Machine Learning Engineer

About

Flatiron is a global remote software development company with engineers located around the world. We unite experts from diverse backgrounds and experiences in a collaborative culture to deliver exceptional products and services for our clients. As a forward-thinking software engineering company, we provide industry-leading solutions to complex problems in both the US and the UK. Operating in a fast-paced, agile environment, we specialize in software consulting for our clients. We offer a stimulating and rewarding environment for our team members. We value innovation, continuous learning, and professional growth, and we strive to create a workplace where everyone can thrive. Join us at Flatiron and be a part of a team that is shaping the future of software development.

Job Summary

This is a full-time fully remote working opportunity where you will be working as part of a Scrum team which requires working closely with other software engineers, stakeholders and contributors on the project. Working with respect to the US timezone is a requirement for the position. Attending meetings, being actively involved in the decision making process and collaborating with all of these stakeholders are essential parts of this position.

Key Responsibilities

Design, develop, and deploy scalable, production-ready machine learning systems and end-to-end pipelines on AWS.

Partner with data scientists, software engineers, and product teams to define requirements, select algorithms, and deliver impactful ML solutions.

Architect, optimize, and maintain ML infrastructure — including data ingestion, model training, deployment, serving, monitoring, and lifecycle management — using AWS services.

Lead the data preparation and feature engineering process, ensuring data quality, integrity, and scalability across large datasets.

Implement and optimize ML models (supervised, unsupervised, deep learning, NLP, recommendation systems) with a focus on performance, accuracy, and reliability.

Build and manage robust data pipelines and orchestration workflows to support ML systems at scale.

Integrate models into backend services and APIs, ensuring seamless interaction with applications and end users.

Contribute to MLOps practices, including CI / CD for ML, model registries, experiment tracking, automated retraining, and infrastructure-as-code provisioning.

Utilize Infrastructure-as-Code tools such as Terraform, AWS CDK, or CloudFormation to build and maintain scalable, secure ML infrastructure.

Stay ahead of emerging trends in AI / ML, evaluating new research, frameworks, and tools to enhance product capabilities.

Provide technical leadership and mentorship to junior engineers, guiding best practices throughout the ML lifecycle.

Minimum Qualifications

Advanced written and oral English proficiency.

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.

7+ years of professional experience designing, building, and deploying machine learning models in production environments.

Strong hands-on experience with AWS for ML workflows (data pipelines, model training, deployment, and monitoring).

Expert proficiency in Python and ML frameworks such as TensorFlow, PyTorch, and scikit-learn.

Proficiency with TypeScript for building ML-integrated backend services and automation workflows.

Experience with Infrastructure-as-Code tools ( Terraform, AWS CDK, or CloudFormation ) for deploying ML infrastructure.

Strong knowledge of data processing and analysis tools (Pandas, NumPy, SQL) and orchestration workflows.

Proven track record deploying ML systems into production and integrating them into real products or services at scale.

Experience with containerization (Docker), orchestration (Kubernetes), and MLOps best practices.

Preferred Qualifications

Experience with large language models (LLMs), retrieval-augmented generation (RAG) pipelines, or agentic AI systems.

Expertise in deep learning, NLP, time-series forecasting, or computer vision.

Familiarity with platforms such as MLflow, Kubeflow, or Amazon SageMaker for model lifecycle management.

Contributions to open-source AI / ML projects or publications in the field.

Understanding of data engineering workflows, ETL pipelines, and real-time data processing

Benefits

Yearly Office Allowance Budget

Macbook Purchase Support

Wellbeing Support

If you are a good fit for the position, please apply through LinkedIn.

We only accept CVs that are in English.

Flatiron has a zero tolerance to discrimination policy. In this regard, during the course of the evaluation of your job application and all your employment relation, if any, all discriminatory factors such as race, sex, sexual orientation, social gender definitions / roles, color, national or social background, ethnicity, religion, age, disablement, political opinion or any status that is protected under law shall be disregarded.