Senior Applied AI Engineer

Curai is looking for a Senior Applied AI Engineer to help design, build, and ship machine learning and LLM systems that influence how clinicians and patients engage with its healthcare platform. This role owns work end-to-end, from framing the right AI problem with partners to productionizing inference and validating real-world clinical and product impact.

What you’ll do

• Lead technical execution for complex AI initiatives, owning the design and delivery of solutions within a product or technical domain and partnering with senior engineers on broader architecture.
• Design, build, train, evaluate, and improve advanced machine learning and LLM-based systems for patient and provider-facing products, including areas such as conversational AI, personalization, user understanding, clinical decision support, and chronic care management.
• Own problems end-to-end: scope with clinicians and product partners, build datasets and evaluations, iterate on modeling, and ship to production with appropriate monitoring and guardrails.
• Build robust evaluation frameworks, including offline benchmarks, human-in-the-loop review, and online experiments, to ensure models are safe, accurate, and improving over time.
• Develop and enhance the team’s platform to move quickly, including data pipelines, training and inference infrastructure, prompt and model management, and tooling for clinical reviewers.
• Collaborate closely with clinicians, product, and engineering to translate medical and operational requirements into ML problems and deliver measurable improvements to patient and clinician experience.
• Set technical direction for your area, mentor other engineers, and raise engineering and scientific rigor, with leadership scope scaling with seniority.
• Stay current with the literature and the rapidly evolving AI ecosystem, bringing back what is most useful for patients and the team.

Requirements

• Bachelor’s degree in Computer Science, Software Engineering, Math, or another related technical degree.
• 3+ years of hands-on engineering experience, including 1+ years building and deploying machine learning systems with generative AI (LLMS) and a clear track record of impact.
• Strong software engineering fundamentals, with the ability to ship reliable, well-tested code in Python (or a comparable language) in production environments.
• Practical understanding of modern LLM techniques, including prompting, retrieval-augmented generation, fine-tuning, and evaluation, along with trade-offs.
• Comfort working with messy, real-world data and designing evaluations to determine whether a system is truly working.
• Strong written and verbal communication, with the ability to collaborate across disciplines including clinicians, product managers, and engineers.
• Bias toward action and ownership: driving ambiguous problems to results and helping others get there.
• Care for the mission, with motivation to translate work into better health outcomes for real patients.

Technologies

• Python
• LLMs
• Retrieval-augmented generation
• Prompting
• Fine-tuning
• Evaluation

Nice to have

• Experience applying ML or LLMs in healthcare, life sciences, or another regulated, high-stakes domain.
• Experience with clinical NLP, medical knowledge representation, or working with electronic health record data.
• Experience building agentic systems and tool-using LLMs in production.
• Experience scaling ML infrastructure, including training pipelines, distributed inference, and evaluation platforms for a small, fast-moving team.
• Track record of technical leadership, such as setting direction across teams, mentoring engineers, or publishing influential work.

Compensation and location

• Location: Remote (remote) across the U.S.
• Base salary: USD 175,000 - 200,000 per year
• Actual base salary depends on qualifications and years of relevant experience.

Benefits

• High-ownership work on problems that matter, with a tight feedback loop from real clinicians and patients.
• A small, senior team where work shows up in the product quickly.
• Competitive compensation, meaningful equity, and comprehensive benefits.
• Remote-first, flexible work environment across the U.S.
• Comprehensive medical, dental, and vision coverage.
• Flexible spending plans.
• Generous and flexible Paid Time Off (PTO), floating holidays, and parental leave.
• 401k plan with employer matching.
• 100% remote, work from home.