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Machine Learning Senior Engineer - Finance

About Truelogic

At Truelogic we are a leading provider of nearshore staff augmentation services headquartered in New York. For over two decades, we’ve been delivering top-tier technology solutions to companies of all sizes, from innovative startups to industry leaders, helping them achieve their digital transformation goals.

Our team of 600+ highly skilled tech professionals, based in Latin America, drives digital disruption by partnering with U.S. companies on their most impactful projects. Whether collaborating with Fortune 500 giants or scaling startups, we deliver results that make a difference.

By applying for this position, you’re taking the first step in joining a dynamic team that values your expertise and aspirations. We aim to align your skills with opportunities that foster exceptional career growth and success while contributing to transformative projects that shape the future.

Our Client

Financial service provider dedicated to helping under-served customers in emerging markets to achieve financial stability and success. We lend through our patented technology that turns a smartphone into digital collateral, and our cutting-edge machine learning, data science, and anti-fraud AI allow us to offer the lowest cost and qualify the most customers in the industry. As of 2024 we have brought billions of dollars in credit to 12 million customers, doubling in the last two years while remaining strongly profitable and sustainable for the long term.


Job Summary

As a Machine Learning Engineer, you will play a critical role in developing and maintaining the core systems and infrastructure that power our data science applications. This position is platform/tooling focused.

You will work closely with other engineers, data scientists, risk/fraud analysts and product managers to build, maintain and improve the whole ML platform where our models and other DS products run. You will also develop tools that help our modeling team to create features, train, retrain, deploy, serve and monitor ML models.

Responsibilities

  • Collaborate with global teams including Risk, Fraud, Engineering and Product to deliver world-class data science products to international markets, including ML models, infrastructure and tools.

  • Own the life cycle (design, development, deployment, delivery and monitoring) of the infrastructure that powers our ML models that serve 300 million transactions per month and ensure they have optimal performance.

  • Drive the enablement of our modeling team by building new tools or adopting new technologies that will allow them to extract data, generate features and deploy/serve models with ease.

  • Work with a data-driven mindset and understand the critical importance of handling data properly and safely.

  • Organize frameworks and develop processes in our codebase so that the easy and default coding style is cleanly structured.

  • Mentor other engineers and data scientists about best practices in engineerin

Qualifications and Job Requirements

  • Bachelor’s degree in Computer Science, Engineering, or a related field

  • 3+ years of experience as a machine learning engineer, data engineer or a closely related position with a proven track record of writing production-level code and developing and maintaining ML infrastructure.

  • Good verbal and written communication skills in English

  • Comprehensive knowledge of ML life cycle: from data extraction and feature engineering to model serving and monitoring for live and batch processing.

  • High proficiency in Python and a strong understanding of its related libraries and frameworks (e.g., Scikit-Learn, Pandas, Flask, FastAPI, etc).

  • Strong background in feature store implementation and usage (Chalk.ai, Tecton, Databricks, Feast, etc.), particularly focused on managing large volumes of data for online and offline processing.

  • Demonstrated experience with cloud providers (AWS preferred) and related data services (e.g., databases, storage, serverless computing).

  • Ability to work in a fast paced environment with constant requirement changes.

Bogota Benefits

  • 100% Company-funded Health and Dental Insurance for employees and immediate family members

  • Life insurance

  • Indefinite-term contract

  • 20 days of vacations

  • Phone finance, Headphone, home office equipment and fitness perks.

  • $2,000 USD annual Co-working Travel perk

  • $2,000 USD annual Professional Development perk

Extra Info

It's required to go to the office 3 times per week.

Apply now!

Average salary estimate

$115000 / YEARLY (est.)
min
max
$100000K
$130000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Machine Learning Senior Engineer - Finance, Truelogic

At Truelogic, we are excited to introduce our opening for a Machine Learning Senior Engineer in the Finance sector. As a leading provider in the staff augmentation space, situated in the heart of New York, we take pride in delivering innovative technology solutions tailored for both startups and industry giants. Your role will be pivotal in supporting our esteemed client, a trailblazer in financial services dedicated to empowering underserved customers. By leveraging cutting-edge machine learning and data science techniques, you will help provide affordable credit solutions to millions. Imagine being at the forefront of deploying technology that uses smartphones as digital collateral while also implementing robust machine learning infrastructure. As a Machine Learning Senior Engineer, you will collaborate with cross-functional teams, delivering exceptional data science products that shape financial stability for countless individuals. You’ll design, develop, and maintain our ML platform, ensuring it runs smoothly and efficiently, capable of handling an astonishing 300 million transactions monthly. Your expertise will drive improvements in our tooling, enabling our modeling team to excel in their tasks with newfound ease. This is not just a job; it’s an opportunity to join a mission-driven team that values your ideas and encourages your professional growth. With us, your contribution matters, and your career can flourish in a supportive, innovative environment. If you're ready to make a tangible difference in the finance world, we invite you to apply today and embark on a rewarding journey with Truelogic!

