Machine Learning Engineer

About Nabla

We are a team of entrepreneurs, clinicians and engineers committed to bringing back joy to the practice of medicine.

Together with a community of clinician innovators, we’ve harnessed the best of machine learning science to develop Nabla: the leading AI assistant that’s restoring the human connection at the heart of healthcare. By streamlining clinical documentation, Nabla is helping clinicians focus on what matters most - patient care. Today, over 85,000 clinicians across 130+ healthcare organizations trust Nabla to support how they deliver care every day.

We’re at the start of an ambitious journey:
Ambient listening, dictation, coding, and command capabilities are all converging into a proactive assistant that intuitively streamlines clinical and financial workflows.

Backed by a recent $70M Series C, we’re hiring to build the next generation of clinical AI and improve the lives of clinicians and patients everywhere.

This is a great time to join us!

Nabla’s phenomenal traction is the result of 3 years of diligent product development.

Led by former Meta AI Research engineers, our team has consistently anticipated how AI can revolutionize healthcare delivery. Our Machine Learning team continually leverages the latest advancements to unlock AI’s full potential in healthcare.

Yann LeCun, Meta’s Chief AI Scientist and Turing award winner, is an advisor to Nabla.

At Nabla, product development is led jointly by Engineering and Product, and organized into cross-functional squads, each focused on a specific product area. Squads bring together product managers, designers, engineers of different profiles, and domain experts, and operate with a high level of ownership. In this role, you’ll join Engineering and collaborate across squads, contributing ML expertise wherever it has the most impact.

Now that Nabla is entering a new phase of growth and scale, we’re looking for an exceptional Staff Machine Learning Engineer to work across multiple squads, partnering with tech leads, product managers, and engineers to shape the long-term direction of our AI systems, support ML-driven features, guide technical decisions, and ensure consistency and quality across our ML stack.

• Lead and contribute hands-on to complex ML initiatives, including model design, fine-tuning and evaluation, context engineering for agents, as well as production integration, monitoring, and reliability.

• As part of Nabla’s agentic platform strategy, build the ML roadmap to support progressively more configurable and autonomous workflows in the product.

• Drive ML experimentation and evaluation frameworks, and guide decisions on approach, model and tooling, based on measurable impact.

• Define and own data strategies for training and evaluation, including dataset quality, coverage, versioning, and data acquisition or annotation when needed.

• Influence ML infrastructure and hardware decisions to ensure training and inference workloads are scalable, reliable, and cost-efficient.

• Raise the ML bar across the organization through mentorship, design reviews, and survey of relevant literature and technology, and ensure ML efforts remain aligned with product and business priorities.

• You have deep, hands-on experience with transformer-based models and LLMs in production, and are comfortable choosing between prompt engineering, fine-tuning, retrieval-based approaches, or combinations of these, depending on the problem.

• You have 10+ years of professional experience between machine learning and software engineering, with demonstrated ownership of production systems throughout their full lifecycle.

• You combine strong machine learning expertise with solid software engineering practices, and care about building reliable, maintainable, and testable systems that operate at scale.

• You have a strong data-centric mindset and experience defining data strategies for ML training and evaluation, including quality, coverage, versioning, acquisition and annotation workflows.

• You’re comfortable making ML infrastructure decisions, and have enough familiarity with cloud-based ML systems…