Talent pool: Senior Machine Learning Engineer

Responsibilities

• Engage with clients to understand their business requirements and provide expert advice on leveraging ML and AI technologies to solve their problems.
• Design end-to-end ML solutions that address client needs, considering factors such as data acquisition, preprocessing, feature engineering, model selection, and deployment.
• Architect scalable and reliable ML systems that can handle large volumes of data and real-time processing. Ensure the solutions are robust, secure, and scalable, taking into account performance, latency, and cost optimization.
• Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts to deliver successful ML projects.
• Stay up-to-date with the latest advancements in ML, AI, and related technologies, identifying opportunities for innovation and differentiation.
• Conduct research and experimentation to explore new ML algorithms, techniques, and frameworks that can enhance the company's offerings.
• Present findings and results to internal teams and external stakeholders in a clear and concise manner.

Requirements

• 3+ years of experience as an ML Engineer or Data Scientist, either in academia or industry.
• Proficient in Python programming and experience with Python data science frameworks. Strong programming skills with proven experience in implementing Python-based machine learning solutions.
• Familiarity with common ML frameworks (e.g., PyTorch, Keras) and libraries (e.g., NumPy, scikit-learn).
• Experience with LLM agents including tool using and reasoning, for instance, the combination of RAG solution and code interpreter.
• Experience with LLM fine tuning.
• Solid knowledge of machine learning and deep learning fundamentals.
• Experience with transformer-based language models.
• Ability to interpret and implement research ideas and algorithms.
• Hands-on experience with relational SQL and NoSQL databases.
• Upper-Intermediate or higher level of English proficiency.
• Ability to work with external clients and strong communication skills, including presenting in webinars and conferences.
• Ability to mentor team members and assist in their professional development.
• Quick learner with the ability to adapt to new technologies, frameworks, and algorithms.

Nice to have

• Experience with designing complex multi-model and multi-modal ML applications and products.
• Solid foundation in development of data analytics systems, including data exploration/crawling, feature engineering, model building, performance evaluation, and online deployment of models.
• Experience with cloud-based tools and technologies for data pipelining, model development, and deployment, particularly AWS (Amazon Web Services).
• Familiarity with AI/ML operational tools such as Airflow, MLFlow, H2O, etc.
• Experience with MLOps tools and frameworks like Jupyter Notebook, Kubernetes, Kubeflow, Spark, etc.
• Experience in building scalable AI/ML systems for continuous training automation, computer vision, natural language processing, or similar advanced AI/ML problems.
• Engineering experience in model tuning using CUDA/OpenCV, C++, low-level Python scripts, etc.
• Up-to-date publications in the areas of deep learning, distributed computing, bioinformatics, or other life sciences.
• Domain knowledge of biology and/or chemistry.

Benefits

• Competitive compensation.
• Remote or office work.
• Flexible working hours.
• Healthcare benefits: medical insurance and paid sick leave.
• Continuous education, mentoring, and professional development programs.
• A team with excellent tech expertise.
• Certifications paid by the company.