Data & Ai

Data & Ai Engineer Jobs

118 jobs found

1d ago

Machine Learning Engineer, Computer Vision

Apple Cupertino, CA

Cupertino, CA

1d ago

$150k - $278k/yr Full time Artificial Intelligence Automation Computer Vision Model Deployment

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Job Description

On-device impact, real-world vision quality is at the core of this role. Apple is building and improving computer vision and vision-language models (VLMs) that support object understanding and recognition on Apple devices, with an emphasis on robustness across real data, meaningful evaluations, and efficient models designed for device constraints.

Location: Cupertino, CA (onsite)
Compensation: USD 150,400 - 277,600 per year

What you’ll do

Design, train, and improve computer vision and VLM models for object understanding and recognition.
Increase robustness to real-world conditions and address failure modes using results from real data, with performance across devices.
Define evaluations that reflect product quality, and use those evaluations to guide model decisions.
Develop models that meet on-device constraints including memory, latency, and power.

Core qualifications

Ph.D. or Master’s in Computer Science, Machine Learning, Computer Vision or a related field, or equivalent industry experience.
Strong foundations in computer vision and deep learning.
Proven ability to deliver end-to-end projects across data, training, and evaluation on real-world visual datasets.
Proficiency in Python and PyTorch (or an equivalent deep learning framework).
Proficiency in using generative coding tools.
Rigorous experimentation skills, including clear baselines, ablations, and meaningful evaluation sets.

Preferred qualifications

Publications in top computer vision or machine learning venues (CVPR, ICCV, ECCV, NeurIPS, ICLR), ideally with released code or work transferred to a product.
Experience in representation learning (such as self-supervised learning, metric learning, retrieval, or recognition).
Experience applying machine learning in an industry or product setting.
Experience with knowledge distillation or model compression.
Familiarity with vision-language models.

Benefits

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

Pay & equity details

At Apple, base pay is one part of the total compensation package and is determined within a range. The base pay range for this role is between $150,400 and $277,600, and base pay depends on skills, qualifications, experience, and location.

Apple employees may also participate in discretionary employee stock programs, including the opportunity to become an Apple shareholder. Employees may be eligible for discretionary restricted stock unit awards and can purchase Apple stock at a discount if they voluntarily participate in the Employee Stock Purchase Plan.

Learn more about Apple Benefits. Note: Apple benefit, compensation, and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Technologies: Python, PyTorch

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4d ago

AI and Image Processing Software Engineer

ASP Health Inc. Northbrook, IL

Northbrook, IL

4d ago

$70k - $90k/yr Full time APIs Applied Ai Artificial Intelligence

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Job Description

ASP Health Inc. is building and bringing to market novel medical devices, combining on-device workflows with AI and image processing to support clinicians with faster, more reliable slide assessment. Join an early-stage team where your software and model work directly contributes to devices used in clinical settings.

This role is based onsite in Northbrook, IL and focuses on developing and validating medical image processing models and production software, including cloud and edge inference pipelines.

Compensation: $70,000 to $90,000 per year. Schedule: flexible schedule with an adaptive work schedule.

What you will build

You will help develop AI-assisted assessment workflows for cytology and pathology microscope slide images. Responsibilities span model development, dataset and annotation workflow design, model validation, performance optimization for real-time inference, and integration into production software that connects inference outputs to clinician-facing visualization.

