Staff Machine Learning Engineer (Modeling), Support Job at Cash App (Seattle)

Staff Machine Learning Engineer - ML Training Infrastructure

The Role:   We are seeking an experienced, technically strong, impact-driven ex...

Location

United States , Austin; Mountain View

Salary:

185000.00 - 335300.00 USD / Year

General Motors

Expiration Date

Until further notice

Requirements

Bachelor's degree or higher in Computer Science or a related field, or equivalent practical experience
8+ years of professional software engineering experience
5+ years of specialized experience in AI/ML infrastructure, such as enabling distributed training for large-scale ML models
Strong programming skills in Python, with deep proficiency in frameworks such as PyTorch (preferred), TensorFlow, or similar ML systems
Proven experience designing and operating distributed systems for ML training, including distributed computing, GPU computing, and cloud environments (AWS, GCP, Azure)
Demonstrated track record of leading technically ambiguous, cross-team infrastructure initiatives and driving them to measurable impact
Strong architectural judgment and ability to make sound technical tradeoffs across performance, reliability, usability, and cost
Willingness to travel to Sunnyvale, CA as needed
Comfortable operating in highly ambiguous and dynamic environments

Job Responsibility

Define and drive the architecture, design, and development of scalable, reliable, and high-performance ML frameworks and platform capabilities to support model training at scale
Lead model training performance analysis and optimization efforts across distributed training workflows, improving scalability, efficiency, and cost across heterogeneous hardware environments
Raise the bar on system observability, debuggability, operational excellence, and developer experience across the ML training stack
Own large, ambiguous, cross-functional technical initiatives from strategy through execution, including technical roadmap definition, tradeoff analysis, and delivery
Influence platform direction by identifying long-term infrastructure investments, setting engineering standards, and driving adoption of best practices across teams
Collaborate across organizational boundaries to align requirements, resolve technical disagreements, and integrate new capabilities into the platform ecosystem
Mentor engineers through design reviews, technical guidance, and hands-on partnership, while elevating engineering quality across the team

What we offer

medical
dental
vision
Health Savings Account
Flexible Spending Accounts
retirement savings plan
sickness and accident benefits
life insurance
paid vacation & holidays
tuition assistance programs

Fulltime

Staff Machine Learning Engineer, Credit Products (Square Financial Services)

The Credit and Lending team is responsible for the predictive intelligence that ...

Location

United States , Seattle

Salary:

194500.00 - 343100.00 USD / Year

Block

Expiration Date

Until further notice

Requirements

Minimum of 8 years of related experience with a Bachelor's degree
or 6 years and a Master's degree
or a PhD with 3 years experience, with a focus on developing and deploying machine learning and statistical models in production environments
A degree in a technical field (e.g., Computer Science, Mathematics, Statistics, Physics, or Engineering)
Strong quantitative intuition and data visualization skills
Full-stack proficiency preferred
The versatility to communicate clearly with both technical and non-technical audiences
A pragmatic approach to problem-solving

Job Responsibility

Apply a rigorous scientific mindset to the challenge of underwriting new customer segments
Lead complex ML Operations and Infrastructure initiatives
Design and implement the full credit modeling stack
Use data science techniques to leverage new data sources for modeling
Identify and execute material improvements to credit policy
Support team members in ad-hoc and scheduled updates to existing models
Operate effectively within the framework of a regulated bank

What we offer

Remote work
medical insurance
flexible time off
retirement savings plans
modern family planning

Fulltime

Staff Machine Learning Engineer, Credit Products (Square Financial Services)

Block is one company built from many blocks, all united by the same purpose of e...

Location

United States , Bay Area

Salary:

Not provided

Block

Expiration Date

Until further notice

Requirements

Minimum of 8 years of related experience with a Bachelor's degree
or 6 years and a Master's degree
or a PhD with 3 years experience, with a focus on developing and deploying machine learning and statistical models in production environments
A degree in a technical field (e.g., Computer Science, Mathematics, Statistics, Physics, or Engineering)
Strong quantitative intuition and data visualization skills, with a proven ability to conduct sophisticated ad-hoc and exploratory analysis
Full-stack proficiency preferred, including the ability to contribute across the entire technical stack—from data pipelines to production-grade software architecture
The versatility to communicate clearly with both technical and non-technical audiences, particularly in the context of high-visibility projects and executive stakeholders
A pragmatic approach to problem-solving, with a willingness to utilize whichever tool is most appropriate for the situation while balancing complex business, technical, and regulatory constraints
Experience with tree-based models and gradient boosting is helpful but not required
we value the ability to adapt and learn new methodologies as the credit landscape evolves

