ML Engineer at Swish
San Francisco, California, United States
🇺🇸 (Posted Nov 28 2018)
About the company
Launched in February 2013, Swish is a fast-growing business with an innovative working culture and teams spanned across the world with teams in Toronto, San Francisco, Berlin, Auckland, Bruxelles, Medellin, and more.
We create products for successful business using cutting-edge technologies: Blockchain, Machine Learning, and Apps Dev. Working with Swish puts you in contact with prestigious brands, wherever your base is. We are a 100% remote-work company because we believe it is everyone’s choice to live and work the way they prefer.
Work is organized in sprints - 2 weeks periods to which, as a member of our talent community, you choose to commit. You always have the choice to accept or decline a sprint, or take-on multiple sprints simultaneously.
We let members choose what suits them best depending on their current situation: family, travel, studies, finance. We know life is not linear and we respect the humans behind the screens.
Our work ethic relies on six core values: Transparency, Directness, Meritocracy, Autonomy, Responsibility, Continuous Learning.
Ensuring a diverse and inclusive workplace where we learn from each other is core to our values. We welcome people of different backgrounds, experiences, abilities, and perspectives. We are an equal opportunity employer and a fun place to work.
Join the future of work today.
Do they allow remote work?
Remote work is possible, see the description below for more information.
Use your extensive knowledge of machine learning to transform the way enterprises run their businesses. With a healthy pipeline of projects ranging from insurance modeling, call center automation, social listening, and text analytics, we are looking to bring on passionate experts to solve the challenges of automation at scale and help our clients capitalize on the power of machine learning and data science.
The Machine Learning Engineer role is responsible for building AI systems that can achieve unprecedented levels of performance. This requires designing, implementing, and improving distributed machine learning systems at large scale with quality code, and leveraging the science behind the algorithms employed.
A Typical Week
• You'll brainstorm with Product Managers and Designers to conceptualize new features.
• You'll collaborate with backend engineers to build new features a client.
• You'll learn about new ml technologies and discuss potential solutions to problems.
• You'll help our skilled support team triage bugs and troubleshoot production issues.
• You'll mentor other engineers and deeply review code.
Skills & requirements
You'll be tasked with developing machine learning techniques and applying them at scale to our projects. We look for the following attributes in candidates:
• Experience building production AI applications.
• Strong communication skills.
• Ability to create implementations of deep learning algorithms from research papers.
• Experience with machine learning toolkits like scikit-learn, Keras, NLTK, numpy, scipym, R, Weka, and Matlab.
• Experience with deep learning frameworks such as TensorFlow, Torch, or Theano.
• Expertise in reinforcement learning and / or deep learning for image datasets.
• 1+ years working with large datasets using Spark, MapReduce or equivalent
• 1+ years building backend system using Java / Scala / Python. Solid knowledge of statistical classifier models (HMM, SVM, deep/recurrent ANN, CRF, LMT, etc.) and of best practices in attribute selection, dimensionality reduction, and runtime performance optimization.
• Knowledge of NLP techniques such as PoS tagging, NP chunking, shallow/deep parsing, and NER.
• Knowledge of IR concepts such as statistical search ranking, knowledge graphs, vectorial semantics, LSA, and document clustering.
In all cases, you should be motivated by a desire to solve the most important problems and obtain unprecedented results and eager to push your methods to their maximal performance.
• Advanced degree in Statistics, Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, Operations Research, or a related field.
• Bachelor or Masters in computer science, mathematics, physics, machine learning or equivalent. B.S., M.S. in Computer Science or Machine Learning.
• Robotics: while working towards breakthroughs in robotic manipulation, develop novel techniques for reinforcement learning and imitation learning.
• Basic research: develop new and improved methods for generative models and unsupervised learning, reinforcement learning, evolutionary algorithms, and meta-learning.
• Track record of coming up with new ideas in machine learning, as demonstrated by one or more major publications or projects.
• Experience in small startup environments helping large enterprises.
• Experience working with a team, especially a distributed team.
• Professional experience designing applications.