Responsibilities

Develop, optimize, test, and validate image processing models, algorithms, and software.
Create, maintain, and improve image processing systems, libraries, and frameworks.
Partner with cytopathologists to define annotation protocols and manage labeled image datasets, including annotation workflows and integration with CVAT, data quality auditing, cloud storage, and traceable dataset versioning.
Design model validation studies with appropriate data splits and clinically relevant metrics such as sensitivity and specificity for sample adequacy, ensuring consistent performance across sites, operators, stain batches, and devices.
Analyze and optimize models for real-time on-device inference, meeting defined latency and reliability targets using tooling such as ONNX and TensorRT and embedded GPU resources.
Develop and maintain cloud AI infrastructure on Google Cloud Platform (GCP), including data storage, compute resources, automated data pipelines, resource optimization, and model deployment workflows.
Develop and maintain production software that integrates AI models into ASP Health’s imaging platform, including image acquisition connections, inference pipelines, prediction metadata, image visualization, and clinician-facing interfaces.
Build and maintain GPU-accelerated training and inference workflows, including multi-GPU training, checkpoint/restart functionality, memory optimization, and performance benchmarking.
Create and troubleshoot production Python applications including backend services, APIs, and multiprocessing workflows.
Develop, maintain, and troubleshoot Windows-based software applications, including desktop user interfaces, packaging, installation, and release management.
Apply software engineering best practices such as automated testing, logging, error handling, debugging, version control, and reproducible builds to support reliability and maintainability.
Independently own solutions across the application stack and collaborate cross-functionally to ensure performance, reliability, and scalability.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
3+ years of experience in a related position.
Strong programming skills in Python and C++.
Experience with medical image processing, specifically microscopy and cytology/pathology (for example, whole-slide imaging or digital pathology systems).
Hands-on experience training and evaluating deep learning models for segmentation, detection, or classification using frameworks such as PyTorch or TensorFlow, including with limited labeled data.
Understanding of computer vision techniques.
Experience building and operating ML workflows on cloud infrastructure such as GCP (cloud storage, GPU compute, Python training pipelines, and resource monitoring such as Prometheus). AWS or Azure experience is acceptable.
Experience with software development lifecycle and methodologies.
Experience with version control for code, data, and models (for example Git, DVC, MLflow).
Knowledge of signal processing and mathematical modeling.
Experience with GPU programming and optimization.
Experience working in healthcare, medical devices, or biotech.
Knowledge of image preprocessing, compression, and enhancement techniques.
Experience deploying models on edge or embedded hardware (for example ONNX, TensorRT, NVIDIA Jetson).
Eagerness to learn, grow, and take on many challenges and opportunities across the company.

Benefits

Health insurance, dental insurance, vision insurance
Health savings account (HSA) and flexible spending account (FSA)
Life insurance, company-paid life insurance, and optional medical/dependent care FSA
Disability insurance
Retirement plan (401k)
Paid time off and paid holidays
Flexible schedule and adaptive work schedule

Overview of ASP Health

ASP Health is an early-stage commercial company developing and bringing to market novel medical devices. The first device supports operating room use by taking a biopsy sample, depositing it on a microscope slide, and drying, fixing, and staining the slide for immediate evaluation of sample adequacy. The second device is a specialized digital microscope with AI and image processing capabilities to aid in assessment of microscope slides.

Technologies you may work with

Python, C++, PyTorch, TensorFlow
Google Cloud Platform (GCP), CVAT
ONNX, TensorRT, embedded GPU, NVIDIA Jetson
Windows, multi-GPU training, multiprocessing workflows
Prometheus, AWS, Azure
Git, DVC, MLflow

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4d ago

AI Engineer

Ford Motor Company Dearborn, MI

Dearborn, MI

4d ago

Full time Ai Ml Artificial Intelligence Big Data Processing

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Job Description

Ford Motor Company is seeking an AI/ML Engineer to design, build, and deploy machine learning solutions that support customer experience, revenue, and operational efficiency.

Responsibilities

Design, develop, and implement AI/ML models and algorithms for complex problem solving
Handle data preprocessing, cleaning, and feature engineering to prepare datasets for model training
Train, evaluate, and tune multiple machine learning models
Build and maintain reliable, high-performance code in Python using libraries such as TensorFlow, PyTorch, and scikit-learn
Document code, experiments, and results with clear, concise write-ups
Collaborate with engineers across the team
Work in an agile environment alongside a product manager and other engineers
Support the full machine learning lifecycle, from preprocessing and feature engineering through deployment

Requirements

Bachelor’s Degree in a related field (examples: Data Science, Predictive Analytics, Statistics, Marketing Analytics, Applied Mathematics, IT)
3+ years of experience applying analytical methods appropriately
3+ years using AI/ML and data science software (Python-based tools)
MUST HAVE experience using Gen AI technologies
Experience using Cloud Platforms and AI Platforms

Preferred Requirements

Working knowledge of GCP
Expertise with open-source data science technologies: Python, R, Spark, SQL
Conceptual understanding of data pipelines and MLOps

