Machine Learning Inference

Machine Learning Inference Data Jobs

6 jobs found

Today

Machine Learning Engineer, Natural Language Understanding, Proactive

Apple Santa Clara, CA

Santa Clara, CA

Today

$150k - $278k/yr Full time Ai Ml Artificial Intelligence Deep Learning

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Job Description

Apple’s On-Device Query Understanding team is seeking a Machine Learning Engineer to research, design, implement, and evaluate machine learning models for personalized query understanding. The work supports search and assistant experiences across Apple products, with an emphasis on LLM/NLU approaches and inference optimization to achieve fast, power-efficient performance on-device.

What You’ll Do

Research, design, implement, and evaluate models that advance personalized query understanding, including user intent resolution, retrieval, and ranking.
Use natural language processing, deep learning, and large language model techniques to deliver grounded, contextually relevant responses.
Assess performance gaps in existing search and assistant systems and develop algorithms to improve model quality and key product outcomes.
Define the long-term technical vision for the team, propose roadmaps, and set measurable objectives.
Collaborate with cross-functional partners to define product requirements, establish evaluation metrics, and maximize user satisfaction.

Required Qualifications

4 or more years of experience in machine learning, natural language processing, or computer vision applied at scale.
Production software engineering experience using one or more of the following: Python, Go, C/C++, Swift, or Objective-C.
Strong communication and collaboration skills.
Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Statistics, Physics, Mathematics, or a related field.

Preferred Qualifications

Advanced degree (Master’s or Ph.D.) in Computer Science, Statistics, or a related field, or equivalent industry experience.
Experience with Small Language Models (SLMs) and on-device LLM training, including model compression/quantization and production deployment.
Experience with large-scale query understanding, search, recommendation, or personal assistant systems.
Proficiency with machine learning frameworks such as PyTorch, JAX, TensorFlow, or XGBoost.
Background in personalization and data-driven decision making, including the ability to prototype solutions and conduct critical analysis.
Experience with Swift, Swift frameworks, or Apple platform development is a plus.
Experience with build infrastructure, release management, and CI/CD pipelines is a plus.

Key Technologies

Large Language Models (LLMs), Natural Language Understanding (NLU)
Machine learning inference optimization
PyTorch, JAX, TensorFlow, XGBoost
Swift, Objective-C
Small Language Models (SLMs), model compression/quantization
Python, Go, C/C++

Compensation & Benefits

The base pay range for this role is USD 150,400 to 277,600 per year.

Comprehensive medical and dental coverage.
Retirement benefits.
Discounted products and free services.
Reimbursement for certain educational expenses, including tuition.
Discretionary bonuses or commission payments, and relocation may be eligible.
Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs.
Eligibility for discretionary restricted stock unit awards.
Option to purchase Apple stock at a discount by voluntarily participating in Apple’s Employee Stock Purchase Plan.

Additional information about Apple Benefits is available through Apple.

Role Details

Location: Santa Clara, CA (onsite)
Experience: 4+ years
Salary: USD 150,400 - 277,600 per yearly

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Today

Sr Machine Learning Engineer - ML Data

Apple Cupertino, CA

Cupertino, CA

Today

$185k - $278k/yr Full time Backend Developer Ad Tech Agentic Ai

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Job Description

Apple’s Ads Machine Learning Platform team builds secure, scalable back-end systems and ML products that help improve and scale advertising features while protecting user privacy. In this role, you’ll contribute to platform capabilities used for scalable modeling and continuous experimentation, partnering closely with ML engineers and scientists to deliver machine learning experiences that align with Apple’s privacy commitments.

Responsibilities

Design and develop secure, scalable back-end systems.
Build high-performing and elegant systems from the ground up.
Collaborate with ML engineers and scientists to create platform capabilities that enable Ads teams to improve and scale ML features, models, and applications.
Select technologies and develop solutions tailored to unique ad network challenges.
Play a meaningful role building machine learning products that support Apple’s privacy commitments and change how advertising works with data.
Contribute to reliability, simplicity, and scalability across systems and services.

