Proudly voted a Great Place to Work®, we are a dynamic startup in the CPaaS (Communication Platform as a Service) space that is revolutionizing the way businesses communicate. Our team is made up of 400+ energetic and passionate Unifones who are dedicated to delivering the best possible experience to 5000+ customer-centric companies.
We pride ourselves on our fun and collaborative work environment, where creativity and new ideas are constantly encouraged. As shareholders in the business, we’re so much more than a group of passionate communicators. We are Unifones. Join our team and be a part of something big!
Meet the team!
Our Engineering team is responsible for designing, developing, and maintaining the systems and technologies that drive Unifonic’s solutions. We work closely with other departments to ensure our products and services meet the needs of our customers. If you are passionate about technology and are excited about working on cutting-edge communication and engagement solutions, we want you on our team.
As a Machine Learning Engineer, you’ll be responsible for delivering ML models to serve use cases like NLP, speech tagging and recognition, text classification, Named Entity Recognition, and semantic extraction. The successful candidate should have a solid technical background in Machine Learning with proven hands-on experience delivering similar projects.
Help us shape the future of communication by:
Fully Understanding technical requirements, challenging them, and producing the most appropriate implementation.
Discussing with product managers about product features.
Designing and implementing ML pipelines from ideation to production.
Analyzing, processing, and interpreting data.
Building and training ML models along with tools to update/retrain those models which become a part of customer-facing products.
Working with other software developers to guarantee Models implementation in production.
Being a role model in agile practices.
Producing technical documentation for encountered problems and maintaining team technical decisions.
What you'll bring:
Hands-on 5-7 years of relevant work experience in shipping ML models for NLP, CV, classifiers and recommenders for large-scale customer-facing projects.
Experience in Python with experience in common data science toolkits, such as NumPy, Pandas, PySpark, Scikit-Learn, TensorFlow, PyTorch, Keras, rasa, BERT, spaCy.
Hands-on experience in NLP is mandatory; e.g. Text representation (n-grams, a bag of words, TF-IDF, etc), feature extraction, part of speech tagging and recognition, text classification, Named Entity Recognition (NER), semantic extraction techniques, Machine Translation, slot filling, Sentiment analysis, etc.
Familiarity with MLOps best practices, e.g. Model deployment and reproducible research.
Mastering data science needed skills like SQL, hypothesis testing, Data cleansing, data augmentation, data pre-processing techniques, dimensionality reduction, mathematics, probability, and statistics (e.g. conditional probability, likelihood, Bayes rule, and Bayes nets, Hidden Markov Models, etc).
Excellent understanding of Machine learning techniques like Naive Bayes classifiers, SVM, Decision Tree, KNN, K-means, Random Forest, modeling and optimization, evaluation metrics, classification, and clustering.
Solid understanding of software engineering fundamentals, data structures, algorithms, and data modeling.
Familiar with code versioning tools such as GIT, CI/CD concepts, and toolchains.
Acquainted with agile methodologies like scrum, and agile tools like Jira.
Experience analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, etc.
Good knowledge of Deep Learning needed skills like Neural network architectures, fully connected networks, CNNs, LSTMs, and RNNs.
- Worked on other ML use cases like:
Speech Recognition algorithms.
Recommender Engines.
Anomaly detection.
Computer Vision (Face Recognition, OCR, etc).
Familiar with managing Linux servers and applications.
Familiar with SaaS and PaaS integration architecture and applications.
Bachelor’s degree in a relevant field. (e.g. Computer Science, Computer Engineering, Software Engineering, etc).
As a Unifone you’ll receive a range of benefits:
Competitive salary and bonus.
Unifonic share scheme (we are all owners!).
30 holiday days after the first anniversary.
Your Birthday off!
Spend up to 10 weeks per year working from anywhere in the world!
Paid leave for new parents.
Linkedin learning license.