Machine Learning Engineer
We believe that properly collected and analyzed data is inherently honest buy often businesses only have access to narrow slices of data about their customers or audiences, or the data they have looks only at what people say and not what they do.
That’s why Helixa has developed technology to look at groups of people, from every angle and dimension, and delivers a way to get more insight into both their hearts (behavior, interests, beliefs) and minds (stated intentions) than you’ve ever had.
We have infused our AI with the most current research into human behavior and given it access to the latest demographic, engagement, and census data to enable truly representative, uniquely insightful, deeply human results. AI offers the ability to find patterns and connections at unparalleled depth, scale, and speed, providing insight in minutes not days. With a detailed understanding of your customers or audience, you will be better able to please them, move them, and build more profitable relationships… everyone wins.
The AI team at Helixa is at the forefront of innovation in advanced analytics and machine learning, including deep learning and more traditional algorithms.
Our team is composed by top class scientists, engineers and domain experts driving the data science capabilities of our products end-to-end, including: data ingestion and processing, exploratory analysis, modeling, validation, visualization, tuning and automation.
We alternate the development workflow with research spikes on state-of-the-art machine intelligence applied to understanding and generalizing people behaviour and affinities.
Our R&D strives to cover a full spectrum of topics: probabilistic programming, cross-domain adaptation, natural language processing, computer vision, generative models, scalable distributed systems and GPUs hardware optimizations just to name few.
Artificial Intelligence at Helixa raises both scientific and engineering challenges. We deal with a very large amount of data and rely on various data management and distributed computing technologies (Spark, MongoDB) deployed into cloud infrastructures (AWS). We utilize a diverse number of languages (Python, Scala) and tools (PyData stack, MLlib, TensorFlow, Keras, PyTorch). Periodically, we work side by side with the core development team for maintaining the data lake and for deploying solutions in production with the minimum overhead.
The ultimate goal is to help the company growing on three major areas:
- Understanding consumer characteristics, preferences and lifestyle.
- Predicting latent variables and future outcomes (e.g. hidden traits, migrations, market indicators).
- Augment with unobserved patterns (e.g. generate synthetic consumers population, latent factors fusion, personalizations).
In addition to the core business, 10% of team time on a weekly basis is devoted to learning and experimenting with latest advances in Artificial Intelligence, Deep Learning, Sociology and Data Technologies.
A Machine Learning Engineer at Helixa turns scientists' findings into services or product features ensuring the design, quality and scalability at every stage of implementation.
He/she is in charge of expanding and optimizing our cloud infrastructure, managing machine learning pipelines consuming large datasets daily, optimize and tune shallow and deep learning models, scale the R&D of our AI technology as well as the operations required to deploy algorithms at scale.
Besides that, our scientists and engineers work as a team and are responsible for the whole end-to-end process, from research to production.
The ideal candidate is a senior engineer with experience in building robust pipelines and large-scale deployment of machine learning systems in the cloud.
The position is based in our rapidly growing R&D office in Milan (Italy) @ Talent Garden Merano.
- Develop intelligent systems based on traditional algorithms as well as the latest AI technologies and deep learning.
- Implement and optimize algorithms using open-source libraries such as numpy, scipy, pandas, tensorflow or torch.
- Speed-up and make more efficient the parallel computing leveraging the cloud infrastructure (distributed computing and GPUs).
- Build tools for supporting experiments, development and debugging of deep and shallow machine learning models.
- Build sanity checks and dashboards for monitoring model performances
- Build robust workflows for training, evaluation and inference at scale.
- Automate the deployment and operations leveraging the latest AWS cloud services.
- Ensure engineering and programming practices among the whole team.
- Work closely with the Chief Scientist, CEO and Product Owner to design new features to implement to improve the functionalities and user experience of the platform.
- Master’s degree or above in computer science or software/computer/IT engineering fields.
- 2 years experience, or a comparable industry career, in building production systems for software development, data science, data engineering, artificial intelligence, deep learning or similar.
- Working knowledge of Python.
- Experience with parallel and distributed computing technologies (Spark, multi-threading systems, GPUs).
- Understanding of popular Machine Learning techniques and ability to apply it to real problems.
- Ability to prototype and test suboptimal solutions quickly and iterate up to a final product that can be deployed in production.
- PhD with a proven track record of publications in Computer Science, Machine Learning, Deep Learning, Computer Vision, NLP, Pattern Recognition or similar.
- Good knowledge of parallel computing using GPUs and CUDA.
- Familiar with functional programming and Scala.
- Experience with large volume ETL or data streaming.
- Experience with microservices and REST APIs.
- Familiar with agile development and lean principles.
- Contributor or owner of GitHub repo.
- Competitive salary.
- Free lunch delivered daily.
- Personal budget for conferences and training.
- Flexible working hours.
- Startup atmosphere with the usual perks.
- Regular team building activities.