Requirements

Experience writing mission-critical production code for machine learning systems.
Experience building ML infrastructure, frameworks, or services used by multiple teams.
Experience building or operating feature stores or comparable ML data infrastructure serving production models.
Experience with embedding management, including generating and versioning embeddings, refresh and retirement policy, and storing and serving for retrieval at scale.
Solid understanding of the ML lifecycle: training, evaluation, deployment, and serving/inferencing, with experience building and deploying models.
Understanding of model evaluation, train-serve skew, and data drift.
Working knowledge of deep learning architectures and training frameworks such as PyTorch or TensorFlow.
Prior experience applying ML at scale in advertising, recommender systems, information retrieval, or related domains.
Experience generating training data across multi-modal data (text, image, structured), including sampling and point-in-time correctness.
Experience building production data pipelines for ML systems where scale and performance are critical using distributed processing systems.
Experience building ML systems using batch and streaming deployments, workflow orchestration, and modern storage formats.
Strong data modeling and data architecture skills, with a high bar for system and data quality: correctness, reliability, testing, and validation.
Strong problem-solving, debugging, and performance tuning skills, with pride in automation, tooling, and CI/CD.
Results-oriented approach with effective communication in written and verbal form across technical and non-technical multi-functional teams.
Product-minded mindset with demonstrated ownership of projects.

Technologies

PyTorch, TensorFlow, LLM-based data generation or evaluation, agentic AI

Preferred Qualifications

Experience in the advertising industry.
Experience with LLM-based data generation or evaluation.
Familiarity with large-scale distributed training and its demands on data infrastructure.
Prior experience in privacy-preserving ML.
Familiarity with agentic AI.
Academic background options: PhD in Computer Science or related field with 3+ years of engineering experience and 5+ years of machine learning experience; or MS in Computer Science or related field with 6+ years of engineering experience and 5+ years of machine learning experience; or BS in Computer Science or related field with 7+ years of engineering experience and 5+ years of machine learning experience.

Pay & Benefits

Base pay range: USD 184,700 to 277,600 per year.
Participation in Apple’s discretionary employee stock programs.
Eligible for discretionary restricted stock unit awards.
Ability to purchase Apple stock at a discount if voluntarily participating in the Employee Stock Purchase Plan.
Reimbursement for certain educational expenses, including tuition.
Comprehensive medical and dental coverage.
Retirement benefits.
A range of discounted products and free services.
Discretionary bonuses or commission payments and relocation may apply.
Apple benefit, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Location: Cupertino, CA (onsite). Minimum experience: 3 years.

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Today

Lead Machine Learning Engineer, Human Sensing

Apple Seattle, WA

Seattle, WA

Today

$175k - $309k/yr Full time Manager Ai Ml Artificial Intelligence

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Job Description

Apple’s Human and Object Understanding (HOUr) team is hiring a Lead Machine Learning Engineer (Technical Lead) to set technical direction for a multimodal Human Sensing team in Seattle, WA.

Partner with engineering management as the primary technical lead to define project scope, technical milestones, and roadmap execution
Set KPI targets and quality benchmarks across demographics, environmental conditions, and device use cases
Define dataset collection, annotation, and curation strategy with the Data team to reduce model blind spots
Architect and lead the team’s evaluation framework and benchmarking pipelines (custom metrics, evaluation scripts, automated tooling)
Stress-test models against production scenarios through evaluation and benchmarking automation
Lead failure mode analysis: root-cause investigation and edge-case discovery to drive model and data iterations
Coordinate day-to-day technical execution across Evaluation, Integration, and Data Operations teams
Drive model optimization in partnership with integration and other partner teams
Train, fine-tune, and run experiments with state-of-the-art vision architectures when needed for research and hypothesis validation
Maintain the team’s core codebase: author and review PRs, uphold engineering hygiene, and enable rapid iteration velocity
Communicate technical strategy, performance trade-offs, and progress to stakeholders and senior leadership
Guide and mentor junior and mid-level engineers
Stay current with machine learning trends and best practices across multimodal foundation models, computer vision, and natural language understanding

