Machine Learning Engineer (Full-time, Internship)

Location

London, UK (Hybrid)

About us

Raia Health is an early-stage startup using behavioural data to deliver supportive care to patients through a responsible AI-first approach. We are a dedicated group of digital health researchers, machine learning scientists, and clinicians from premier institutions in research and industry (Harvard, Stanford, DeepMind, and Memorial Sloan Kettering Cancer Center) and are backed by leaders in artificial intelligence. 

About the role

We are on the search for a talented Machine Learning Engineer to help us develop machine learning methods for building reliable and engaging patient-facing experiences. This role is intended for candidates with a proven history of building and deploying impactful machine learning tools. These are skilled engineers with a solid machine learning foundation and extensive experience in natural language processing. We’re looking for candidates with strong software engineering skills, an excitement for machine learning research, and a deep passion for building reliable systems for high-stakes, real-world applications.

As an early-stage startup, our close-knit team thrives on robust communication and collaboration across different disciplines. Joining us means being a part of an adept group of ML researchers and engineers, where you’ll work closely with our Chief Scientist and regularly liaise with the broader team. As a team, our central goal is consistent alignment to our mission — delivering continuous supportive care to patients.

What you’ll do

Help build and maintain a platform for continuous patient engagement. Specifically, you will

• Maintain and scale backbone infrastructure responsible for handling user engagement and conversation.
• Manage models for domain-specific applications and conversational nuances.
• Iterate and maintain frameworks for gathering consistent user feedback.
• Develop tools for evaluating and monitoring performance of deployed real-world experiments.
• Implement and scale training and inference engineering frameworks.
• Develop a deep intuition of user needs via direct user feedback and clinical research.
• Report and present developments directly to Leadership and other team members.
• Engage in collaborations among team members to readily meet on-going goals.

Stay up-to-date on the latest of machine learning and clinical research

• Prepare a monthly talk on relevant machine learning research and advances that excite you
• Attend machine learning and clinical conferences at the team’s favorite venues

What we’re looking for

You are a determined and caring problem solvers excited and eager to make a impact in healthcare. Additionally, you have

• A bachelor’s, master’s or higher in Computer Science, Software Engineering, or a related field.
• 2+ years of industry experience in relevant roles.
• A proven track record in software engineering, particularly in building, maintaining, and deploying ML-based applications.
• A expertise in machine learning, with extensive experience in natural language processing.
• Familiarity with evaluating and deploying machine learning models.
• Expertise with programming languages such as Python, and machine learning frameworks such as TensorFlow, PyTorch, and HuggingFace.
• A deep interest in building safe and reliable AI tools for real-world applications.

Bonus points ✨

• Experience in collaborating and working with researchers in an interdisciplinary setting.
• Experience working with real-world datasets.
• Previously worked in a healthcare setting or an early-stage startup.

Apply

Please email us at inquiries@raiahealth.com with include a cover letter and a resume/CV.

We competitive compensation and benefits. Additionally, we offer a hybrid work model.

Our Culture

At Raia Health, we are staunch advocates for an inclusive and supportive workplace. We particularly welcome applicants from historically under-represented backgrounds. Our commitment to equality is unwavering, irrespective of race, religion, national origin, gender, sexual orientation, age, veteran status, or disability.