Job Responsibility

Apply a rigorous scientific mindset to the challenge of underwriting new customer segments, involving the evaluation of alternative external data sources and the deployment of advanced architectures to enhance predictive accuracy
Lead complex ML Operations and Infrastructure initiatives that advance our modeling capabilities, such as scaling data ingestion or enabling the use of more complex neural networks
Design and implement the full credit modeling stack, taking responsibility for the entire lifecycle of credit decisioning and ensuring models are robustly integrated into production environments
Use data science techniques to leverage new data sources for modeling, making sense of messy datasets and bringing clarity to business decisions
Identify and execute material improvements to credit policy, applying an analytical lens to determine where technical or logic shifts can yield significant positive outcomes for the customer and the bank's portfolio
Support team members in ad-hoc and scheduled updates to existing models, and help troubleshoot issues in a real-time production environment
Operate effectively within the framework of a regulated bank (SFS), balancing rapid innovation with the requirements of safety, soundness, and compliance

What we offer

Remote work
Medical insurance
Flexible time off
Retirement savings plans
Modern family planning

Fulltime

Member of Technical Staff - Machine Learning Engineer

As Microsoft continues to push the boundaries of AI, we are on the lookout for p...

Location

United States , Mountain View

Salary:

119800.00 - 234700.00 USD / Year

Microsoft Corporation

Expiration Date

Until further notice

Requirements

Bachelor's Degree in Computer Science, or related technical discipline AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience
3+ years of experience building and deploying ML models in production environments
Strong coding skills in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow)
Familiarity with data processing tools (e.g., Spark, Pandas) and cloud platforms (e.g., Azure, AWS)
Experience with classification, recommendation, or personalization systems
Experience using large language models (LLMs) for machine learning and AI applications
Hands-on experience in growth engineering, driving improvements in user acquisition, engagement, and retention
Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn
Expertise in personalization strategies and user behavior modeling
Strong problem-solving skills and the ability to independently design solutions to complex challenges

Job Responsibility

Develop and Deploy Models: Design, develop, and implement machine learning models for high-performance recommendation systems and personalized feeds
Large Language Model Expertise: Leverage large language models (LLMs) to create scalable, intelligent solutions for content understanding, user engagement, and relevance ranking
Experimentation and Analysis: Drive data-driven experimentation using A/B testing, advanced analytics, and statistical techniques to identify growth opportunities and refine algorithms
Infrastructure Optimization: Develop and optimize pipelines, tools, and infrastructure to support real-time decision-making, personalization, and predictive analytics
Technical Leadership: Mentor team members and foster collaboration within cross-functional teams, including engineers, product managers, and designers
Continuous Innovation: Stay informed on emerging trends in AI and machine learning, and integrate them to drive innovation and improve product offerings
Cross-functional Collaboration: Articulate findings and recommendations to technical and non-technical audiences, influencing decisions across teams and leadership
Embody our Culture and Values

Fulltime

Sr Staff Machine Learning Engineer - Ads

Ads is a growing business at Uber. As part of this team, you will get an opportu...

Location

United States , San Francisco; Sunnyvale

Salary:

267000.00 - 297000.00 USD / Year

Uber

Expiration Date

Until further notice

Requirements

Technical leadership and drive technical direction for ads delivery area
Lead the design and implementation of advanced ML systems for all ads products at Uber
Own end-to-end ML model lifecycle from research through production deployment and continuous optimization
Build scalable ML architecture and feature management systems supporting ads marketplace
Establish ML engineering best practices, monitoring, and operational excellence across the organization
Create platform abstractions that enable other ML engineers to iterate faster on improvements
Collaborate with ads marketplace product and science teams to productionize cutting-edge ML research
Work with platform engineering teams to ensure ML systems meet reliability and performance standards
Influence technical roadmaps across multiple teams through technical leadership and strategic thinking
Mentor and grow senior ML engineers, establishing technical standards and engineering culture

Job Responsibility

Provide technical leadership, and drive technical direction for the ads delivery area
Work with cross-functional teams to find opportunities and implement enhancements to improve consumer experience and unlock value for advertisers and Uber

What we offer

Eligible to participate in Uber's bonus program
May be offered an equity award & other types of comp
All full-time employees are eligible to participate in a 401(k) plan
Eligible for various benefits

Fulltime

Staff Machine Learning Engineer, Money (Founding ML Lead)

The Money org at DoorDash builds the financial products and platforms that power...

Location

United States , San Francisco; Los Angeles

Salary:

137100.00 - 299300.00 USD / Year

DoorDash

Expiration Date

Until further notice

Requirements

8+ years of industry experience building and deploying production-scale ML systems
Direct experience with credit, lending, or cash advance underwriting, including risk modeling and decisioning
Strong foundation in statistics, probability, and machine learning, and know how to apply them to noisy, real-world financial data
Fluent in Python (and/or Java, Scala, or C++) and experienced with ML frameworks such as XGBoost, PyTorch, TensorFlow, or similar
Designed and operated ML systems in production, including monitoring, retraining, and model governance
Can lead complex technical projects end-to-end, influencing stakeholders across multiple orgs
Communicate clearly and effectively with technical and non-technical audiences
Excited about building something new and operating with ambiguity at high ownership

Job Responsibility

Lead the design, development, and deployment of production-grade ML systems that drive financial decisioning across DoorDash’s Money ecosystem
Own ML-driven underwriting models for merchant cash advances, partnering closely with Product, Risk, Data Science, and Engineering to improve approval rates, loss performance, and capital efficiency
Broaden scope to support additional initiatives such as banking products, payouts, and consumer payments optimization
Set best practices for model development, deployment, monitoring, and governance across the Money org
Influence architecture, strategy, and execution beyond a single team
Help shape the future of Money ML at DoorDash, with the opportunity to mentor others and grow a team over time

What we offer

401(k) plan with employer matching
16 weeks of paid parental leave
Wellness benefits
Commuter benefits match
Paid time off
Paid sick leave
Medical, dental, and vision benefits
11 paid holidays
Disability and basic life insurance
Family-forming assistance

Staff Machine Learning Engineer - AI Platform

You will join our Data Department to support the development of Phantom Intellig...

