Libre Talent hires world-class technical talent for early-stage startups. This intake form is for one of Libre Talent's clients, as described below in more detail.

Senior Machine Learning Engineer @ RXNT

About RXNT

At RXNT, we think of ourselves as a backbone to the US healthcare system. Every day, we provide the digital foundation on which healthcare professionals serve patients, order medications and lab tests, bill insurance companies, and interact with the wider healthcare community.

25 years of blood, sweat, and tears have taught us that this pursuit comes with great responsibility. It's in the best interest of everyone if we hold ourselves to high standards, think rigorously, and at times, act urgently. Interesting technical challenges keep us engaged and enable us to do important work.

We're looking for self-motivated people who love to learn, are comfortable in a fast-paced and quickly-growing environment, and who seek ownership.

You will join a team operating at start-up pace, led by experienced entrepreneurs, yet leveraging the strong foundations of a company with decades in the business.

Senior Machine Learning Engineer:

We’re looking for an experienced Machine Learning Engineer who:

1. Understands the full ML lifecycle—from data preprocessing to model training, fine-tuning, and deployment.
2. Has real-world production experience building and launching services at scale (not just lab experiments).
3. Has a strong software engineering foundation, with the ability to write clean, maintainable, and efficient code.
4. Is excited about orchestrating AI agents (e.g., using LangGraph or similar) and training, fine-tuning, and deploying models in production environments.

What You’ll Be Doing

1. Model Training & Fine-Tuning

• Train, fine-tune, and optimize foundation models and LLMs for various use cases.
• Experiment with techniques to enhance performance (prompt engineering, hyperparameter tuning, etc.) while keeping a production-first mindset.

2. Generative AI Orchestration

• Build and manage ML graphs/workflows using orchestration frameworks like LangGraph or similar tools to enable AI-driven agents.
• Integrate multi-step pipelines that might involve text generation, retrieval-augmented generation (RAG), or other advanced NLP capabilities.

3. Production-Grade Code & Infrastructure

• Write robust Python code that meets high standards of quality, scalability, and reliability.
• Collaborate with software engineers to integrate ML services into larger, distributed systems.
• Adhere to best practices in code reviews, version control, CI/CD, and testing.

4. ML Pipeline Management

• Develop and maintain end-to-end pipelines for data ingestion, feature engineering, model training, and inference.
• While MLOps is not your primary role, you should be comfortable with cloud platforms (AWS, GCP, Azure), containerization, and monitoring to ensure models run reliably in production.

5. Collaboration & Technical Leadership

• Work closely with data scientists, software engineers, and product teams to design and deliver impactful AI features.
• Provide technical guidance on system architecture and ensure solutions align with production needs.
• Champion best practices in machine learning, as well as software engineering principles.

You Might Be a Good Fit If You Have:

1. Professional Background

• 5+ years of overall professional experience in machine learning engineering and/or software engineering.
• Recent 2+ years focused primarily on ML engineering with production deployments (ideally in Generative AI, NLP, or related areas).
• Experience leading or mentoring teams on technical projects is a plus.

2. Machine Learning Expertise

• Strong knowledge of ML fundamentals: model architectures, data processing, evaluation methods, and strong statistics and probabilities foundations.
• Hands-on experience with LLMs, Generative AI, or other advanced ML domains.
• Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and advanced techniques for fine-tuning.
• Experience with audio and image modalities is a plus.

3. Software Engineering Skills

• Proficiency in Python (expert-level), with a track record of writing production-quality code.
• Solid understanding of system design, APIs, microservices, and distributed architectures.
• Comfort with software development best practices (code reviews, testing, CI/CD).

4. MLOps & Cloud Know-How

• While not the primary focus, you’re comfortable with containerization, orchestration, and basic cloud services.
• Experience with monitoring, logging, and CI/CD pipelines to keep ML systems healthy and up-to-date.

5. Mindset & Culture

• Bias to action: you excel at moving quickly and pragmatically to solve complex problems.
• Attention to detail: you value clean, maintainable solutions that can scale.
• Ownership: you take pride in your work and thrive in a collaborative environment.
• Adaptability: you enjoy learning and can pivot quickly as new challenges arise.

Nice to Have:

• LangChain / LangGraph or similar frameworks for orchestrating AI components.
• Audio or multimedia processing techniques (e.g., ffmpeg, WebRTC).
• Infrastructure-as-code (Terraform, Pulumi) or DevOps tools.
• Previous experience in healthcare technology or other regulated industries.

Not a perfect fit on paper? Apply anyway!

We understand you might not meet every single requirement. Our team has historically welcomed candidates whose strengths outweigh the gaps—especially those who learn quickly and show a strong drive to succeed. If this sounds like you, we encourage you to apply!