Frequently Asked Questions (FAQs) for Machine Learning Senior Engineer - Finance Role at Truelogic
What are the responsibilities of a Machine Learning Senior Engineer at Truelogic?

As a Machine Learning Senior Engineer at Truelogic, your main responsibilities will include collaborating with various teams such as Risk, Fraud, Engineering, and Product to create and enhance data science products. You'll be responsible for the entire life cycle of ML model infrastructure, ensuring optimal performance for our high-volume transactions. Additionally, you'll develop tools that assist our modeling teams in features extraction, training, and deployment, while also mentoring fellow engineers in best practices.

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What qualifications are required for the Machine Learning Senior Engineer position at Truelogic?

To qualify for the Machine Learning Senior Engineer position at Truelogic, you should possess a Bachelor’s degree in Computer Science, Engineering, or a related field. Your background should include at least 3 years of relevant experience in machine learning or data engineering, demonstrated through production-level code and infrastructure management. Additionally, expertise in Python and its libraries, as well as experience with cloud services like AWS, are crucial for this role.

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What tools and technologies should a Machine Learning Senior Engineer be familiar with at Truelogic?

A Machine Learning Senior Engineer at Truelogic should be well-versed in Python and its associated libraries such as Scikit-Learn, Pandas, and Flask. Additionally, comprehensive knowledge of ML life cycles, feature store implementations, and cloud data services are essential. Familiarity with tools that assist in data extraction and online/offline processing will also be advantageous in optimizing our ML infrastructure.

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What kind of team dynamics can a Machine Learning Senior Engineer expect at Truelogic?

At Truelogic, Machine Learning Senior Engineers can expect a collaborative and dynamic team environment. You will work closely with talented professionals across various domains, including engineering, data science, and product management. The culture promotes knowledge sharing and mentorship, allowing you to not only contribute but also grow through interactions with diverse team members.

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What benefits can a Machine Learning Senior Engineer expect when working at Truelogic?

Working as a Machine Learning Senior Engineer at Truelogic comes with a range of attractive benefits. These include comprehensive health and dental insurance for you and your family, life insurance, an indefinite-term contract, and generous vacation time. Additionally, you will receive annual perks for professional development and co-working travel, along with support for home office equipment and fitness programs.

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Common Interview Questions for Machine Learning Senior Engineer - Finance
Can you explain the ML model lifecycle and your experience with it?

In discussing the ML model lifecycle, focus on stages such as data collection, data preprocessing, feature engineering, model training, evaluation, deployment, and monitoring. Highlight your specific experiences related to designing and managing each of these stages effectively, especially mentioning any tools or frameworks you’ve utilized.

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What is your experience with cloud-based ML infrastructure?

Share your experiences with cloud providers like AWS and discuss specific services you've used, such as serverless computing or managed databases. Emphasize a project where you successfully implemented cloud-based solutions to enhance ML model performance or scalability.

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How do you handle data security and privacy when working with ML models?

Address the importance of data integrity and privacy in machine learning projects. Discuss any policies, encryption techniques, or best practices you follow to ensure that sensitive data is protected throughout the ML lifecycle.

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What tools and frameworks are you proficient in, and how have you applied them in your previous roles?

List tools like Scikit-Learn, Pandas, and frameworks you are proficient in, providing examples of how you've used them to develop and deploy models. Mention any specific challenges you faced and how you overcame them using these tools.

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Describe a project where you improved an existing ML model.

Discuss a specific project where you identified performance issues in an existing ML model. Describe the steps you took to optimize it, the techniques you implemented, whether through tuning hyperparameters or redesigning features, and the outcomes of your efforts.

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How do you stay updated with the latest trends and technologies in machine learning?

Detail your strategies for continuous learning, such as reading industry blogs, attending conferences, participating in online courses, or engaging with professional communities. Provide examples of how insights gained from these activities have influenced your work.

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Can you explain the importance of feature engineering in ML?

Share the significance of feature engineering in enhancing model performance. Provide examples illustrating how you approached feature creation or transformation in your projects, and discuss the impact of your efforts on model accuracy.

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How do you manage performance monitoring for deployed ML models?

Explain your process for monitoring ML models post-deployment, including the metrics you track and tools used for performance evaluation. Discuss how you ensure that models remain effective and adapt to changing data patterns over time.

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What challenges have you faced when working on ML projects, and how did you overcome them?

Identify a challenge, such as data quality issues, model drift, or technology constraints, and explain the strategy you used to address it. Emphasize your problem-solving skills and adaptability in overcoming obstacles.

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How do you foster communication and collaboration with other teams in your work as an ML engineer?

Discuss your approach to teamwork, emphasizing your communication skills and how you ensure alignment with other teams, such as Product or Data Science. Provide examples of successful collaborative projects and how you contributed to their success.

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EMPLOYMENT TYPE
Full-time, hybrid
DATE POSTED
April 9, 2025

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