Who We Are
LIFELENZ is a rapidly growing team of 80+ mathematicians, engineers, designers, computer scientists, strategists and client success experts based in the U.S., Canada, Australia, and UK building a machine learning-based, advanced analytics workforce and human capital management platform. We have built and maintained over 20+ platforms over a 19-year period. We have won many awards for client delivery and collectively contributed to over 100+ global patents for complex software platforms. In the past 2 years, the business has been funded over $20M USD as we deliver metrics that will unlock our Series B funding in early Q1 2026, providing us with significant capital to accelerate our growth & expansion across verticals and geographies while further driving our dominance in the US QSR Top Brands market.
Our mission is to create optimized outcomes for both employers and employees.
LIFELENZ is an automated workforce and human capital management platform solving challenges related to onboarding, sales & labour forecasting, scheduling & time clock/keeping, insights & reporting and labour law compliance. It uses machine learning to automatically self-tune and self-manage models to a particular store with hyper-local attributes. The technology approach and analytic methodologies used enable the distribution across large-scale centralized ownership and highly fragmented franchised ownership companies.
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
The LIFELENZ Data Science team is looking for a candidate with strong analytical and data skills to join the team. The Data Scientist will work closely with multiple stakeholders across the business to develop models, optimize systems, and leverage data to support tactical and strategic decisions.
- 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
- Embrace a dynamic startup environment
Knowledge, Experience, [Competencies/Capabilities] Qualifications
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.
Why LIFELENZ
We are a ground-breaking platform with a unique vision (we can’t give away our secrets here!). We have office locations in Chicago; Washington, D.C.; Adelaide, Australia; London, UK.
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.
Join our team today!
