Senior Machine Learning Engineer, Alloy
Predict
This position has a minimum base salary of $199,000 and a midpoint base salary of $234,000 + Equity
2 days a week in office
Identity verification for financial institutions.
Job no longer available
Identity verification for financial institutions.
201-500 employees
Job no longer available
This position has a minimum base salary of $199,000 and a midpoint base salary of $234,000 + Equity
2 days a week in office
201-500 employees
Company mission
We’re your unified platform for fraud management, identity decisions, and regulatory compliance.
Role
Who you are
- Experience deploying machine learning models to external facing applications
- Experience deploying to production, including best SDLC practices, CI/CD and IaC
- Experience with streaming technologies such as Kafka, Kinesis, Flink, or Spark
- A minimum of 5 years experience writing production-level code as a data engineer, with at least 2+ years of professional experience building product features and leveraging machine learning to external-facing users
- Someone who embodies our shared Alloy values: be bold, get scrappy, collaborate, and celebrate our differences
- Someone who thrives in crafting solutions that serve immediate needs, and keeps in mind how these solutions can enable the broader engineering organization’s goals
- Strongly proficient in at least one machine learning framework (pytorch, tensorflow)
- A portfolio of successful projects demonstrating a commitment to product excellence
- Deep technical expertise with a background in software development:
- Proficient in writing Python, terraform, and javascript. Technical dexterity and the ability to develop with high proficiency are more important
- Ability to give thorough code reviews and help engineers solve bugs
- Experience with AWS infrastructure
- Experience with highly scalable and highly available application architectures
- Able to prioritize and communicate progress in a timely manner
- We like to set ambitious goals without sacrificing on the quality of the things that get shipped
- Able to work across multiple teams across Alloy
- Must be local to Greater New York City
- Extra credit:
- Experience working with Typescript
- Some experience with Java/Scala
What the job involves
- We’re seeking an experienced member to join a lean/focused team to drive our real-time machine-learning fraud capabilities
- The Predict team is focused on building machine learning into Alloy’s product to improve our clients' decision-making and help them manage their risk
- Because the team's work introduces foundational capabilities for risk management, the team is expected to work with teams across Alloy
- Architect scalable, fault-tolerant batch and real-time systems to power our machine learning systems
- Work across the teams at Alloy to keep up to speed with relevant data that signals can be extracted from, and formulate approaches to get those into our systems
- Build and manage the interface between the machine learning system and the Alloy application
- Building machine learning systems that are optimized for low latency and high availability
- Ensure that our machine learning algorithms generate accurate outcomes
- Collaborate with Product & Engineering organization members to develop machine learning algorithms
- Designing clean interfaces and well-tested systems
- Participate in hardening our feature generation systems, both real-time and batch. The technologies we use for feature generation include Flink, Kinesis, and Sagemaker
- Participate in our on-call rotation
- Stay up-to-date with the industry developments in machine learning
Our take
In recent years, there has been an enormous shift towards online banking, a trend that’s unlikely to turn around with surveys showing that more than half of US banking customers don’t intend to return to branches in person. The rise in digital onboarding has aroused concern about both the speed and labour of ID verification, as well as fraud potential. Alloy was built to help connect banks and fintech companies to more sources of KYC, AML and other compliance data.
The company's single API identity verification platform allows financial institutions to secure against the risk of fraud and misjudged risk decisions when onboarding new customers online. Alloy also helps to improve customer conversion through the compliance funnel and speed up previously manual back-office processes.
The company serves the burgeoning fintech market, a lucrative market with a 25% annual growth rate and already has notable clients in the form of Radius Bank and Aspiration. Alloy's main competition in the future will likely come from financial institutions themselves, who will have to decide on buying the company's solution or building their own in-house. Regardless, Alloy's global expansion efforts have created substantial growth opportunity.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Company
Company benefits
- 16 weeks of parental leave
- Premium medical, dental, vision, HSA, and FSA programs
- 401k with matching and immediate vesting & eligibility
- Commuter, health & wellness benefits
- $1,000 annual Learning and Development stipend
- Memorable team events and retreats
- Work from home opportunities
- Unlimited PTO
Company HQ
Greenwich Village, New York, NY
Leadership
Tommy Nicholas
(Co-Founder & CEO)Founder of The City Swig. Software Developer of SHOCKOE.COM. Owner of Web/Mobile Freelancing. Most recently was Head of Product at Knox Payments.
Laura Spiekerman
(Co-Founder)Studied Political Science at Columbia. Early career as a Paralegal at Clayman & Rosenberg. Went on to become Director of Marketing at Kopo Kopo. Research/Investments Analyst at Imprint Capital Advisors. Became Head of Business Development & Strategic Partnerships at Knox Payments.
Charles Hearn
(Co-Founder & CTO)Studied Computer Science at University of Virginia. Independent Researcher at UVA Researcher for over a year. Mobile Lead & General Developer at The City Swig. Became Program Manager at Microsoft. Recently Lead Project Engineer at Knox Payments.
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