Technologies

Python
TensorFlow
PyTorch
scikit-learn
GCP
R
Spark
SQL

Benefits

Immediate medical, dental, and prescription drug coverage
Flexible family care options, parental leave, new parent ramp-up programs, subsidized back-up child care, and more
Vehicle discount program for employees and family members, plus management leases
Tuition assistance
Established and active employee resource groups
Paid time off for individual and team community service
Generous schedule of paid holidays, including the week between Christmas and New Year’s Day
Paid time off and the option to purchase additional vacation time

Job Type / Work Type

Job Type: Full time
Work Type: Hybrid

Location

Dearborn, MI (hybrid)
On-site requirement: 4x a week
#LI-Onsite

Visa / Relocation

Visa Sponsorship provided for this specific role
Relocation assistance provided for this specific role

Equal Opportunity Employer

Committed to a culturally diverse workforce
All qualified applicants receive consideration without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status, or protected veteran status
Reasonable accommodation for online application process: call 1-888-336-0660

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4d ago

Machine Learning Engineer

Glidewell Dental Irvine, CA

Irvine, CA

4d ago

$113k - $115k/yr Full time Artificial Intelligence AWS Aws Sagemaker

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Job Description

In this onsite Machine Learning Engineer role in Irvine, CA, you will build and operationalize ML models and automated training and inference pipelines to improve business workflows and customer experiences.

Build, deploy, and optimize ML systems by working with the team on model training, deployment, monitoring, and continuous optimization
Develop automated ML pipelines for both model training and inference
Drive ML research to production across ML algorithms, feature engineering, analysis, modeling, and production deployment
Collaborate across ML engineering to help mature the use of machine learning within the organization
Learn the business context by building a deep understanding of products, services, data, and customers to support personalized experiences
Identify ML opportunities to improve business workflows and processes using machine learning technology
Support additional initiatives by performing other related duties and projects as directed by management

Requirements

Education: Master’s degree in Machine Learning, Deep Learning, or a computer science-related field
PhD: PhD preferred
Experience: Previous relevant working experience required
ML fundamentals: Understanding of fundamental concepts, practices, and procedures in the ML field
Data and SQL: Prior experience in data discovery, data aggregation, and feature engineering with strong SQL query writing skills preferred
Model lifecycle: Experience training, evaluating, optimizing, deploying, and maintaining ML models on production systems preferred
Evaluation and experimentation: Experience with logging, tracking, A/B testing, evaluation, and analysis of ML algorithm/model performance on production systems preferred
Python development: Strong development skills in Python and experience building data-driven, scalable, reliable applications; AWS preferred
Optimization and applied ML: Experience applying ML algorithms to optimization problems preferred, including examples such as customer sales prediction, recommendation engines, sentiment analysis, deep learning for image and natural language, customer segmentation/clustering, and object detection
ML libraries: Experience with open source ML/deep learning libraries preferred, including MxNet/Gluon, Tensorflow, scikit-learn, pandas, pyTorch, Keras, xgboost, LightGBM, Turi Create, and Nvidia Digits
Data platforms: Experience with relational, non-relational, and high-scale data processing and storage frameworks preferred, including SQL, AWS RedShift, Aurora, S3, DynamoDB, MySQL, and PostgreSQL
AWS services and serverless: Experience with AWS Serverless architecture and AWS native services preferred, including EC2, Lambda, Step Functions, SageMaker, DeepLens, Rekognition, IoT, GreenGrass, Comprehend, Lex/Polly, and Transcribe

Technologies

Python
SQL
AWS
MxNet/Gluon
Tensorflow
scikit-learn
pandas
pyTorch
Keras
xgboost
LightGBM
Turi Create
Nvidia Digits
AWS RedShift
Aurora
S3
DynamoDB
MySQL
PostgreSQL
EC2
Lambda
Step Functions
SageMaker
DeepLens
Rekognition
IoT
GreenGrass
Comprehend
Lex/Polly
Transcribe

Pay Range

$113,000 – $115,000.00 per year

Location: Irvine, CA (onsite)

Education: Master’s degree in Machine Learning, Deep Learning, or a computer science-related field

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4d ago

Machine Learning Engineer 5 (Senior Manager, IC)

Information Technology Senior Management Forum New York, NY

New York, NY

4d ago

$230k - $262k/yr Full time Backend Developer Manager Artificial Intelligence

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Job Description

Design, build, and scale production-grade machine learning with a role focused on training and serving platforms, model monitoring, and Responsible and Explainable AI. This onsite position in New York, NY pairs hands-on ML engineering with platform and infrastructure work, including CI/CD, data pipelines, and cloud architectures that support large-footprint model training and deployment.

