Senior Software Engineer - Machine Learning

Uber Freight

$183K — $223K *
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years of machine learning engineering experience
  • Experience deploying ML models at scale using frameworks like PyTorch or TensorFlow
  • Proficiency in programming languages such as Python, Go, or Java
  • Experience with machine learning models in production environments with large data sets
  • Solid understanding of machine learning theory and a variety of algorithms including deep learning and statistical models

Responsibilities

  • Drive development of high-impact solutions for Uber Freight's marketplace
  • Oversee end-to-end lifecycle of predictive models from research to production
  • Identify step-change opportunities using advanced techniques in ML
  • Optimize logistics operations and network effects via core ML
  • Collaborate with cross-functional stakeholders to deliver scalable models

Benefits

  • Company sponsored health plan
  • Dental and vision coverage
  • 401k matching
  • Financial and mental wellness benefits
  • Parental leave
  • Short- and long-term disability coverage
  • Life insurance
  • Performance or sales incentive bonuses
  • Participation in equity awards program
Full Job Description
Schedule: Full Time Employment

Job Type: Hybrid

Salary Type: Salary

Req #: 2536

No immigration sponsorship or transfer available for this role.

What the candidate will do

As a Senior Engineer you will drive the development of high-impact solutions for Uber Freight's marketplace and operations by leveraging a deep foundation in traditional machine learning alongside emerging Generative AI applications. You will spearhead the end-to-end lifecycle of predictive models-from identifying step-change opportunities and researching advanced techniques to overseeing rapid prototyping and robust production monitoring at scale. While your primary focus will be on optimizing logistics operations and network effects through core ML, you will also guide the team in integrating GenAI and Agentic systems where they drive the most business value, collaborating with cross-functional stakeholders to deliver scalable, production-ready models that redefine efficiency.

Basic Qualifications
  • At least 5+ years of Machine Learning engineering experience
  • Experience deploying ML models at scale using frameworks like PyTorch, TensorFlow, Scikit-Learn, or Spark MLlib
  • Experience in one or more programming languages including Python, Go or Java
  • Proven experience with designing and implementing machine learning models in production environments with large data sets
  • Deep understanding of ML theory and a broad toolkit of algorithms, including deep learning, instance-based learning, and traditional statistical models


Preferred Qualifications
  • BS, MS or PhD degree in computer science, Data Science, ML or equivalent practical experience
  • Experience with designing and implementing machine learning models in production environments applied to generative AI, applications of large language models
  • Experience in stream processing--Storm, Spark, Flink etc.-- and graph processing technologies.
  • Explain & communicate Algorithm choices, ML system design & concepts to leadership, technical peers & industry experts
  • Strong adherence to metrics driven development, with a disciplined and analytical approach to product development.


Benefits & Compensation for U.S. Employees

Employees working more than 30 hours in the US at Uber Freight are eligible for benefits like a company sponsored health plan, dental and vision benefits, 401k match, financial and mental wellness benefits, parental leave, short- and long-term disability coverage, life insurance and more. US based employees may also be eligible for a performance or sales incentive bonus program, participation in Uber Freight equity awards, and other types of compensation depending upon the role.

For California-based roles: The salary range for this role is $183,000.00 - $223,200.00 per year

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