Machine Learning Optimization

Machine Learning Optimization Developer 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, working with ESPN, is seeking a Sr Machine Learning Engineer based in New York, NY (onsite) to lead the end-to-end journey of advanced deep learning models. This role focuses on designing, building, and productionizing models used in large-scale recommendation and ranking systems, with ownership spanning architecture, training methodology, offline evaluation, inference optimization, and online serving code.

The position partners closely with platform and infrastructure teams to ensure successful deployment and reliable operations. Responsibilities also include defining model interfaces and observability signals, coordinating safe release criteria, and contributing to technical standards and design reviews for deep learning and recommender-system development.

What you’ll do

Lead 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 emphasis on correctness, modularity, testing, reproducibility, and maintainability.
Optimize online performance for latency, throughput, memory efficiency, and cost, using techniques such as distillation, quantization, pruning, batching, compilation, and efficient architecture design where appropriate.
Own the productionization path from prototype through offline validation and online integration, ensuring consistency between training and inference behavior.
Design offline experiments, ablation studies, and evaluation frameworks, partnering with data science and product teams to measure business impact with online experiments.
Diagnose model behavior and failure modes, including bias, calibration, cold start, feedback loops, delayed labels, distribution shift, and relevance-quality tradeoffs.
Collaborate with MLOps, platform, data, and serving teams to define model interfaces, runtime requirements, observability signals, and safe release criteria (without owning general ML infrastructure).
Participate in technical design reviews, help set model engineering standards, and mentor engineers on deep learning, recommender systems, and production-quality development.

Requirements

Strong theoretical and applied knowledge of deep learning, including optimization, representation learning, regularization, generalization, and modern neural architectures.
Proven experience designing and launching deep learning models in production, with ownership beyond notebook experimentation to production-quality implementations.
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, plus strong Python software engineering skills.
Experience improving inference performance and efficiency, including practical understanding of latency, throughput, memory, hardware utilization, and model-quality tradeoffs.
Experience with large-scale model 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 effectively with product, data science, data engineering, platform, and infrastructure partners.
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 similar inference acceleration technologies.
Ability to design models for strict real-time serving constraints and measure p95/p99 latency, throughput, memory footprint, and cost-to-serve.
Knowledge of recommender-system challenges such as 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 working in fast-paced or 0-to-1 environments with ambiguous requirements.
5+ years of hands-on experience developing machine learning systems, including substantial experience designing and productionizing deep learning models.
Bachelor’s or Master degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent practical experience.

Technologies

PyTorch
TensorFlow
Python
ONNX
TensorRT
CUDA

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 consider internal equity and may vary depending on geographic region, job-related knowledge, skills, and experience.
A bonus and/or long-term incentive units may be included, in addition to full-range 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

Build production ML infrastructure at scale with Goliath Partners in San Francisco, CA (onsite). This Senior Machine Learning Engineer role sits at the intersection of ML infrastructure and distributed systems, helping connect ML Infrastructure Engineers to deliver distributed model training and high-performance inference using modern cloud and GPU tooling. The focus is end-to-end: design, performance optimization, and owning infrastructure that supports demanding AI workloads.

What you’ll do

Build and scale the infrastructure powering cutting-edge AI systems, from distributed model training to high-performance inference.
Develop scalable distributed training and inference infrastructure that can operate reliably at scale.
Optimize system performance, reliability, latency, and throughput for production workloads.
Design and own production ML infrastructure, ensuring it meets operational and performance requirements.

What you bring

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 performance and reliability, including latency and throughput.
Experience supporting large-scale LLM or foundation model training.
Expertise with 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.

Tech you’ll work with

Python, C++, Go, PyTorch, JAX, Kubernetes, Docker, AWS, GCP, CUDA, Triton, NCCL, 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.

Compensation: USD 2,000,000 per year.

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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 building personalized search and recommendation experiences, and it needs Machine Learning Engineers who can own major parts of the system from architecture through evaluation and deployment. This role spans end-to-end work across ranking, retrieval, and query understanding, with a focus on raising the bar for both model quality and operational reliability.

As a Senior to Senior Staff Machine Learning Engineer, you will help set technical direction across teams, translate complex technical trade-offs into business terms, and mentor engineers so critical systems do not rely on a single person.

What you will own

End-to-end architecture for the ranking and recommendation stack, including candidate retrieval, multi-objective ranking, calibration, and the ensemble that balances conversion against profitability.
Cross-system design decisions that extend beyond the scope of a single model owner.
Evolution of the optimization objective from short-term conversion toward long-term diner value, including offline evaluation and online experimentation to validate results before shipping.
Production runtime integration of state-of-the-art methods for information retrieval and recommender systems, including:
- LLM-driven query and intent understanding
- Embedding-based retrieval
- Sequential user representations for cold start
- Real-time inference
Rigorously assess what transfers to Wonder’s traffic and decline changes that do not hold up in practice.

