Asst/ Research Engineer (Machine Learning / Deep Learning)

Company description:

Tan Tock Seng Hospital

Job description:

About the job

We are looking for solution-oriented software engineers for machine learning (ML) / deep learning (DL). Here is your chance to shape the future with us! Our team develops AI for ophthalmological applications. We are leading the change in telemedicine for home monitoring and our machine learning engineers are at the forefront of this development. By leading critical projects and initiatives that have never been done before, you will have the opportunity to help us improve the way people perform eye exams.

Job Title: Asst / Research Engineer (Machine Learning / Deep Learning) - Clinical Research Coordinator

The ideal candidate should have industry experience working on a range of recommendation, classification and optimisation problems. You will be working with the world's leading retinal ophthalmologists to solve some of the most challenging ocular data and prediction problems for maculopathy.

Full-time, solution-oriented software developer who is versatile and has strong knowledge of Python and/or the R language. Experience with web development is a plus. The candidate must have a background in data science, deep learning and machine vision. The role is varied and challenging and requires initiative, resourcefulness and problem-solving skills. Applicants must have a passion for analytical software development and a good understanding of different platforms and SDK environments.

Additionally, the candidate will also be required to plan, conduct and co-ordinate research projects and clinical trials being conducted in TTSH.

Engineering Roles & Responsibilities (30%)

1. Design and develop ML systems: Create scalable machine learning systems tailored to clinical problems.
2. Adapt standard machine learning methods to optimally utilize modern parallel environments (e.g. distributed clusters, multicore SMP and GPU).
3. Re-evaluate the trade-offs of already delivered features/ML systems and be able to drive large efforts across multiple teams to reduce technical debt by starting from first principles where appropriate.
4. Test and evaluate: conduct experiments to evaluate model performance, perform A/B testing and iteratively improve model accuracy.
5. Develop highly scalable classifiers and tools that utilize machine learning, data regression and rule-based models.
6. Propose, gather and summarize requirements and create an effective feature roadmap.
7. Deliver data science code in collaboration with the clinical team.
8. Support the Principal Investigator (PI) in all software coding work required for the primary objective.
9. Create and compile all technical specifications, flowcharts, architectures and code descriptions.

Clinical Research Coordinating Responsibilities (60%)

1. Planning and coordination of research projects/clinical trials which include patient recruitment, dispensing investigational medication, follow up with research visits and research related activities or procedures.
2. Maintenance of database and statistical support.
3. Data collection from case-records/patient interviews.
4. Assist in monitoring of all research projects/clinical trials.
5. Overseeing of assigned research projects/trials coordinated by the RA/IRRC in respective research areas.
6. Overseeing all on-site visits by external visitors.

GENERAL (10%)

1. Co-ordinating meetings for research committee.
2. Planning and making posters/papers for research submissions.
3. Any other tasks assigned.

Required Skills and Qualifications

1. Education: Bachelor's in computer science, mathematics, statistics data science, artificial intelligence, life sciences, biomedical science, psychology or a related field.
2. Minimum 1 year working experience.
3. Familiarity with Python development environments, machine learning and machine vision.
4. Prior experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining, artificial intelligence, or a related technical field.
5. Programming skills: Very strong knowledge of programming languages such as Python or R.
6. Framework knowledge: Familiarity with machine learning libraries such as TensorFlow, Keras, PyTorch or scikit-learn.
7. Analytical skills: Strong problem-solving skills with a solid understanding of algorithms, statistics and probability.
8. Communication Skills: Excellent verbal and written communication skills to make technical concepts understandable to non-technical stakeholders.
9. Familiarity with the development of RESTful APIs and database management (NoSQL/RDBMS).
10. A high level of confidence in dealing with ambiguity in early stage research projects.
11. Good communication skills and ability to work independently.
12. To fulfil domain specific review board and institutional requirements e.g CITI, GCP and vaccinations.