ML Customer Solutions Engineer

Company Description

HOPPR is at the forefront of innovation in medical imaging, developing the first multimodal AI foundation model. Our deep learning platform, unique for its proprietary privacy-compliant trust architecture, integrates diverse data sources with cutting-edge AI/ML development. HOPPR is co-founded by Dr. Khan Siddiqui, a visionary leader with a prolific background including founding higi, former roles at Hyperfine (NASDAQ:HYPR), and Microsoft.

Role Description

The Customer Solutions Engineer will support healthcare organizations in integrating machine learning foundation models into their radiological clinical software, assisting with model fine-tuning, prompt engineering, and pre-sales activities such as delivering demos. This role requires a blend of technical expertise, client engagement, and strong collaboration and communication skills to ensure successful implementation and optimal clinical outcomes.

Key Responsibilities

• Fine-Tuning:
  • Collaborate with clients to fine-tune foundation models for specific radiology tasks (e.g., anomaly detection, image segmentation) using various fine-tuning methodologies such as prompt engineering, parameter-efficient fine-tuning, etc.
• Pre-Sales Support and Demos:
  • Partner with client experience to deliver demos and facilitate technical discussions, highlighting the foundation model’s capabilities to potential clients.
• Client Support and Integration:
  • Serve as the primary technical contact for our clients, working closely with application developers and serving as a trusted advisor to implement solutions using our foundation models.
• Workflow Optimization:
  • Ensure AI models fit seamlessly into radiological workflows, optimizing outputs for clinical decision-making.
• Cross-Functional Collaboration:
  • Work closely with product, engineering, and client engagement teams to address client needs and feedback.
  • Assists in translating complex technical findings into actionable insights and recommendations for non-technical stakeholders, contributing to impactful business decisions.
• Documentation:
  • Assist with development of technical materials, including integration guides, training documents, and best practices for model fine-tuning.
  • Solicit, summarize and document platform and tooling requirements and feature requests for the product development team.
  • Preparation of model details to be used for client regulatory documentation as it relates to specific fine-tuning use cases.

Minimum Qualifications (Knowledge, Skills, and Abilities)

• Experience: 7-10 years’ total experience, including 3-5 years in a technical client-facing role with previous experience working on machine learning projects and industry knowledge of standard technologies in the machine learning space. 
• Technical Skills: Proficiency in Python, machine learning frameworks (e.g., TensorFlow, PyTorch), and understanding of radiological software (PACS, DICOM). 
• Communication: Strong ability to explain and present complex technical concepts to both technical and non-technical stakeholders. 
• Education: B.S. degree in a quantitative field such as Computer Science, Engineering, or comparable degree/experience. 

Preferred Qualifications

• Experience with generative LLM fine-tuning and prompt engineering 
• Experience with cloud native architecture, including machine learning model development and deployment, in a client-facing or support role. 
• Experience with RESTful APIs, radiology workflow orchestration tools, and medical interoperability standards is a plus.  
• Familiarity with pre-sales processes, including delivering demos, gathering requirements, and customizing solutions for clinical environments. 
• Previous experience within a team that underwent a high growth stage. 

What We Offer

• A key role in a fast-growing startup with immense potential.
• An innovative, collaborative, and supportive work environment.
• Competitive salary and benefits package. 

Essential Job Functions

HOPPR is committed to providing reasonable accommodation to employees with disabilities, as required by law. We encourage those with disabilities to request accommodations if needed to perform the essential functions of the job.

The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

• While performing the duties of this job, the employee may be regularly required to stand, sit, talk, hear, reach, stoop, kneel, and use hands and fingers to operate a computer, telephone, and keyboard.
• Specific vision abilities required by this job include close vision requirements due to computer work.
• Regular, predictable attendance is required; including quarter-driven hours as business demands dictate.
• Some travel is required.

The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

• Ability to sit at a computer terminal for an extended period.
• Moderate noise (i.e., phone calls, online meetings, computer audio).

HOPPR is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

Important Note: This opportunity is open exclusively to US citizens and permanent residents. We kindly request that recruiters and agencies refrain from contacting Dr. Khan Siddiqui or any HOPPR team members directly regarding this role. Unrequested outreach from recruiters will not be entertained or responded to. Thank you for respecting this directive and helping us maintain a focused and efficient hiring process.