Machine Learning Optimization

Machine Learning Optimization Engineer Jobs

5 jobs found

Today

Sr Machine Learning Engineer

Disney Entertainment & Sports LLC New York, NY

New York, NY

Today

$144k - $202k/yr Full time Artificial Intelligence Machine Learning Deployment Machine Learning Engineering

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Job Description

Disney Entertainment & Sports LLC is seeking a Senior Machine Learning Engineer for ESPN to help build and productionize advanced deep learning models that power large-scale recommendation and ranking experiences. This onsite role in New York, NY will drive key architecture and training decisions, then partner with platform and infrastructure teams to bring models into reliable online serving.

The position focuses on end-to-end ownership across model design, offline evaluation, inference optimization, and online integration, with an emphasis on consistency between training and production behavior.

Responsibilities

Lead the design and implementation of advanced deep learning models for retrieval, ranking, and personalization, including embedding-based, sequence, transformer, and multi-task architectures.
Convert product and personalization goals into model formulations, loss functions, sampling strategies, feature representations, and evaluation plans grounded in machine learning theory.
Build production-grade training and inference code with standards for correctness, modularity, testing, reproducibility, and maintainability.
Optimize models for online performance, targeting latency, throughput, memory efficiency, and cost, using approaches such as distillation, quantization, pruning, batching, compilation, and efficient architectural design when appropriate.
Own the productionization path from prototype through offline validation and online integration, ensuring training and inference behavior remain aligned.
Create rigorous offline experimentation plans, ablation studies, and evaluation frameworks, and collaborate with data science and product teams to validate impact through online experiments.
Diagnose model behavior and failure modes, including bias, calibration, cold start, feedback loops, delayed labels, distribution shift, and relevance-quality tradeoffs.
Partner with MLOps, platform, data, and serving teams to define model interfaces, runtime requirements, observability signals, and safe release criteria, without owning general ML infrastructure.
Contribute to technical design reviews, establish model engineering standards, and mentor engineers on deep learning, recommender systems, and production-quality model development.

Requirements

Strong theoretical and applied knowledge of deep learning, including optimization, representation learning, regularization, generalization, and modern neural network architectures.
Experience designing and launching deep learning models in production with ownership that goes beyond notebook experimentation.
Hands-on experience with recommendation, ranking, retrieval, search, or personalization models at meaningful scale.
Expert proficiency in PyTorch, TensorFlow, or a comparable deep learning framework, along with strong Python software engineering skills.
Experience improving inference performance and efficiency, including practical understanding of latency, throughput, memory, hardware utilization, and quality tradeoffs.
Experience with large-scale training, distributed data processing, and production model evaluation.
Solid understanding of experimentation and evaluation, including metric selection, offline-online correlation, ablation analysis, and statistical validation.
Ability to communicate complex modeling decisions clearly and collaborate with product, data science, data engineering, platform, and infrastructure teams.
Experience with multi-stage recommender systems, including candidate generation, retrieval, ranking, re-ranking, and embedding-based approaches.
Deep expertise in one or more areas such as transformers, sequence modeling, multi-task learning, graph neural networks, self-supervised learning, contextual bandits, or reinforcement learning for recommendations.
Experience optimizing neural models with ONNX, TensorRT, CUDA, custom kernels, or comparable inference acceleration technologies.
Experience designing models under strict real-time serving constraints and measuring p95/p99 latency, throughput, memory footprint, and cost-to-serve.
Knowledge of recommender-system challenges including implicit feedback, negative sampling, selection bias, cold start, exploration versus exploitation, and delayed outcomes.
Research publications, patents, open-source contributions, or equivalent evidence of depth in deep learning or recommender systems.
Experience operating in fast-paced or 0-to-1 product development settings with ambiguous requirements.
5+ years of hands-on experience developing machine learning systems, including substantial deep learning model design and productionization experience.
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related field, or equivalent practical experience.

Technologies

PyTorch
TensorFlow
Python
ONNX
TensorRT
CUDA

Compensation and Pay Transparency

The hiring range for this position in New York, NY is $150,900 - $202,400 and for Glendale, CA is $144,100 - $193,200.
The base pay actually offered will take internal equity into account and may vary based on candidate geographic region and job-related knowledge, skills, and experience.
A bonus and/or long-term incentive units may be provided in addition to medical, financial, and/or other benefits, depending on the level and position offered.

