Job description
About Docsumo:
Docsumo is your go-to Document AI solution for streamlining business operations. We turn complex documents like bank statements, policies, and financial statements into valuable, actionable data. Our cutting-edge technology helps businesses make smarter decisions faster. We are backed by marquee investors such as Sequoia, Barclays, Fifth Wall, Common Ocean, and Techstars.
At Docsumo, we’re on a mission to revolutionize how businesses handle data. We empower companies to:
Boost efficiency by 6-10 times
Make quick, accurate decisions from unstructured information
Scale operations effortlessly through innovative technology
The opportunity as Senior Machine Learning Engineer:
- We are seeking senior professionals with over 4 years of experience in the field. This is a role for candidates with a proven track record in machine learning, deep learning, NLP, and computer vision.
- If you have led and managed a team of ML scientists and engineers and have a strong foundation in deploying end-to-end ML solutions, this opportunity is for you.
- This role offers the chance to rapidly advance into leadership positions, such as Lead ML Engineer, where you’ll spearhead critical projects with creative autonomy.
- You’ll work closely with our CTO, Data Science, and Engineering teams, shaping the future of intelligent document processing for our expanding client base in the US.
- This is a full-time role with flexible options, including hybrid work in Kathmandu or remote work from India.
- Working hours are 10:00 am to 7:00 pm IST, with a 1-hour lunch break.
- You will report directly to the Data Science Lead or the CTO, collaborating with a talented team to deliver innovative ML solutions that drive customer success.
Key Responsibilities
- Collaborate with cross-functional teams of scientists and engineers to design, develop, and implement advanced machine learning systems that transform innovative ideas from conceptual stages into operational APIs.
- Conduct cutting-edge research in machine learning, focusing on the application and fine-tuning of Large Language Models (LLMs) to develop robust, scalable solutions for intelligent document processing.
- Lead a team of data scientists and machine learning engineers, providing mentorship and fostering a culture of collaboration and continuous learning to achieve high performance and innovative outcomes.
- Plan, manage, and oversee the full lifecycle of ML projects, ensuring alignment with business goals and timely delivery of high-quality solutions. Develop and apply sophisticated machine learning algorithms to address complex business challenges, particularly those involving the processing and analysis of unstructured data.
- Engage in Agile development processes including regular standups, sprint planning, and retrospectives to facilitate iterative progress and maintain high standards of output.
- Ensure the documentation of machine learning methodologies, model development processes, and maintain rigorous testing standards to ensure reliability and efficiency of models in production.
- Drive the integration and optimization of LLMs and other advanced models to enhance performance and operational efficiency, continuously seeking improvements.
Need to Have:
- At least 4 years of industry experience in machine learning, deep learning, NLP, and computer vision, ideally within tech companies, product startups, or R&D environments.
- 1-2 years of experience leading teams of 4-5+ ML scientists and engineers, demonstrating effective leadership and project management skills. Proficiency in PyTorch and TensorFlow, with experience in training deep neural networks, implementing transfer learning, and optimizing models.
- Strong skills in classification and regression techniques, with hands-on experience in Scikit-learn, Numpy, Pandas, and Scipy. Practical experience with Transformers, such as BERT and GPT, for tasks like text classification, entity recognition, and sentiment analysis.
- Proficiency in Python and strong understanding of Object-Oriented Programming (OOP) principles.
- Experience with version control using Git, cloud platforms like AWS and Google Cloud, and containerization technologies such as Docker and Kubernetes.
- Ability to work effectively in a team, demonstrating motivation, resourcefulness, and a growth mindset.
- Bachelor’s degree in Computer Science, Statistics, Machine Learning, Physics, Mathematics, or a related field.
Nice to Have:
- Experience with Large Language Models, including techniques like PEFT, prompt engineering, few-shot learning, and RLHF.
- Knowledge of computer vision applications like OCR, OpenCV, CNNs, and multimodal AI.
- Experience with advanced data visualization libraries.
- Familiarity with Agile development practices and sprint management.
- A track record of innovative research contributions or publications in the machine learning domain.
