Founded in 2014, Even Financial is a B2B fintech company that is transforming the way financial institutions find and connect with consumers, enabling any company to add financial products to their business. As the leading search, comparison, and recommendation engine for financial services, Even seamlessly bridges financial services providers (such as SoFi) and channel partners (such as TransUnion) via its simple yet robust API and embeddable solutions. Even turns any consumer touchpoint into a comprehensive financial services marketplace with full compliance and security at scale. Even was named one of "America's Best Startup Employers'' by Forbes for 2021 and placed in the Top 50 of the 2020 Deloitte Technology Fast 500, which recognizes the fastest growing tech companies in the world. In December 2021, Even announced it will be acquired by MoneyLion, a mobile banking and financial membership platform that empowers people to take control of their finances.

Challenges You'll Tackle Daily

• How do we design a scalable and reliable system that enables data scientists across the company to build and deploy models within a day?
• How can we balance the needs of multiple product, BI, business and engineering teams in order to improve our systems and internal products? 
• Can we design model container solutions to host arbitrarily complex models?
• How do we build tooling to enable a robust model lifecycle with governance and reproducibility in mind?How do we enable a systematic approach to testing and validating ML models?
• How do we enable team best practices around writing and testing application code, automating tests and deployment protocols?

Who You Are

• You have experience implementing production ML systems at scale in Java, Scala, Python, or similar languages, using frameworks such as scikit-learn,  XGBoost, TensorFlow/PyTorch, and/or SparkML
• You understand the architecture and development workflow for large-scale batch and streaming machine-learning systems
• You care about shipping product, agile software process, reliability, and focused but fast experimentation
• You have experience with implementing first-class SDLC concepts into Machine Learning systems - ensuring adequate systems of monitoring and observability are implemented
• You have contributed code and models to large-scale real-time recommender systems
• You’ve leveraged CI/CD best practices for ML engineering, including test automation and monitoring, to ensure successful deployment of models and application code
• You have experience w/ MLOps and care about model explainability and governance
• You are comfortable writing performant SQL queries for data exploration and feature creation
• You understand a variety of ML algorithms, including online bandit models, learning to rank systems, and recommendation systems
• You know that people always come first, and that the rest (code, process, engineering) follows directly after this

Requirements

• 4+ years of experience designing and building data-intensive solutions using distributed computing, microservices, and production grade APIs
• 3+ years of experience working with or in:
• ML platforms: Sagemaker, TensorFlow, PyTorch, SparkML, Dask, Vowpal Wabbit, etc
• Data processing systems: Spark, Kafka, AWS EMR/Glue, Redshift/Snowflake, PostgreSQL
• Deploying ML models in production environments serving with low latency
• Python and another application programming language such as Scala
• Experience with data and workflow pipeline tools such as Airbyte, Meltano, Airflow, Dagster, etc
• Understand and have used architectural designs such as CDC, event driven design, domain driven design successfully
• Strong grasp of computer science fundamentals and common design patterns (and knows how to use them effectively!), including object oriented programming, functional programming
• Ability to decompose large, complex problems into smaller actionable parts
• Effective at implementing pragmatic solutions using iterative and incremental design and product process
• Cross-functional team oriented mindset and an effective communicator with both technical and nontechnical people

Nice to Haves

• Have a solid foundation in statistics and Bayesian inference
• Have some exposure to causal inference methods, probabilistic programming, optimization methods, and/or recommendation systems
• Implemented a cutting edge research model into a production software system from scratch
• Have an understanding  of tools like MLFlow, DVC, Pachyderm, Weights & Biases, TF Serve, and why they might be needed
• Understand and have experience working with monitoring and telemetry systems such as Datadog, Prometheus/Grafana, Arize, etc
• You are not boring at parties and have a great sense of humor

Full time employees are eligible for the following benefits:

Comprehensive medical/dental/vision packages + life and disability insurance

401K retirement plan

Stock options

Uncapped paid time off (PTO)

Company paid holidays + monthly personal holiday 

Membership to Udemy  

Perks including a monthly wellbeing stipend, $200 one time WFH stipend

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.