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Today

Senior Machine Learning Engineer

Goliath Partners San Francisco, CA

San Francisco, CA

Today

$2,000k - $2,000k/yr Full time Artificial Intelligence Cloud Machine Learning Distributed Machine Learning

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Job Description

Senior Machine Learning Engineer role focused on connecting ML Infrastructure Engineers to build and scale infrastructure for distributed model training and high-performance inference.

Responsibilities

Build and scale infrastructure powering distributed model training and high-performance inference for production AI systems
Develop scalable distributed training and inference infrastructure
Optimize system performance, reliability, latency, and throughput
Design and own production ML infrastructure at scale

Requirements

5+ years of experience in ML Infrastructure, Distributed Systems, or Software Engineering, or equivalent demonstrated expertise
Strong programming skills in Python and C++ or Go
Hands-on experience with PyTorch, JAX, or similar ML frameworks
Experience building scalable distributed training and inference infrastructure
Strong knowledge of Kubernetes, Docker, AWS/GCP, and GPU compute environments
Proven ability to optimize system performance, reliability, latency, and throughput
Ability to design and own production ML infrastructure at scale
Experience supporting large-scale LLM or foundation model training
Expertise in CUDA, Triton, NCCL, or GPU performance optimization
Experience with distributed training frameworks such as Ray, DeepSpeed, or Megatron-LM
Contributions to high-performance ML systems or influential open-source infrastructure projects
Experience building infrastructure at leading AI research labs or high-growth technology companies

Technology Focus

Programming: Python, C++, Go
ML frameworks: PyTorch, JAX
Infrastructure & orchestration: Kubernetes, Docker
Cloud & compute: AWS, GCP, GPU environments
GPU optimization: CUDA, Triton, NCCL
Distributed training frameworks: Ray, DeepSpeed, Megatron-LM

Education

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Mathematics, or a related technical field
PhD in Computer Science, Machine Learning, or Distributed Systems is a plus
Exceptional engineers with demonstrated technical impact and deep systems expertise are strongly encouraged to apply

What Sets You Apart

Experience supporting large-scale LLM or foundation model training
Expertise in CUDA, Triton, NCCL, or GPU performance optimization
Experience with distributed training frameworks such as Ray, DeepSpeed, or Megatron-LM
Contributions to high-performance ML systems or influential open-source infrastructure projects
Experience building infrastructure at leading AI research labs or high-growth technology companies

Location: San Francisco, CA (onsite)

Compensation: USD 2,000,000 - 2,000,000 per yearly

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Today

Machine Learning Engineer

Wonder New York, NY

New York, NY

Today

$176k - $260k/yr Full time A/B Testing Artificial Intelligence AWS

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Job Description

Wonder is hiring Machine Learning Engineers (Senior through Senior Staff) to take ownership of end-to-end machine learning architecture supporting search ranking, homepage and topic recommendations, retrieval, and query understanding. The role combines technical direction, rigorous evaluation, and cross-team delivery of personalized recommendation and retrieval systems.

Role Overview

You will define system-level design across ranking and recommendation workflows, guide the evolution of optimization objectives, and bring research advancements into production environments. This position also emphasizes improving engineering and operational standards across multiple teams while mentoring engineers and partnering closely with Product and engineering stakeholders.

Key Responsibilities

Own the architecture of the ranking and recommendation stack end to end, including candidate retrieval, multi-objective ranking, calibration, and the ensemble that balances conversion against profitability.
Make cross-system design decisions that require system-wide trade-offs beyond a single model owner.
Lead the evolution of optimization objectives from short-term conversion toward long-term diner value, including offline evaluation and online experimentation to validate results prior to release.
Integrate state of the art information retrieval and recommender systems into runtime, including LLM-driven query and intent understanding, embedding-based retrieval, sequential user representations for cold start, and real-time inference.
Evaluate transfer of approaches to Wonder traffic and discontinue approaches that do not demonstrate value.
Raise engineering and operational standards across teams through model evaluation and scorecards, reproducible training pipelines, safe deployment practices, SLOs and observability for tier-1 models, and proactive technical debt management.
Partner with Product, Search Engineering, Ads, and the Data Platform to shape roadmaps, surface risks early, and ensure data and infrastructure readiness before model needs arise.
Mentor senior and mid-level engineers, participate in hiring, and increase technical depth so critical systems are not dependent on a single person.
Translate technical trade-offs into business terms for product and executive stakeholders and document rationales to preserve decision context over time.
Continuously challenge assumptions and analyze business performance to identify improvements and innovation not yet pursued.