How you will raise standards

Raise engineering and operational standards across multiple teams, including model evaluation and scorecards, reproducible training pipelines, safe deployment, and SLOs with observability for tier-1 models.
Proactively manage technical debt across systems before it becomes urgent.

Collaboration and leadership

Partner with Product, Search Engineering, Ads, and the Data Platform to shape roadmaps, surface risks early, and ensure the right data and infrastructure exist before model needs arise.
Mentor senior and mid-level engineers, participate in hiring, and grow team technical depth.
Translate technical trade-offs into business terms for product and executive stakeholders, with documentation that preserves rationale over time.
Continuously question assumptions, explore innovations not yet pursued, and improve business performance through persistent analysis.

Qualifications

MS/PhD in a quantitative discipline (Computer Science, Math, Physics, Engineering, Statistics or other technical field) or equivalent experience.
4-8+ years building and shipping machine learning systems.
For staff level: 3+ years operating at 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.
Experience with language understanding over imperfect grammar (real-world search queries, menu and catalog text) is a strong plus.
Strong data engineering fundamentals: PySpark, Hive/SQL, the Python data stack, and feature pipelines you can debug and build.
Experimentation fluency: designing A/B tests, selecting guardrails, and recognizing when offline metrics can mislead.
Experience with cloud ML infrastructure such as AWS/SageMaker, plus model deployment and production monitoring/observability.
Demonstrated technical leadership through mentoring, driving architecture reviews beyond your team, and influencing decisions without direct authority.
Ability to communicate performance metrics, model behavior, and technical trade-offs to technical and non-technical audiences, including executives.
Comfort staying current with research publications and synthesizing them into production systems.
Strong interest in self-motivated continuous learning.

Technologies

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

Compensation and location

Location: New York, NY (onsite)
Base salary range: USD 176,000 - 260,000 per year
Senior MLE (New York Base Salary): $176,000 - $191,000
Staff MLE (New York Base Salary): $208,500 - $225,000
Senior Staff MLE (New York Base Salary): $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

Final note: Wonder participates in the federal government’s E-Verify program to confirm employment eligibility. If you need an accommodation during the interview process, contact your recruiter. Interviews will be scheduled and information shared via email or text.

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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

Build large-scale machine learning systems that directly improve how people move. As a Sr Machine Learning Engineer on Uber's Maps Optimization team, you will design, deploy, and maintain high-throughput, low-latency prediction models and APIs that power map experiences such as location search, ETA calculation, and routing across millions of trips worldwide. The role blends applied ML with production engineering, with an emphasis on measurable gains in logistical efficiency.

What you’ll do

Develop and deploy end-to-end machine learning models and the infrastructure needed to optimize Uber map-related applications
Design and maintain high-throughput, low-latency prediction systems and APIs that handle hundreds of billions of daily requests
Test and iterate on novel approaches to improve logistical efficiency across Uber product verticals
Collaborate with product managers, scientists, designers, and engineers to deliver solutions to real-world challenges

What you’ll bring

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

Technologies you may work with

Apache Spark, Hive, Presto, Michelangelo
Java, Scala, Golang
Python, shell scripts

Compensation and benefits

Salary range: USD 202,000 - 224,000 per year.

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

Preferred qualifications

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

Location and work setup

Sunnyvale, CA (hybrid)

Unless approved for full remote work, employees must spend at least 50% of their time in-office
Ask your Recruiter for details about this role’s specific requirements

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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 builds machine learning systems that help protect users and the platform through scalable fraud detection, identity verification, and account security. As a Staff Machine Learning Engineer, you’ll lead development efforts end to end, from defining ML problems through deployment and ongoing monitoring, while collaborating closely across product, data science, and engineering.

This role is based in San Francisco, CA on an onsite schedule. The annual salary range is USD 232,000 - 258,000, and the position requires 8+ years of relevant experience.

What you’ll do

Work with product, data science, and engineering partners to identify high-impact opportunities, formulate complex ML problems, and contribute to the team’s technical roadmap.
Use deep expertise in machine learning, optimization, and statistics to design and build scalable, robust ML solutions with measurable business impact.
Provide technical leadership across complex ML initiatives, promote engineering best practices, and mentor other machine learning engineers to raise team capability.
Own the full ML development lifecycle, including data preparation, feature engineering, model development, system design, implementation, A/B testing, deployment, and production monitoring.

What you bring

8+ years of industry experience developing ML models, including classical ML and deep learning, and deploying ML solutions to production.
Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related technical field.
Strong problem-solving skills and a solid foundation in machine learning algorithms, statistics, and optimization.
Experience applying ML techniques to solve complex, large-scale real-world problems.
Proficiency in Python and experience with 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.

Technologies

Python
PyTorch
TensorFlow
JAX
Spark
SQL

Preferred qualifications

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

Compensation and 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.

Office requirements

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

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Machine Learning Optimization Developer 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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