Staff Machine Learning Engineer, Compute Job at General Motors (Austin)

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Staff Machine Learning Engineer, Underwriting and Credit

Block has provided over $200 billion in credit to customers globally. Afterpay a...

Location

United States , Bay Area

Salary:

276800.00 - 415200.00 USD / Year

Block

Expiration Date

Until further notice

Requirements

A Bachelor's degree in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science). Advanced degrees welcome.
10+ years applying AI, machine learning, or statistical modeling in decisioning contexts such as credit, risk, fraud, recommendations, or similar domains.
Experience with probabilistic models and decision systems, including calibration, score transformations, and interpretation of model outputs.
Strong experimentation skills: you know how to design holdouts, measure lift, and evaluate models beyond aggregate metrics.
Experience with model monitoring, degradation detection, and retraining strategies in production systems.
Proficiency with AI-native development workflows. You use LLMs, agentic coding tools, and AI-assisted automation as a regular part of how you build and ship.
Experience explaining modeling concepts, results, and limitations to senior stakeholders and cross-functional partners.
Experience working across disciplines in environments with meaningful constraints.

Job Responsibility

Build, evaluate, and maintain underwriting and decisioning models across Cash App Borrow and Afterpay.
Design and evolve credit decision frameworks, including the modeling, automation, and policy logic that manage credit exposure over time.
Design and run experiments to evaluate model performance, measure impact on approval rates and loss, and inform credit policy decisions.
Develop deep understanding of borrower behavior, repayment dynamics, and portfolio structure across both products, and use that to inform model design and decision logic.
Contribute analysis and perspective that inform portfolio-level decisions, including explaining model behavior, tradeoffs, and uncertainty to senior technical and business leaders.
Work across the full modeling lifecycle: problem formulation, feature engineering, training, calibration, deployment, monitoring, and iteration in production.
Build agentic engineering workflows that accelerate development, testing, and documentation.
Collaborate with Product, Engineering, Legal, Compliance, and Operations to ensure credit systems reflect business goals and regulatory expectations.
Share modeling context and approaches across teams, helping align how credit risk is measured, interpreted, and discussed.
Shape how AI developer tooling is adopted across the team, defining review practices, quality standards, and governance patterns.

What we offer

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

Fulltime

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 - Rider Intelligence

Rider Experience drives and enables the critical trip booking funnel within the ...

Location

United States , Seattle

Salary:

232000.00 - 258000.00 USD / Year

Uber

Expiration Date

Until further notice

Requirements

Ph.D., M.S. or Bachelor in Computer Science, Mathematics with focus on Machine Learning, or equivalent technical background with exceptional demonstrated impact
8+ years experience leading the development and deployment of ML models in large-scale production environments at top-tier ML companies

Job Responsibility

Define and execute technical strategies, spanning from model and system architecture to business objectives and stakeholder alignment
Lead the design, development, and production of end-to-end ML solutions for large-scale distributed systems serving billions of trips
Lead and mentor a team of Machine Learning Engineers (MLEs), providing technical leadership, setting the vision, and guiding the team through the full development lifecycle—from ideation to model deployment and scaling

What we offer

Eligible to participate in Uber's bonus program
May be offered an equity award & other types of comp
Eligible to participate in a 401(k) plan
Various benefits

Fulltime