal departments work day to day. We're relentlessly customer-centric. Everything we do is in service of making our clients' work easier and helping them deliver better experiences to their clients. We're also a true team: supportive, scrappy, and always in it together. We believe in showing up for one another, rolling up our sleeves, and celebrating the wins. It's who we are, and it's how we help our customers succeed. Neostella is in hyper-growth mode, leveraging cutting-edge technology to solve real challenges for our clients. And we're looking for driven, people-first professionals to help us scale with purpose and heart. As we continue to expand, we are seeking an AI/Machine Learning Engineer to join our team! You'll develop machine learning and AI capabilities that ship to customers-working with engineers and product partners to build features that are measurable, reliable, and scalable. Why this role matters right now

Neostella is scaling fast. Our platform is handling more customers, more complexity, and higher expectations every quarter. We are investing heavily in AI and machine learning to help legal teams work smarter inside our legal case management platform. Our customers deal with messy, real-world data-documents, workflows, edge cases, and nuance-and they expect automation that actually works in practice.

This role exists because we need engineers who can bridge the gap between experimentation and production. You'll help turn emerging AI and ML capabilities into reliable, measurable product features that customers can trust every day. What you'll manage

You'll build AI and machine learning systems that ship to production and create real customer value. Working closely with product managers and backend engineers, you'll help design, implement, and iterate on features that improve automation, insight, and assistive workflows across the platform.

Your work may involve LLM-powered capabilities, traditional ML models, or hybrid approaches-always with a focus on reliability, scalability, and clear success metrics. You'll operate in ambiguity, experiment thoughtfully, and turn ideas into systems that perform under real-world constraints. What you bring

We're looking for engineers who are curious, thoughtful problem solvers-people who enjoy learning, experimenting, and improving systems over time. Curious what your day would look like as a Machine Learning Engineer? Check out the details below!, * Build, evaluate, and maintain ML and/or LLM-driven systems that power product features

• Develop data pipelines, training/evaluation workflows, and tooling to support iteration
• Optimize models and inference workflows for latency, scalability, and cost
• Collaborate closely with backend engineers to deploy and integrate models into production
• Stay current with modern ML/AI approaches and propose pragmatic improvements

• Strong Python proficiency (this is essential)
• Ability to translate messy real-world problems into workable approaches with clear success criteria
• Strong debugging and analytical thinking; comfortable iterating from prototype to production
• Solid foundations in ML/AI concepts (e.g., experience with LLMs, classification/regression, evaluation metrics, overfitting, data leakage, experiment design)

Nice to have

• 2+ years of prior experience
• Experience with LLM applications (RAG, embeddings, prompt engineering, evaluation, guardrails)
• Experience with common ML frameworks (PyTorch, TensorFlow, scikit-learn, etc.)
• Experience deploying models (batch or real-time inference), monitoring, and model lifecycle management
• Familiarity with AWS, containers, and CI/CD
• Experience with NLP, information extraction, search, or document understanding

Backgrounds that thrive here

• For this role, we welcome applicants from all backgrounds-CS, Physics, Math, Statistics, Engineering, economics, or nontraditional paths. A CS degree or work experience is not required. If you're capable of learning new skills and enjoy solving tough problems, you're the kind of patient we want.
• Please include a link to your GitHub, a portfolio, or any other example of your work in programming or a quantitative field.