What you’ll do

Design, build, and deliver ML models and components to solve real business problems in close collaboration with Product and Data Science.
Build and scale massive multi-tenant platforms for large footprint ML training and serving.
Drive ML infrastructure decisions using modeling expertise across model and data selection, feature selection, training, hyperparameter tuning, dimensionality, validation, and bias/variance tradeoffs.
Solve complex engineering problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
Collaborate in a cross-functional Agile environment to create and enhance software for big data and ML applications.
Retrain, maintain, and monitor models in production.
Leverage and/or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
Construct optimized data pipelines to feed ML models.
Apply continuous integration and continuous deployment practices, including test automation and monitoring, to support successful releases.
Ensure well-managed code to reduce vulnerabilities, support risk-governed model governance, and follow best practices in Responsible and Explainable AI.
Use programming languages such as Python, Scala, or Java.

Required qualifications

Bachelor’s Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering).
At least 6 years programming with Python, Java, Golang, or C++.
At least 6 years Machine Learning experience with industry standard frameworks PyTorch or TensorFlow and libraries Pandas, NumPy, Scikit-learn.
At least 6 years experience operating large-scale distributed systems such as Spark and Ray to prepare AI/ML data.
At least 4 years deploying and operating ML solutions in production and operating production services in the cloud (AWS, GCP, Azure), using Kubernetes to manage large-scale containerized ML software systems.

Preferred qualifications

Master’s or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
5+ years optimizing ML algorithms, configurations, and infrastructure.
5+ years following software development best practices including source control, testing, code reviews, and CI/CD.
5+ years building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response plan preparation.
5+ years working with ML techniques (Supervised, semi-supervised, unsupervised, reinforcement learning) across model types (Regression, Classification, Clustering), architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and evaluating model accuracy to diagnose underfitting and overfitting.
5+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models.
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents.
Ability to communicate complex technical and ML concepts clearly to a variety of audiences.

Technologies

Python, Scala, Java, Golang, C++
PyTorch, Tensorflow
Pandas, NumPy, Scikit-learn
Spark, Ray
AWS, GCP, Azure
Kubernetes

Salary: USD 229,900 - 262,400 per year. Experience: minimum 4 years. Location: New York, NY (onsite).

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4d ago

Machine Learning Engineer 5, Senior Manager - Intelligent Foundations and Experiences (IFX)

Information Technology Senior Management Forum McLean, VA

McLean, VA

4d ago

$230k - $262k/yr Full time Backend Developer Manager Ai Ml

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Job Description

This role is for a Senior Manager focused on building and scaling AI and machine learning capabilities, including model training and serving in production and multi-tenant ML platforms. You will work with Product and Data Science teams to deliver reliable data pipelines, CI/CD workflows, and Responsible and Explainable AI practices.

Location

McLean, VA (onsite)

Salary

McLean, VA: $229,900 - $262,400 per year (Machine Learning Engineer 5)
Richmond, VA: $209,000 - $238,500 per year (Machine Learning Engineer 5)

Role Summary

You will design and deliver ML models and platform components that address business needs, while using your depth in modeling and ML infrastructure decisions. The work includes training, deploying, and maintaining models in production, scaling large footprint training and serving, and ensuring governance and security best practices for risk and Responsible and Explainable AI.

Responsibilities

Design, build, and/or deliver ML models and components to solve real-world business problems in collaboration with Product and Data Science teams
Build and scale multi-tenant platforms enabling large footprint ML training and/or serving at scale
Use ML modeling fundamentals to guide infrastructure decisions, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
Collaborate in a cross-functional Agile team to create and enhance software for big data and ML applications
Retrain, maintain, and monitor models in production
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
Construct optimized data pipelines to feed ML models
Apply CI/CD best practices, including test automation and monitoring, to support successful model and application deployments
Ensure code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and ML follows Responsible and Explainable AI best practices
Use programming languages such as Python, Scala, or Java

Required Qualifications

Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
At least 6 years of experience programming with Python, Java, Golang, or C++
At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries Pandas, NumPy, Scikit-learn
At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data
At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning systems