Requirements

Master’s or Ph.D. in Computer Science, Computer Engineering, or related field (or equivalent practical experience)
6+ years of industry experience in Computer Vision and Machine Learning
Proven Technical Lead or Staff-level experience driving project scoping, KPI setting, and technical initiatives across cross-functional teams
Expertise in evaluating complex ML systems, defining benchmarking methodologies, and conducting deep-dive failure analysis
Ability to coordinate engineering teams and mentor peers while partnering closely with management on roadmap execution
Strong ownership mindset, attention to detail, and agility in fast-evolving research environments
Deep proficiency in Python and PyTorch, including hands-on experience writing clean, maintainable code and managing shared repositories

Technologies

Python, PyTorch
Core ML
Quantization-aware training, knowledge distillation
Latency profiling, quantization, pruning
Multimodal foundation models, computer vision, natural language understanding
Face recognition, identity re-identification (ReID)
Foundation vision models
Large-scale Vision-Language Models (VLMs), large language models (LLMs)
Multimodal large language models (LLMs), large-scale vision-language models (VLMs)

Benefits

Comprehensive medical and dental coverage
Retirement benefits
Range of discounted products and free services
Reimbursement for certain educational expenses, including tuition
Discretionary bonuses or commission payments (may be eligible)
Relocation (may be eligible)
Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
Eligible for discretionary restricted stock unit awards
Can purchase Apple stock at a discount with voluntary participation in the Employee Stock Purchase Plan

Preferred Qualifications

Domain knowledge in face recognition, identity re-identification (ReID), biometrics, or visual human sensing (pose, expression, human-object interaction)
Experience collaborating with Data Collection & Annotation teams on collection protocols and active learning datasets
Experience with on-device model optimization (quantization-aware training, knowledge distillation, Core ML conversion, latency profiling)
Experience with foundation vision models or large-scale Vision-Language Models (VLMs)
Hands-on experience training and scaling multimodal LLMs or large-scale VLMs
Experience with on-device ML, model optimization (knowledge distillation, quantization, pruning), or production-grade ML pipelines
Research and innovation background shown through publications, patents, or impactful software developments

Pay & Benefits

Base pay range: $175,000 to $308,500 per year
Base pay depends on skills, qualifications, experience, and location
Apple also offers eligibility for discretionary restricted stock unit awards and an Employee Stock Purchase Plan discount (if voluntarily participating)
Note: Benefits, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program

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Today

Sr. Machine Learning Engineer - Answers, Knowledge & Information (AKI)

Apple Santa Clara, CA

Santa Clara, CA

Today

$175k - $309k/yr Full time Ai Engineer Ai Ml Artificial Intelligence

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Job Description

Apple’s Geo domain team is seeking a Senior Machine Learning Engineer to build machine learning and LLM-based systems that interpret user queries, rank documents, and surface useful local answers through Siri and Spotlight. The role focuses on improving search quality across the full pipeline, from query understanding to retrieval and ranking.

Role Overview

As a senior member of the AKI (Answers, Knowledge & Information) team, you will own end-to-end machine learning development to enhance how local information is understood and delivered. You will work on models and systems that support query understanding, document ranking, and overall search relevance.

Key Responsibilities

Own the entire ML development cycle, from opportunity analysis, exploration, and prototyping through data collection, feature engineering, training, evaluation, and production deployment.
Lead development of machine learning models to improve search quality across retrieval, ranking, reranking, and query understanding.
Enhance search quality and user experience by applying techniques such as learning-to-rank, embedding models, contrastive learning, multi-task learning, and reinforcement learning where appropriate.
Provide technical leadership by mentoring junior engineers and contributing to the architecture of ML systems and model design.
Translate product requirements into modeling tasks and engineering tasks.

Required Qualifications

8+ years of experience in information retrieval, natural language processing, machine learning, or deep learning.
Deep understanding of machine learning theory, including supervised learning, ranking models, embeddings, representation learning, and evaluation metrics.
Proven ability to apply advanced ML techniques to improve search relevance and retrieval quality at scale.
Comfort leading experimentation, offline evaluation, and online A/B testing to support iterative improvements in search quality.
Excellent interpersonal skills, with the ability to work independently and collaboratively.
Master’s Degree in Computer Science or equivalent experience in machine learning or a related field.

