As a Machine Learning Engineer (MLE) on the Connections AI team, you’ll be a part of a skilled group of applied scientists, software developers, and other machine learning engineers working together to connect buyers with the right professionals to help realize their home-buying dreams.
About the role
We are seeking a collaborative, customer-focused, and product-minded engineer and scientist with deep experience with applied machine learning. You are a roll-up-the-sleeves and get-it-done engineer with a deep understanding of, and are constantly learning about, new techniques in modeling, ML frameworks, and ML infra. You excel at prototyping new ML applications and optimizing impact, performance, and efficiency in production.
- Apply a growth-mindset and first principles to ambiguous customer problems to rapidly iterate on novel solutions and ways of working.
- Collaborate closely with applied scientists, engineering, design and research to understand, scope, design, prototype, implement and iterate on internal and external facing systems supporting and implementing next generation AI applications.
- Shepherd the deployment of machine learning applications into production with an eye towards reliability.
- Cultivate connections with other teams for critical dependencies and infrastructure.
- Contribute to carrying and growing our team culture of rapid innovation and creative frugality.
This role has been categorized as a Remote position. “Remote” employees do not have a permanent corporate office workplace and, instead, work from a physical location of their choice, which must be identified to the Company. U.S. employees may live in any of the 50 United States, with limited exceptions.
In California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington DC the standard base pay range for this role is $145,500.00 - $232,500.00 annually. This base pay range is specific to these locations and may not be applicable to other locations.In Colorado, Hawaii, Illinois, Minnesota, Nevada, Ohio, Rhode Island, and Vermont the standard base pay range for this role is $138,300.00 - $220,900.00 annually. The base pay range is specific to these locations and may not be applicable to other locations.
In addition to a competitive base salary this position is also eligible for equity awards based on factors such as experience, performance and location. Actual amounts will vary depending on experience, performance and location. Employees in this role will not be paid below the salary threshold for exempt employees in the state where they reside.
Who you are
- Proficiency with a high-level programming language (we most commonly use Python)
- Practical knowledge of statistics (for example, causal inference, Frequentist or Bayesian inference)
- The communication skills to influence, collaborate with, and educate others (whom you may need to educate on methods and requirements in experimentation and statistics).
- Experience prototyping, developing, and implementing algorithmic solutions and new technologies with diverse analytics and data.
- Hands-on experience in deploying machine learning models into realtime production environments.
- Experience working with large scale datasets and building ETL pipelines using Spark, Kubeflow, and DataBricks.
- Strong understanding of Machine Learning and Natural Language Processing fundamentals.
- Experience with Machine Learning tools and Frameworks (e.g. PyTorch, Transformers, XGBoost, scikit-learn, etc.)
- The tenacity to embrace and tackle challenging problems.
- Practiced technical ability and passion for both owning implementation and contributing technical/thought leadership for a team of world-class scientists engineers.
- Bachelor's degree or equivalent experience in Computer Science, or a related field.
Bonus Qualifications:
- Experience with generative AI or large language models and related technologies (knowledge retrieval solutions, for example).
- Experience with regulated, private or sensitive data, document understanding, user interest modeling, or reinforcement learning.
- Experience collaborating with science, engineering, design, research and product partners in a team with a startup culture.
- Advanced degree (M.S. or Ph. D.) or equivalent experience in Computer Science or Engineering, Machine Learning, or related field.
