Senior Machine Learning Engineer
2 days ago
The Company
Metropolis is an artificial intelligence company that uses computer vision technology to enable frictionless, checkout-free experiences in the real world. Today, we are reimagining parking to enable millions of consumers to just "drive in and drive out." We envision a future where people transact in the real world with a speed, ease and convenience that is unparalleled, even online. Tomorrow, we will power checkout-free experiences anywhere you go to make the everyday experiences of living, working and playing remarkable - giving us back our most valuable asset, time.
The Role
Metropolis is seeking a Senior Machine Learning Engineer to play a crucial role in developing innovative pricing algorithms tailored to a variety of parking scenarios. Understanding various customer and operational business constraints, we are exploring theoretically grounded approaches to find the right price for our customers. This includes translating complex real-world problems into well-defined mathematical objectives and constraints. The ability to research, develop, and implement large-scale optimization algorithms is crucial for the success of this role.
The right candidate will have a robust background in optimization, forecasting, and causal inference, particularly within pricing applications or closely related fields. You will be involved in every stage of the ML development pipeline – from data acquisition and ingestion to analysis, prototyping and deployment. You should be able to thrive and succeed in an entrepreneurial setting, working collaboratively in a fast-paced environment with multiple stakeholders. You should not be afraid to break new technological ground at Metropolis and need to be more than willing to roll up your sleeves, dig in and get the job done.
Responsibilities
- Research and develop optimization, machine learning, and statistical models to solve complex pricing challenges.
- Navigate large and complex datasets to derive insights that inform key algorithmic strategies for pricing.
- Shepherd models and algorithms from conception to production, ensuring successful and sustainable deployments.
- Communicate ideas and results effectively, verbally and in writing, to a wide range of technical and non-technical audiences.
- Collaborate closely with cross-functional teams to ensure alignment with organizational objectives and requirements.
Requirements and Qualifications:
- PhD (strongly preferred) or MS in Operations Research, Statistics, Economics, Computer Science, or a relevant quantitative discipline.
- 4+ years of industry experience as a ML scientist, research engineer, or equivalent role.
- Strong proficiency in Python and SQL for model development and statistical analysis.
- Proven expertise in implementing and deploying machine learning algorithms related to optimization and forecasting.
- Experience with large-scale datasets, data warehouses, ETL pipelines, and familiarity with relevant tools and libraries.
- Proficiency with optimization libraries such as SciPy, CVXPY, or Gurobi.
- Proficiency in utilizing cloud platforms such as AWS, Azure, or GCP.
- Previous experience working inside innovative, high-growth environments.
- A proven track record of publications in machine learning or optimization conferences and journals (ICML, ICLR, Neurips, INFORMS, SIAM, etc).
When you join Metropolis, you’ll join a team of world-class product leaders and engineers, building an ecosystem of technologies at the intersection of parking, mobility, and real estate. Our goal is to build an inclusive culture where everyone has a voice and the best idea wins. You will play a key role in building and maintaining this culture as our organization grows. The anticipated base salary for this position is $170,000.00 to 210,000.00 annually. The actual base salary offered is determined by a number of variables, including, as appropriate, the applicant's qualifications for the position, years of relevant experience, distinctive skills, level of education attained, certifications or other professional licenses held, and the location of residence and/or place of employment.
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