Staff Machine Learning Engineer

Date: Sep 12, 2023

Location: Dhaka, BD, 1213 BD

Company: Optimizely

Optimizely is known for content, commerce, and optimization with our Digital Experience Platform (DXP). Millions of experiences are served with our platform every single day, helping organizations grow exponentially online. We have the honor of serving some incredible customers – which makes what we do extremely rewarding. Optimizely has over 9,000 brands, from global organizations such as Visa, Sky, Yamaha, and Wall Street Journal to tech innovators like Atlassian, DocuSign, FitBit, and Zillow.


Not only are we financially sound and growing, but we have unicorn status: we exceeded $300M in revenue in 2020, is profitable already, and have all strategic options ahead of us. Optimizely continues to invest and addresses a market opportunity north of $30 billion, providing significant personal career growth opportunities.


We are an inclusive culture with a global team of 1200+ people across the US, Europe, Australia, Bangladesh, UAE, Singapore, and Vietnam. We blend European and American business cultures, emphasizing teamwork, inclusion, and moving fast. People make the difference!


Our Data Science team harnesses big data, natural language processing, and machine learning to help create next generation products for Optimizely’s Experimentation, CMS, e-commerce, and data platforms. 

This team emerged as a result of the acquisition of two start-ups (Peerius and Idio) that provided personalisation products for e-commerce and content. Over the past years, the consolidated company has improved the lives of customers such as Intel, HP, Fitch Ratings, Sainsbury’s and many other brands. Episerver’s acquisition of Optimizely has paved the way for interactions between data-driven experimentation and AI. 

This role is within a team whose primary focus lies in the following areas: Natural Language Processing, Machine Learning, and Recommendation Systems. Our stack employs a variety of technologies, including (i) code written in Python, Scala, and TypeScript, (ii) data pipelines using Spark, Luigi, Kubernetes, and Terraform, (iii) prototyping and deploying Machine Learning solutions using Pandas, Scikit-learn, and Dask.

Working Hours: Sunday-Thursday (2pm-10pm)

Job Responsibilities

  • Architect, design, and implement robust and scalable machine learning systems
  • Support and grow team members by setting direction, providing guidance, and active mentoring
  • Effectively work with cross-functional team members to develop and deploy large-scale projects
  • Advocate software engineering and machine learning best practices
  • Perform technical interviews

Knowledge and Experience

In order to apply for this role, you will need to demonstrate the following:

  • Strong experience planning, designing, and implementing scalable machine learning systems 
  • Previous technical leadership experience in a similar environment
  • Excellent Python programming skills and experience in software development
  • Strong understanding of Machine Learning, Recommender Systems and Natural Language Processing
  • Experience with cloud computing infrastructure (AWS / Azure / GCP)
  • Experience with distributed data processing (Spark or similar cloud services)
  • Experience with data querying
  • Basic understanding of Generative AI and LLMs
  • Ability in conveying complex concepts to a non-technical audience
  • Experience shipping ML models to production environments
  • At least 5 years of experience in a development team
  • Experience with source control tools (GitHub / GitLab)

You will get bonus points if you have experience with these but otherwise these are skills you are likely to learn in this role:

  • Experience with Reinforcement Learning or Statistics applied to Machine Learning 
  • Experience with A/B testing
  • Experience with data processing pipeline frameworks (Airflow, Luigi, or similar)
  • Experience working with teams of Data Engineers
  • Experience with functional programming concepts and architecture
  • Experience prototyping ideas discussed in research papers into code that can be assessed and benchmarked

Keywords: Team lead, Machine Learning, Data Science, GenAI, Cloud, Python, Recommendation systems, NLP, Scikit-learn, Pandas, Luigi, Airlfow, Docker, Git


Bachelor's or Master's degree in Computer Science, Information Systems, or equivalent


Driving Continuous Improvement
Driving for Results
Driving Projects to Completion
Interacting with People at Different Levels
Using Computers and Technology

Our culture is the most important thing we offer. We continuously aim to provide a high-growth space where you can do your best work and, in the process, unlock your boundless potential. We work hard to provide meaningful rewards and development opportunities for our employees, recognizing performance and creating a supportive working environment. You'll also get: 


  • Best-in-class compensation plans  
  • Two annual festival bonuses  
  • Reward and recognition programs   
  • Paid Maternity (16 weeks) & Paternity Leave (12 weeks) 
  • Unlimited vacation days and flexible working hours in a hybrid environment 
  • Medical & life insurance for employees and dependents 
  • Volunteering and opportunities to give back   
  • Monthly and quarterly regional and global team-building events 
  • Chance to work with our incredible global team all over the world 
  • Communal transport facilities inside Dhaka and free catered lunch when we return to office 
  • An agile performance review process that encourages ongoing transparency between managers and direct reports   
  • Enablement program and soft skill training to support internal career growth and development    
  • A free “Hacking day” per month for self-studying and researching any IT-related subjects 
  • An annual performance-based increment 


At Optimizely, our standardized language is English, and it is crucial to have good English communication skills to be successful in your global role. All our external and cross-location communication is done in US English (en-us), but internally you can speak in whichever native language you most identify with.