Preferred Qualifications

Advanced diploma in AI, Machine Learning, Computer Science, or Mathematics.

Technology Focus

LLMs

Location

Santa Clara, CA (onsite)

Compensation

Base pay range: USD 175,000 - 308,500 per year.

Benefits

Comprehensive medical and dental coverage
Retirement benefits
Discounted products and free services
Reimbursement for certain educational expenses, including tuition
Participation in Apple’s discretionary employee stock programs (eligible for discretionary restricted stock unit awards)
Ability to purchase Apple stock at a discount if voluntarily participating in the Employee Stock Purchase Plan
Discretionary bonuses or commission payments as well as relocation (might be eligible)

Education

Master’s Degree in Computer Science.

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Today

Senior Machine Learning Engineer

Xenon7 Columbus, OH

Columbus, OH

Today

Contract Backend Developer Senior APIs

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Job Description

Senior Machine Learning Engineer to design and run production ML systems for a life sciences client in a hybrid environment.

Responsibilities

Design, deploy, and maintain production-grade MLOps pipelines and infrastructure for continuous training, deployment, versioning, and monitoring.
Implement automated model drift detection, performance monitoring, and self-healing inference pipelines in high-reliability environments.
Operationalize production ML models and integrate them into operational technology (OT), API manufacturing workflows, and chemical process control systems.
Deploy predictive models for batch processing, process control optimization, real-time quality assurance, and facility automation use cases.
Build low-latency, high-throughput model serving microservices and architectures using FastAPI, Triton Inference Server, and TorchServe.
Containerize and orchestrate ML workloads across distributed cloud and edge systems with Kubernetes, Docker, and pipeline engines such as Kubeflow and MLflow.
Partner with chemical engineers, computational biologists, and software architects to turn operational friction into production-ready ML solutions.
Define enterprise MLOps standards, model governance, and CI/CD best practices across the full ML lifecycle.

Requirements

10–20+ years of senior-level experience across software engineering, MLOps, production ML deployment, and infrastructure scaling.
Proven ability to deliver production ML systems in highly specialized, non-standard domains, including transitions between process/chemical engineering ML and clinical or scientific research applications.
Must hold unrestricted US work authorization (no sponsorship available).
Ability to work 3 days per week onsite in the Indianapolis, IN area (regional/EST candidates may travel onsite).
Pragmatic problem-solving and strong collaboration; able to explain complex MLOps architecture to cross-functional engineering teams.
Advanced Python and C++ plus deep expertise with PyTorch, TensorFlow, or Scikit-learn.
Demonstrated expertise with Triton Inference Server, TorchServe, MLflow, Kubeflow, or Databricks ML runtime.
Hands-on experience with Kubernetes, Docker, CI/CD pipelines, FastAPI/gRPC, and AWS/Azure ecosystems.
Experience building real-time model monitoring, feature stores, drift detection, and integrations with enterprise data pipelines.
Deep exposure applying ML to either scientific/clinical domains (drug discovery, small or large molecule, computational biology) or chemical/process engineering environments (API manufacturing, batch processing, SCADA/MES integration, process optimization).

Required Technologies

Python, C++, PyTorch, TensorFlow, Scikit-learn
Triton Inference Server, TorchServe, MLflow, Kubeflow, Databricks ML runtime
Kubernetes, Docker, CI/CD pipelines
FastAPI, gRPC, AWS, Azure
Feature stores, SCADA, MES

Location & Contract Details

Location: Columbus, OH (hybrid)
Onsite requirement: 3 days per week onsite in the Indianapolis, IN area
Job type: Contract (Full-Time / enterprise project engagement)
Engagement route: Outsourced via Xenon7

Nice-to-Haves & Certifications

Academic background in Chemical Engineering, Bio-process Engineering, Computer Science, or related STEM discipline.
Experience operationalizing ML models in regulated GxP environments in life sciences or specialty chemicals.
AWS Certified Machine Learning – Specialty, Databricks Certified Machine Learning Professional, or equivalent MLOps credentials.