Technologies

Python, Scala, Java, Golang, C++
PyTorch, Tensorflow, Pandas, NumPy, Scikit-learn
Spark, Ray
AWS, GCP, Azure
Kubernetes, CI/CD

Benefits

Eligible for performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits supporting total well-being

Preferred Qualifications

Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
+2 years experience building production RAG pipelines (chunking, embeddings, vector indexing, graph RAG, or fine-tuning)
+2 years experience leveraging Agentic patterns (Tool Calling, ReAct loops, Plan-and-Execute, Multi-Agent Collaboration)
+2 years with LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel) and model APIs (OpenAI, Anthropic, Bedrock, Ollama, Hugging Face)
+2 year experience in building agentic AI systems and agentic workflows
+2 year experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang
+2 years experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs)
5+ years of experience optimizing ML algorithms, configurations, and infrastructure
5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc.
5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans
5+ years of experience working with Machine Learning techniques and model types, including evaluation and diagnosing issues such as underfitting and overfitting
5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences

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5d ago

Data Engineer

Robotics Technologies Jersey City, NJ

Jersey City, NJ

5d ago

Full time Backend Developer Artificial Intelligence Data

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Job Description

Robotics Technologies is hiring a Data Engineer in Jersey City, NJ for an onsite role supporting retail-focused data and analytics initiatives. The work centers on building agentic AI-driven workflows that connect to enterprise systems, while also shaping data mesh and data fabric architectures for decentralized data management and access.

This position blends production engineering with AI agent development, including secure integrations, governance and observability controls, and the delivery of SQL and BI assets through automated, tool-enabled agents and human review gates. The role is listed for 10 Months + Extension and includes multiple employment type options under W-2, 1099, and C2C.

Responsibilities

Use GitHub Copilot as a hands-on engineering environment to analyze existing code, implement production-quality changes, generate and maintain tests and documentation, debug failures, and review proposed changes with executable evidence.
Design, build, test, and operate AI agents that plan and execute multi-step workflows using LLM reasoning, structured prompts, context and memory, retrieval, APIs, data tools, and human approval gates to improve data engineering, reporting, testing, and operational processes.
Develop tool-enabled agents and Model Context Protocol (MCP) integrations to securely connect AI assistants to enterprise APIs and platforms, including BI/reporting services, data products, work-management systems, and operational knowledge sources.
Implement browser and UI automation agents for authenticated enterprise workflows using Playwright and browser developer protocols, including evidence capture, regression testing, test generation and healing, and operational workflow support.
Define agent evaluation, observability, and safety controls such as grounded-response checks, deterministic tool contracts, structured outputs, least-privilege credential handling, approval checkpoints, test datasets, execution traces, failure recovery, and measurable quality and productivity outcomes.
Develop and implement data mesh and data fabric architectures to enable decentralized data management and access.
Build data and BI development agents that generate and validate SQL, DAX, semantic models, reports, data-product specifications, quality tests, and deployment artifacts while preserving governance, auditability, and human review.
Create user personas and business personas aligned with data requirements to deliver solutions that meet business needs.
Collaborate with business users to translate functional specifications into technical designs for implementation and deployment.
Work with cross-functional team members to develop prototypes, produce design artifacts, build components, support SIT and UAT testing, and triage and fix defects.
Apply problem-solving expertise and complex data analysis to develop business intelligence integration designs.
Ensure high quality and optimum performance of data systems to meet business expectations.

Requirements

2+ years of hands-on experience using GitHub Copilot or similar AI-assisted engineering tools across the software development lifecycle, including requirements analysis, coding, refactoring, testing, debugging, documentation, code review, and verification.
2+ years designing and building Agentic AI solutions using prompt and context engineering, LLMs, RAG, structured tool/function calling, APIs, memory or state management, human-in-the-loop controls, and AI governance principles.
Hands-on programming experience in Python, Java, TypeScripts, and SQL>, including the ability to design agent tools, API clients, MCP servers, command-line workflows, typed data contracts, and automated tests for enterprise use cases.
Experience integrating agents with enterprise authentication, APIs, databases, knowledge repositories, BI platforms, and browser automation while protecting credentials, sensitive data, and audit trails.
Ability to evaluate agent quality through unit, integration, behavioral, and end-to-end tests; diagnose hallucinations and tool failures; instrument execution; and improve prompts, retrieval, context, and workflows using evidence.