Senior Machine Learning Engineer @ RXNT

Join a team who is building an AI product suite, on the solid foundation of a company serving the industry for over 2 decades.

We are re-inventing and radically improving how the patients receive care, and enabling the medical workers to comfortably focus on patients and be precise in their treatment. This is the promise we are delivering on and feeling excited about.

About RXNT

At RXNT, we think of ourselves as a backbone to the US healthcare system. Every day for the past 25 years, we have been providing the digital foundation on which healthcare professionals serve patients, order medications and lab tests, bill insurance companies, and interact with the wider healthcare community.

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

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

What we're looking for in an experienced senior MLE:

Have strong foundational understanding of machine learning disciplines and advanced ML techniques.
Understands the full ML lifecycle—from data analysis to model training, fine-tuning, and deployment.
Is experienced in building and launching models that are serving customers at scale today.
Is excited about orchestrating AI agents 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 different models, including multi-modal generative models, for various use cases.
Experiment with techniques to enhance performance 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.

3. Building for Production

Write robust Python code that meets high standards and familiarity with software engineering principles.
Collaborate with software engineers to integrate the ML models into larger, distributed systems.
Adhere to best practices in code reviews, version control, CI/CD, and testing.

4. ML Pipeline Management

While MLOps is not your primary role, you should have a strong foundation in full ML lifecycle and be familiar with cloud platforms (AWS, GCP, Azure), containerization, and monitoring to ensure models run reliably in production.

5. Collaboration & Technical Leadership

Work closely with data teams, software engineers, and product teams to design and deliver impactful AI features.
Champion best practices in machine learning.

You Might Be a Good Fit If You Have:

1. Professional Background

5+ years of professional experience in machine learning engineering and/or data science.

2. Education:

Preferably MS or PhD in computer science, math, artificial intelligence, or similar disciplines; or BSc with equivalent experience.

3. Machine Learning Expertise

Strong knowledge of ML fundamentals: model architectures, data analysis, evaluation methods, and strong statistics and probabilities foundations.
Proficient in ML frameworks (e.g., PyTorch, TensorFlow, JAX) and advanced techniques for fine-tuning.
Expert at building accurate and high performing models, including evaluation, profiling, iterating, and monitoring.

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:

Hands-on experience with LLMs, Generative AI, or similar advanced ML domains is a strong advantage, but not mandatory.
Experience leading or mentoring teams on technical projects.
Strong published research background is an advantage.

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!

Application process and more information about the company:

The screening process is designed to maximize comfort and effectiveness of the time spent. At each offline stage you are more than welcome to book a time to meet with our hiring team, but you are given the comfort of starting the process in a more relaxing, self-paced offline process before starting your virtual onsite interviews.

You can view more information about the full interview process and how to prepare here.

Libre Talent

Senior Machine Learning Engineer @ RXNT 🧑‍💻

100% Remote (from US/Can.)up to $180k USD (if in U.S.) or $220k CAD (Canada) + bonuses + retirementfull-time