ML Engineer

TL;DR: Full-time ML engineer position. Major work around our categorisation engine. Must have experience in NLP, Python and data.

🏦 About Fold

At Fold, we are rebuilding personal finance and mobile banking from the ground up for India’s internet native citizens.

🏋️ About the role

We are seeking a talented ML Engineer to join our dynamic tech team. As an ML Engineer, you will be responsible for developing and implementing machine learning models and algorithms to enhance our categorisation engine F1. You will work closely with the development team to integrate machine learning capabilities into Fold. You will also collaborate with cross-functional teams to design and deploy scalable ML solutions that deliver value to our users.

💯 You’d fit in if

  • We don’t care about your credentials and degrees, but having a solid 4+ years of experience in machine learning and related field is important.
  • You enjoy building ML-powered products from scratch. Joining in at an early stage involves a lot more than just coding. You'll be working in often fast-paced environments, contributing to product and business decisions, and leveraging your expertise to drive innovation.
  • You can work in teams. You need to be able to collaborate and work across teams, effectively communicating complex ML concepts to both technical and non-technical stakeholders.
  • You can work within business constraints. You understand how companies work and can balance the tradeoffs between time, speed, and features when implementing ML solutions.
  • You have a deep understanding of the fintech industry. Experience applying ML techniques to fintech applications is a big plus.

🛠 Responsibilities

  • Develop and implement machine learning models and algorithms to build and improve our categorisation engine F1.
  • Execute EDA and data preprocessing tasks to optimize datasets for model training and evaluation.
  • Train and fine-tune machine learning models using appropriate techniques and frameworks, with a focus on accuracy, performance, and scalability.
  • Evaluate and validate the performance of ML models through rigorous testing and experimentation.
  • Optimize ML models for deployment in production environments, considering factors such as latency, memory usage, and resource constraints.
  • Collaborate with cross-functional teams to define and implement data collection strategies, ensuring high-quality and relevant data for training and evaluation.
  • Stay up-to-date with the latest advancements in machine learning and AI tools, frameworks, and libraries, and apply them to improve our ML capabilities.
  • Document and communicate ML methodologies, experiments, and results to team.
  • You'll create experiences that shape an iconic product. We believe in hiring smart people that pride themselves in good values, their work ethics, and holding great responsibility.

🌱 Join us

  • Full-time
  • We'll help set up your workspace
  • Competitive salary
  • Generous stock options (for full-time employees)

🎁 Perks and benefits

  • Generous leave policy: Unlimited paid leave.
  • Flexible working hours: It doesn't matter if you're a morning person or a night owl, work when you want. We all work asynchronously. Meetings are the exception, not the rule.
  • Get your perfect setup: Mac/Windows/Linux, mechanical keyboard or anything that you need to do the best work of your life. We'll help set up your workspace.
  • We'll take care of you: Annual team retreats, all meals and accommodation if you choose to work at Bangalore HQ. Wellness allowance to take care of your physical and mental health (gym memberships, meditation apps, and anything you need).
  • Health insurance & benefits: Comprehensive health insurance coverage of ₹20L for full-time employees, includes free health check-ups, unlimited doctor consultations, dental care, and generous personal accident insurance.
  • Make a big difference: Take, own, and implement decisions to build Fold from scratch. We are not rushing to market but focusing on a quality product and the attention to little details.

⚙️ Our recruitment process

  1. Introduction: Get on an introductory call with us. We'll talk about your interests, your past experience, our vision of the future, and how you can contribute and help us achieve it.
  2. Take-home exercise: We'll send over an application development challenge, which you'll have to complete and send back in 48 hours.
  3. Pair programming: If we like your submission, we'll invite you to an interview with one of our engineers (strictly no white-boarding), with whom you'll be pair-programming on a real-world problem.

We move very fast, and we'll be mindful of your time. All of this will be done within a week, and if all goes well you'll receive a final offer within 24 hours of the pair-programming round.

✍️ Write to us

We are committed to assembling an unrivalled team of builders, artists, technologists, and adventurers who aim to create a new way to explore the world. As an early crew member, you'll have an enormous impact on both our product and company culture.

If you think you'd like to join us, write to us at join@fold.money, with links to your GitHub profile, Devfolio, personal portfolio, LinkedIn, or anything else you think might be relevant. To truly stand out, we recommend you answer at least two of these questions:

  • List three problems in 21st-century personal finance, and tell us how you would solve them.
  • What are the most impactful machine learning projects you've worked on?
  • Can you share an example of a complex problem you solved using machine learning techniques?
  • Have you implemented any deep learning models? If so, can you describe your experience and the challenges you faced?
  • Have you worked with large-scale datasets and distributed computing frameworks for training ML models? If yes, please explain.
  • How do you evaluate the performance and interpret the results of your machine learning models?
  • Can you explain a time when you had to troubleshoot and debug a machine learning model that was not performing as expected?
  • Are you experienced in deploying machine learning models in production environments? If so, what tools and techniques have you used?