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
We offer
- 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 an excellent tech expertise
- Certifications paid by the company