Required Qualifications

MS/PhD in a quantitative discipline such as Computer Science, Math, Physics, Engineering, Statistics, or another technical field, or equivalent experience.
4-8+ years building and shipping machine learning systems.
For staff-level roles: 3+ years operating at a senior-level scope, including setting technical direction across multiple teams, model families, or systems.
Deep experience in recommendation systems, ranking, or information retrieval at scale in production with real latency and cost constraints.
Production deep learning experience with TensorFlow or PyTorch, including training, serving, and tuning runtime models on GPUs.
Experience with Large Language Models and transformer-based architectures, including fine-tuning, embedding generation, and deploying in latency-sensitive applications.
Strong plus: experience with language understanding over imperfect grammar, including real-world search queries and menu or catalog text.
Strong data engineering fundamentals including PySpark, Hive/SQL, the Python data stack, and feature pipelines that can be debugged and built.
Fluency with experimentation, including designing A/B tests, selecting appropriate guardrails, and identifying when offline metrics mislead.
Experience with cloud ML infrastructure such as AWS/SageMaker, model deployment, and production monitoring and observability.
Demonstrated technical leadership through mentoring, driving architecture reviews beyond your own team, and influencing decisions without direct authority.
Ability to communicate performance metrics, model behavior, and technical trade-offs to both technical and non-technical audiences, including executive stakeholders.
Ability to keep up with research publications and synthesize them into production systems.
Deep interest in self-directed continuous learning.

Technologies

TensorFlow
PyTorch
GPUs
Large Language Models
Transformer-based architectures
PySpark
Hive/SQL
Python
AWS
SageMaker
A/B tests

Location and Compensation

Location: New York, NY (onsite)
Salary Range: USD 176,000 - 260,000 per year

Base salary by level (New York):

Senior MLE: $176,000 - $191,000
Staff MLE: $208,500 - $225,000
Senior Staff MLE: $240,000 - $260,000

Benefits

Competitive compensation package with equity
401(k)
Choice of medical, dental, and vision plans
Company paid short and long term disability coverage
Paid time off including flexible time off for exempt employees
Paid vacation for non-exempt employees
Paid sick leave in compliance with applicable law
Paid parental leave
Discounted meals and exclusive perks across the Wonder family of brands

Additional Information

Wonder participates in the federal government's E-Verify program to confirm employment eligibility.
If you need an accommodation during the interview process, notify your recruiter.
Interviews will be scheduled via email or text, and information about your candidacy will be shared accordingly.

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6d ago

Sr Machine Learning Engineer- Maps Optimization

Uber Sunnyvale, CA

Sunnyvale, CA

6d ago

$202k - $224k/yr Full time Ai Ml Artificial Intelligence Big Data

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Job Description

Sr Machine Learning Engineer on Uber’s Maps Optimization team, focused on building and optimizing large-scale ML systems that improve map experiences across location search, ETA calculation, and routing.

Develop and deploy end-to-end machine learning models and supporting infrastructure for map-related applications
Design and maintain high-throughput, low-latency prediction systems and APIs
Support prediction workloads that handle hundreds of billions of daily requests
Apply and iterate on new approaches to improve logistical efficiency across Uber product verticals
Collaborate with product managers, scientists, designers, and engineers to deliver solutions to real-world problems

Requirements

5+ years of machine learning experience, or 2+ years with a PhD
2+ years of experience in one or more areas: machine learning, artificial intelligence, optimization, signal processing, operational research, or a related technical field
Experience building, debugging, and scaling distributed data processing and ML pipelines (examples include Apache Spark, Hive, Presto, Michelangelo)
Knowledge of development and debugging in Java, Scala, or Golang
Experience with scripting languages such as Python and/or shell scripts
Technical leadership experience, including working with teams, owning projects, and setting technical direction
Bachelor’s degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience

Technologies

Apache Spark
Hive
Presto
Michelangelo
Java
Scala
Golang
Python
Shell scripts

Preferred Qualifications

Experience architecting large-scale, production software applications
Experience productionizing applied machine learning solutions (for example, graph/graphless prediction, learning to rank)
Experience tackling complex ML challenges using production ML
Master’s degree or PhD in Computer Science or a related technical field

Compensation & Location

Location: Sunnyvale, CA (hybrid)
Salary: USD 202,000 - 224,000 per year

Benefits

Eligible to participate in Uber’s bonus program
May be offered an equity award and other types of compensation
Eligible to participate in a 401(k) plan
Eligible for various benefits

Additional Notes

Unless approved for full remote work, employees must spend at least 50% of their time in-office
Some roles, such as those at greenlight hubs, require full-time in-office presence
Ask your Recruiter for details about this role’s requirements

Base salary range for this Sunnyvale, CA-based role: USD $202,000 per year - USD $224,000 per year.

All US locations: You will be eligible to participate in Uber’s bonus program, and may be offered an equity award and other types of compensation. All full-time employees are eligible to participate in a 401(k) plan.

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7d ago

Staff Machine Learning Engineer - Risk and Trust

Uber San Francisco, CA

San Francisco, CA

7d ago

$232k - $258k/yr Full time Ai Ml Artificial Intelligence Data Analytics

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Job Description

Uber’s Risk and Trust engineering organization is seeking a Staff Machine Learning Engineer to lead scalable machine learning initiatives focused on fraud prevention, identity verification, and account security. This role provides technical leadership across the end-to-end ML lifecycle and works closely with product, data science, and engineering partners to deliver measurable outcomes.

Role Responsibilities

Partner with product, data science, and engineering teams to identify high-impact business opportunities, define complex machine learning problems, and contribute to the team’s technical roadmap.
Use expertise in machine learning, optimization, and statistics to design and build innovative, scalable, and reliable ML solutions with measurable business impact.
Lead technical execution for complex ML efforts, establish engineering best practices, and mentor other machine learning engineers to strengthen team capability.
Own end-to-end development across data preparation, feature engineering, model development, system design, implementation, A/B testing, deployment, and production monitoring.

Required Qualifications

8+ years of industry experience developing machine learning models, including classical ML and deep learning, and deploying ML solutions into production.
Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related technical field.
Strong problem-solving skills with a foundation in machine learning algorithms, statistics, and optimization.
Proven ability to apply ML techniques to complex, large-scale, real-world problems.
Proficiency in Python and experience using modern ML frameworks such as PyTorch, TensorFlow, or JAX.
Experience building large-scale data pipelines and working with distributed data processing frameworks such as Spark and SQL-based systems.

Preferred Qualifications

PhD in Computer Science, Engineering, Mathematics, Statistics, or a related technical field.
Hands-on experience with advanced ML techniques, including deep learning, representation learning, sequence modeling, multi-task learning, reinforcement learning, or large language models.
Experience developing ML solutions for fraud detection, anomaly detection, or other adversarial environments.
Ability to independently drive complex ML projects from problem formulation through production deployment.
Experience working with large-scale, real-time ML systems and making data-driven decisions in production environments.

Tools and Technologies

Python
PyTorch
TensorFlow
JAX
Spark
SQL

Compensation and Benefits

Base salary range: USD $232,000 - $258,000 per year for San Francisco, CA-based roles.
Eligible to participate in Uber’s bonus program.
May be offered an equity award and other types of compensation.
Eligible to participate in a 401(k) plan.
Eligible for various benefits.

Work Location and Office Expectations

Location: San Francisco, CA (onsite).

Unless approved for full remote work, employees must spend at least 50% of their time in-office.
Some roles may require full-time in-office presence, such as roles at greenlight hubs.

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Machine Learning Optimization Engineer Jobs

Sr Machine Learning Engineer

Senior Machine Learning Engineer

Machine Learning Engineer

Sr Machine Learning Engineer- Maps Optimization

Staff Machine Learning Engineer - Risk and Trust

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