Location

Salary:

Not provided

PhantomBuster

Expiration Date

Until further notice

Requirements

5+ years of experience as a Data Scientist or Machine Learning Engineer
Experience working with LLMs (e.g., prompt engineering, fine-tuning, retrieval-augmented generation)
Experience working with Agents for Amazon Bedrock AgentCore or similar agent setups
Strong understanding of machine learning algorithms, statistical methods, and data preprocessing techniques
Experience with cloud platforms for model training and deployment, especially AWS
Proficiency in Python, including experience with libraries such as LangChain, Scikit-Learn, NumPy, Pandas, and PyTorch/TensorFlow
Proficiency in SQL and experience working with data warehouses (e.g., Snowflake, GCP)
Knowledge of MLOps best practices, including CI/CD pipelines, model monitoring, and versioning (e.g., MLflow, Airflow)
Experience deploying models to production and supporting them post-deployment
Fluency in English

Job Responsibility

Define and evolve our infrastructure to allow for better ML and AI capabilities, with a focus on LLM-based and agentic systems
Contribute to the development and expansion of our agentic AI framework powered by AWS Bedrock, enabling both internal tools and customer-facing features
Identify, source, and refine datasets to allow tuning models, powering retrieval pipelines, or expanding agentic workflows
Pre-process data by using techniques such as data cleaning, feature engineering, and transformation
Train, evaluate, and deploy both LLM-based systems and traditional machine learning models into production
Monitor, debug, and continuously improve deployed models and AI tools
Support machine learning usage throughout the company, including selecting the right modeling approach for the use case (LLM vs. traditional ML)
Support the integration and use of LLMs, including approaches such as fine-tuning, prompt tuning, and retrieval-augmented generation (RAG), to improve accuracy

What we offer

Fully remote working environment
€40/month for remote work
Flexible working time
Home office budget up to €1500
100% of an Alan Blue subscription (french-based contracts)
Lunch vouchers - €8 (50% The Phantom Company) / worked day (french-based contracts)
Partnership with MokaCare
€70 a month benefit for entertainment expenses
Book Allowance and Sharing Program

Fulltime

Staff Machine Learning Engineer - Ads

The Ads Machine Learning team at Uber is responsible for designing, building, an...

Location

United States , New York; San Francisco

Salary:

30.00 USD / Hour

Uber

Expiration Date

Until further notice

Requirements

Bachelor’s degree or equivalent experience in Computer Science, Computer Engineering, Data Science, Machine Learning, Statistics, or a related quantitative field
Demonstrated ownership of designing, deploying, and evolving large scale machine learning systems powering ads ranking, auction, or pricing in production environments
Strong proficiency in Python for building production ML systems and defining model, feature, and training abstractions used across teams
Deep understanding of SQL with experience driving production decision making, data validation, and system level analysis
Strong grasp of big data and distributed system architectures, with experience designing data platforms and ETL pipelines that support Ads ML workloads
Hands on experience building and operating batch data pipelines using Spark or comparable distributed compute frameworks, with accountability for data quality and correctness
Proven expertise in experimentation and evaluation, including A/B testing and offline metrics for ads auctions, ranking quality, and marketplace outcomes
Experience defining and operationalizing model and serving level metrics, and building observability for reliable online ML inference systems
Experience owning or influencing online model serving, including latency aware inference, scalability, and reliability considerations
Strong grounding in statistical methods, with the ability to reason about bias, uncertainty, and tradeoffs in ads and marketplace systems

Job Responsibility

Lead the design and evolution of machine learning models that power ads ranking, pricing, and auction systems at scale
Own end to end ML systems, including training pipelines, feature infrastructure, and low latency online inference for real time and batch use cases
Apply advanced statistical and ML techniques to improve ads relevance, marketplace efficiency, and advertiser outcomes
Define experimentation strategies, success metrics, and evaluation frameworks, and drive iteration through rigorous offline and online testing
Establish model and system observability through metrics, dashboards, and reliability best practices
Translate ambiguous product goals into durable ML architectures in close partnership with Product and Engineering
Provide technical leadership through mentorship, design reviews, and raising engineering standards across the Ads ML org
Stay current on advances in machine learning and ads auction systems, and drive adoption where they deliver clear impact

Fulltime

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Staff Machine Learning Engineer (Modeling), Support

Job Description

Job Responsibility

Requirements

What we offer

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