Darwin Homes

USA

Posted on: 11 February 2024

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Machine Learning Engineer

Job Title

Location:

Remote or Austin, TX

About Darwin Homes:

At Darwin Homes, we fundamentally believe that the rental experience is broken. Too often, property management—serving as the middleman between investors and residents—often means shoddy service, hidden fees, and inefficient processes that shortchange everybody involved. Darwin was built to make residents' and owners' lives easier through an innovative ecosystem of technologies. We have created the best product in the market for residents to discover, tour and lease their future home; and for owners and partners to have complete peace of mind from our modern management and leasing services built around our core values of transparency and professionalism. Darwin Homes is the destination for single-family rental services for property owners and residents.

The Darwin Homes team is composed of a diverse set of alumni from DoorDash, Square, Facebook, Apple, LinkedIn and other top technology companies. The founders and executive team have over 30+ years of combined experience in scaling disruptive technology and operations-focused businesses. Darwin Homes was backed by top Silicon Valley venture capital (Khosla, Fifth Wall) and was acquired by Pagaya Technologies, a publicly traded company, in early 2023. Pagaya is an AI/ML data technology company with offices in Tel Aviv, New York and Austin.

About The Role

Be the founding ML Engineer on the team.

Design and implement enterprise machine learning systems at scale.

Develop standards and best practices for ML discipline and ensure consistency to these standard.

Design and implement machine learning tests and experiments as part of a scalable CI/CD pipeline

Develop and deploy scalable feature extraction and machine learning experiments to handle training, tuning and inference in cloud-native production environments.

Identify and evaluate new technologies and research to improve performance, maintainability, and reliability of machine learning systems over time.

Facilitate the development and deployment of proof-of-concept machine learning systems.

Communicate with stakeholders to build requirements and share progress.

Qualifications

3+ years of experience building end-to-end ML projects as a ML Ops Engineer, Platform Engineer, or ML Engineer (or equivalent).

MS/PhD in Computer Science with a focus in ML/Data Science/AI topics preferable

Knowledge of machine learning methodology and best practices.

Knowledge of machine learning frameworks: PyTorch, Tensorflow, Keras, Scikit-Learn, etc.

Strong competency in software engineering skills and core languages such as Python.

Understanding of machine learning algorithms, statistical work, and modern-day research and methodology.

Experience working with and deploying scalable database systems (NoSQL, SQL).

Experience with running machine learning tests and experiments in cloud environments.

Experience building custom API integrations between cloud-based systems.

Experience with ML pipeline testing, benchmarking and monitoring.

Experience working with ML/DS pipeline (data loading and cleaning, feature extraction, deployment, etc.)

Familiarity with artifact storage and re-training frameworks.

A proactive self-starter and skilled problem-solver, capable of driving complex, impactful projects in a fast paced, ambiguous environment.