Machine Learning Engineer, Wisedocs
+ Stock options
2-3 days a week in office
AI processing for medical documents
Be an early applicant
AI processing for medical documents
21-100 employees
Be an early applicant
+ Stock options
2-3 days a week in office
21-100 employees
Company mission
To erase the headache of manual work to create a more positive experience for insurance companies, legal firms, healthcare providers, and their claimants.
Role
Who you are
- Have a minimum of 3 years of experience in software development, with a strong focus on Python programming
- Possess professional working experience with LLMs, RAG, transformers, BERT
- Having demonstrated a solid foundation in data structures, algorithms, and software engineering principles, with a track record of developing high-quality, maintainable code
- Should be familiar with recent model developments and frameworks, i.e. LlaMa, Mistral, GLINER, TGI, etc
- Proven Machine Learning industry experience, preferably at scale
- Prior professional experience with LlamaIndex is preferred
- Prior experience with transfer learning and model modifications
- Excellent problem-solving skills, while exhibiting high enthusiasm to tackle complex challenges in AI/ML development and deployment
- Are enthusiastic about working in a fast-paced, innovative environment, contributing to a team that aims to make a significant impact in the medical and insurance tech space
- Possess good understanding on leveraging Azure's AI and machine learning services to deploy, monitor, and maintain ML models, ensuring high availability and performance
What the job involves
- Join Wisedocs AI as a Machine Learning Engineer, where you will be at the forefront of developing advanced AI/ML capabilities and building V2 of our platform's model serving
- This role is ideal for a developer who excels in Python and has an established track record in tackling and developing leading-edge ML solutions, building models with real-time inference, ML Ops and best practices, and a combination of LLM and encoder-based work
- We currently have two types of ML systems running in production, an entity classification system and a RAG summarization system
- Our entity classification has been in production for 4 years and currently processes data asynchronously with a combination of models
- Drive the rebuild and re-architecture of our entity classification system to have higher throughput, reliability and observability
- Design with best practices in mind for growing and evolving company
- Collaborate with other senior engineers to integrate the ML system into the broader Wisedocs platform
- Designing and implementing machine learning models to analyse and interpret large datasets of medical and insurance documents, enhancing our platform's decision-making and efficiency
- Developing robust, scalable backend services in Python, ensuring seamless integration with our AI/ML components and front-end systems
- Collaborating with other technical stakeholders and leaders to actively work towards the design and implementation of systems
- Ensuring the reliability and scalability of ML systems, implementing best practices in data engineering and model lifecycle management
- Staying abreast of the latest developments in AI/ML technologies and Azure services, continually seeking ways to optimize our platform and workflows
- Conducting rigorous testing and validation of ML models, ensuring accuracy, efficiency, and alignment with business objectives
- Coordinating with cross-functional teams to understand requirements and translate them into effective AI solutions within our platform
- Other duties and responsibilities will be assigned as projects develop, adjust and mature
Application process
- Round #1 – HR (Quick Prescreen)
- Duration: 20-30 minutes
- Focus: High level Get-to-Know-You, Culture fit, High level technical expertise and tech stack
- Round #2 - Technical
- Duration: 30-45 minutes
- Focus: Conceptual
- Round #3 - Technical
- Duration: 1-1.5 hours
- Focus: General Coding
- Round #4 - Hiring Manager
- Duration: 1 hour
- Focus: Culture fit, experience, high level technical questions
Our take
Wisedocs was born out of frustration with one of the healthcare administration's most cumbersome tasks: medical record management. Its automated processing software tackles this head-on, aiming to contribute to better overall healthcare by freeing up valuable manpower and time currently spent on paperwork.
Wisedocs has successfully developed an AI-powered solution that demonstrably outperforms traditional methods of handling unstructured medical documents. Hence, the company streamlines the process of clinician notes, medical briefs, records, assessments, and discharge summaries.
Backed by prominent investors, Wisedocs successfully launched in the US in 2022, followed by the opening of a new American headquarters to support its growing US customer base. This was followed by further funding in 2024. It believes its product can significantly reshape the American healthcare and insurance landscape, where streamlining paperwork and unlocking critical data are major concerns.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Few candidates hear
back within 2 weeks
Company
Funding (last 2 of 6 rounds)
Aug 2024
$3.3m
EARLY VC
Jan 2024
$9.1m
SERIES A
Total funding: $19.2m
Company benefits
- Healthcare: 100% company paid healthcare benefits. Benefits across health, dental, vision, and extended services plus sick days
- Vacation: Generous paid time off for all employees to ensure you can step back and avoid burnout and stress
- Remote-First: A flexible remote-first work environment with a large office space in the heart of downtown Toronto to use as you see fit
- Stock Options: We offer you stock options so your investment in your career will pay off
- Referral Bonus: We reward those who refer great team members our way. Get a referral bonus for successful onboarding
- Events: Gather regularly for fun activities as a team completely sponsored by Wisedocs
Company HQ
Downtown Toronto, Toronto, ON
Articles
Leadership
Connor Atchison
(CEO)Previously served in the Canadian Armed Forces Infantry before moving into Healthcare Administration within the Armed Forces.
Salary benchmarks
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