Machine Learning Engineer
Optimove is a global marketing tech company, recognized as a Leader by Forrester and a Challenger by Gartner. We work with some of the world’s most exciting brands, such as Papa John’s, Staples, and Entain, who love our thought-provoking combination of art and science. With a strong product, a proven business, and the DNA of a vibrant, fastly-growing startup, we’re on the cusp of our next growth spurt. It’s the perfect time to join our team of ~400 thinkers and doers across NYC, LDN, TLV, and other locations, where 2 of every 3 managers were promoted from within. Growing your career with Optimove is basically guaranteed.
You will be working within our Personalization team, helping to shape and drive the development of numerous products and initiatives that will allow our customers to personalize customer messages across all digital touchpoints. This is an exciting opportunity at the cutting edge of machine learning helping to bring Accessible Intelligence to our customers with great scope to make a key difference to Optimove’s overall platform.
We are looking for an experienced machine learning engineer to work on some incredibly interesting projects as we take Graphyte’s personalization capabilities to the next level.
Location is flexible, remote, office – it’s up to you
Best bits of the job:
- Exposure to a phenomenal array of machine learning domains including massive scale search, ranking, NLP, hybridization, classification, and far beyond.
- Fully real-time architecture for data processing and model development and deployment
- Deploying, enhancing ML frameworks, optimizing for inference, and training/retraining
- Online testing for models with live data using proprietary A/B/N testing tech to rapidly figure out what works (and what doesn’t).
- Super-bright, supportive, and friendly machine learning team to work within an environment where rapid experimentation is the norm.
- Friday Foundry – Time each week to research new methods, build and test proofs-of-concept, and deploy to production instantly if effective
- GPU support to efficiently train DL models
Role & Core Responsibilities:
- Own the model development and release process across all products and internal platforms.
- Management of AWS cloud-hosted modeling environment.
- Operationalization of models as APIs working in a real-time environment.
- Own the production monitoring system for models.
- Development of predictive machine learning models for classification and ranking purposes.
- Definition and preparation of new ML applications in close cooperation with product and development teams.
- Analysis of performance and continuous improvement and development of scoring processes hosted models.
- 2 years experience in a similar role.
- Experience with AWS environment: Athena, S3, DynamoDB, Batch, CloudWatch Rules & Logs, EventBridge, ECR, or similar with other cloud providers.
- Expert-level knowledge of Python for ML, data manipulation
- Good knowledge of SQL
- Extensive experience with Git, Bash, Docker tools, and machine learning pipelines.
- Experience of working in a real-time analytical environment, and the necessary efficiencies and trade-offs of working in such an environment.
- Experience in the use of open-source machine learning libraries like PyTorch, scipy, and SKLearn along with a good knowledge of NLP.
- Teamwork, communication skills, and hands-on approach.
- Language skills: English.
- Experience performing data analysis to identify opportunities, aid decision-making, and guide model improvements.
- Full understanding of Recommendation algorithms and their applications.
- Understanding and/or direct professional experience of working with less traditional data such as images, video, and text.
- Understanding of Personalising for sports betting and gaming, where it might add value, and what best practice looks like.
- Professional experience in personalization and/or predictive CRM, and micro-segmentation.
- In-depth knowledge of machine learning and statistics for classification and ranking on massive datasets.
- Understanding of Bayesian and/or Frequentist approaches to model comparison / statistical testing.