Role Focus

Not a Data Scientist or exploratory R&D role focused on Jupyter-style modeling or research algorithms.
Not a non-coding architecture position; emphasis is hands-on MLOps and engineering execution, including model deployment and infrastructure creation.
Not fully remote; hybrid onsite commitment is required.

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1d ago

AI Engineer 5 (MLX, Agentic AI, Gen AI platform Services)

Capital One San Jose, CA

San Jose, CA

1d ago

$251k - $286k/yr Full time Agentic Ai Artificial Intelligence Engineer

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Job Description

Build and support AI-powered products and foundational AI systems that improve how associates work and how customers interact with Capital One.

Responsibilities

Work with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
Design, develop, test, deploy, and support AI software components, including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
Use a broad stack of Open Source and SaaS AI technologies, including AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more.
Develop state-of-the-art foundation model optimization techniques to improve production performance across scalability, cost, latency, and throughput.
Shape the technical vision and long-term roadmap for foundational AI systems.
Design, implement, and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models into unified systems.
Establish and lead cost-performance governance reviews across AI systems, tracking GPU utilization, model throughput, and inference cost efficiency.
Lead team design councils or design review boards to ensure technical consistency and compliance with AI engineering standards.
Mentor Principal and Manager-level AI engineers to drive cross-domain learning and improve organizational technical maturity.

Requirements

Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies; or Master’s degree plus at least 4 years of experience developing AI and ML algorithms or technologies.
At least 6 years of programming experience with Python, Go, Scala, CUDA, or Java.

Technologies

AWS Ultraclusters, Huggingface, VectorDBs, PyTorch
AWS, Google Cloud, Azure
Python, Go, Scala, CUDA, Java, C++, C#, Golang
LLMs, vector search, GPU utilization
Rule-based, retrieval-augmented, generative components

Education

Master’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields

Location

San Jose, CA (onsite)

Salary

USD 250,800 - 286,200 per year

Benefits

Comprehensive, competitive, and inclusive health, financial, and other benefits to support total well-being
Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)

Team Description

The Intelligent Foundations and Experiences (IFX) team brings Capital One’s AI vision to life.
Collaborates across the company to advance state of the art in science and AI engineering.
Builds and deploys proprietary solutions central to the business and value delivered to millions of customers.
Enables teams across Capital One to enhance products with responsible and scalable AI.

Preferred Qualifications

Experience leading development AI systems with tradeoff decisions across cost, latency, throughput, and accuracy
7 years of experience deploying scalable and responsible AI solutions on cloud platforms (AWS, Google Cloud, Azure, or equivalent private cloud)
Experience designing, developing, delivering, and supporting complex AI systems
Experience developing and applying AI and ML algorithms, including LLM inference, similarity search and VectorDBs, guardrails, and memory using Python, C++, C#, Java, CUDA, or Golang
Experience optimizing training and inference software to improve hardware utilization, latency, throughput, and cost
Experience building agentic AI systems and agentic workflows
Passion for staying current with AI research and applying novel techniques in production
Excellent communication and presentation skills for explaining complex AI concepts to peers
Experience architecting and integrating heterogeneous AI systems, including rule-based, retrieval-augmented, and generative components, into unified production pipelines
Experience defining and enforcing standards for ethical AI deployment, including explainability, fairness, and human-in-the-loop review processes
Ability to balance model performance and operational cost using dynamic inference strategies and model compression
Experience right-sizing models, instance counts, and hardware types based on requirements such as context length and token inputs/outputs

Additional Notes

Salary ranges vary by location for AI Engineer 5, including McLean, VA ($229,900 - $262,400), Cambridge, MA ($229,900 - $262,400), New York, NY ($250,800 - $286,200), San Francisco, CA ($250,800 - $286,200), and San Jose, CA ($250,800 - $286,200).

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Machine Learning Inference Data Jobs

Machine Learning Engineer, Natural Language Understanding, Proactive

Sr Machine Learning Engineer - ML Data

Lead Machine Learning Engineer, Human Sensing

Sr. Machine Learning Engineer - Answers, Knowledge & Information (AKI)

Senior Machine Learning Engineer

AI Engineer 5 (MLX, Agentic AI, Gen AI platform Services)

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