Senior Software Engineer (Machine Learning/Applied Data Scientist)

Cricut•South Jordan, UT

4d•Onsite

About The Position

We are looking for an experienced Senior Machine Learning Engineer / Applied Data Scientist with deep expertise in modern machine learning and deep learning techniques. In this role, you will design, build, and deploy intelligent systems that power recommendations, clustering, and image analytics — directly shaping how our users discover ideas and live more creative lives.

Requirements

Bachelor's degree or higher in Computer Science, Applied Mathematics, Data Science, AI, or related fields.
3+ years of industry experience building and deploying ML models in production.
Strong Python skills and proficiency with deep learning frameworks (TensorFlow, PyTorch, or Keras).
Experience with data manipulation using NumPy, Pandas, and scikit-learn.
Ability to write complex SQL queries, ideally with Redshift.
Solid understanding of software engineering principles including Git and agile methodologies.
Excellent problem-solving and communication skills with the ability to work independently on complex ML challenges.

Nice To Haves

Experience with AWS ML infrastructure (SageMaker, CloudFormation, CloudWatch).
Familiarity with Apache Airflow for workflow orchestration.
Knowledge of containerization and distributed systems (Docker, Kubernetes).
Motivation to drive projects end-to-end from research to production deployment and communicate progress with executives.

Responsibilities

Analyze and preprocess large-scale datasets for training, evaluation, and experimentation.
Develop and optimize deep learning models using architectures, loss functions, and augmentation strategies tailored to each problem.
Apply and integrate LLMs (e.g., ChatGPT, Gemini) for reasoning, analytics, and workflow automation.
Stay current with advancements in ML, computer vision, and AI, and translate research into production-ready solutions.
Build scalable training pipelines and integrate them into our CI environment to support experiment tracking, nightly builds, and automated testing.
Collaborate with software engineers to deploy ML models into production systems with reliability and performance in mind.
Troubleshoot and debug ML systems across training, inference, and distributed environments.
Willing to comply with 4- 5 day/week RTO policy. Monday-Thursday.
Work with product and engineering teams to define requirements and deliver impactful ML-driven features.
Document findings and present insights to technical and non-technical stakeholders.