Job Description

What we're looking for

The role of the LIFELENZ Machine Learning Engineer is to aid in the designing, training, experimenting, production deployment, and monitoring of machine learning models that are developed in alignment with our mission - to build and grow a global AI-optimized scheduling and forecasting platform that will empower and reward people within the fast-food and Quick Service Restaurant (QSR) industry.

LIFELENZ ML Engineers have a passion for understanding the modeling needs that solve real customer problems and devising innovative solutions to deploy, monitor, and iteratively improve modeling solutions at scale.

Key responsibilities

• Build and test machine learning models to support the LIFELENZ platform
• Design, build, and deploy data and machine learning pipelines on AWS
• Enable an iterative lifecycle for data products to continuously improve, integrate and deploy
• Bring data science workflows, analysis, and modeling into a healthy state of standardization, evaluation, deployment and observability in production.
• Build observability and monitoring of ML models & experiments
• Work collaboratively across teams to ensure a holistic MLOps process connecting modeling with engineering standards

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a relevant field with 2-4 years of experience.
• 4+ Years experience with python and machine learning frameworks
• 1+ year experience with MLOps and maintaining machine learning models at scale

Strong knowledge and hands-on experience in several of the following areas:

• Extensive experience with Python programming language.
• Proficiency with relational database concepts,
• SQL, and a working knowledge of ETL processes.
• Experience with cloud technologies such as AWS, GCP, or Azure.
• Experience with version control systems (e.g., Git).
• Versioning and Tracking Models and Experiments (e.g. DVC, MLFlow)
• Iterative ML Pipeline Development and Deployment (e.g. Metaflow, Kubeflow Pipelines, Prefect, Dagster)
• Container Applications (eg. Docker, Kubernetes)
• Visualizing ML processes (eg. Dash, Streamlit). Monitoring and debugging large amounts of models in production, maintaining observability and explainability of active ML processes
• Modeling, tuning, and optimization with common frameworks (e.g. sklearn, pytorch))

Preferred Qualifications

Experience with the following:

• Real time inference deployment and monitoring (e.g. FastAPI, Ray Serve) 
• CI/CD practices
• Model Deployment Strategies (e.g. A/B testing, canary release)
• Cross team projects (DevOps, Data Engineering, Data Science)
• Time series analysis and predictive models

And to be successful in LIFELENZ you would have to:

• Be an aspiring individual who enjoys variety and unpredictability in a role.
• Thrive in a fast-paced environment with demonstrated ability to quickly learn and adapt to new processes, tools, and software engineering concepts.
• Demonstrate tenacious problem-solving and critical thinking skills, attention to detail and a passion for driving efficient and scalable solutions.
• Be a self-starter and naturally curious individual who thrives in a dynamic work environment on individual initiatives, and as part of a team.
• Embrace a dynamic startup environment

Why LIFELENZ

We are a ground-breaking platform with a unique vision (we can’t give away our secrets here!). We are truly seeking to revolutionize the way our clients operate and enhance the employer/ employee relationship. Our passion and excitement are genuine. We also offer great benefits (flexible time off, benefit plans, 401k, holidays, etc.), employee stock options and have surrounded ourselves with incredible talent across the globe.

Salary: $110,000-$150,000

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