Machine Learning Engineer

3 giorni fa

Torino, Italia SYNERGEN Health A tempo pieno

Join to apply for the Machine Learning Engineer role at SYNERGEN Health.
SYNERGEN Health is a US-based company that pioneers comprehensive financial solutions for healthcare organizations, providing tech-enabled services, advanced analytics, FinTech payment solutions, machine learning, consulting, and software solutions. The company was ranked among the 5000 fastest-growing private companies in the US for five consecutive years by Inc. The solutions team was formed with a mission to transform the U.S. healthcare industry with breakthrough products using the latest technology and an agile approach.
Job Role And Responsibilities

Develop and train machine learning models to solve defined business or research problems, using appropriate algorithms and techniques.
Build and maintain data pipelines that feed data into ML models, including data collection from databases or APIs and preprocessing steps.
Deploy models into production environments (cloud or on-premises) and create APIs or interfaces for other applications to interact with these models.
Monitor and evaluate model performance in production, tuning parameters or updating data to improve accuracy and efficiency.
Collaborate closely with data scientists, software engineers, and IT to integrate ML solutions seamlessly into the broader system.
Write clean, efficient code, use version control (e.g., Git), perform code reviews, and incorporate best practices in development.

Required Qualifications

Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field (Master’s degree a plus).
1‑2+ years of hands‑on experience in machine learning engineering or software development focused on data/ML projects.
Proven experience developing machine learning models, including training, validation, and evaluation techniques.
Strong programming skills in Python, with experience in ML frameworks such as TensorFlow, PyTorch, scikit‑learn, etc.
Experience with databases (SQL or NoSQL) and large datasets; familiarity with cloud platforms (AWS, Azure) or big data tools is advantageous.

Skills And Competencies

Technical Skills: Docker, Flask, FastAPI, SQL, NoSQL, AWS/GCP, Prometheus, Grafana, XGBoost.
Machine Learning Expertise: Solid understanding of algorithms and when to apply them.
Software Engineering: Production-quality code, performance optimization, testing, and containerization.
Data Handling: Manipulation and analysis of noisy or unstructured data.
Problem‑Solving: Analytical thinking, troubleshooting, and creative solutions.
Communication & Teamwork: Explaining ML concepts to stakeholders and collaborating across teams.
Continuous Learning: Staying up-to-date with latest developments in ML and AI.

Career Progression
A successful ML Engineer can be promoted to Senior ML Engineer, specialize further, or advance into leadership roles such as Technical Lead, guiding junior engineers and taking responsibility for system design and project leadership.
Seniority Level
Entry level
Employment type
Full‑time
Job function
Engineering and Information Technology
Industries
Hospitals and Health Care
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