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1mo ago

Data Analyst Logistics

Purple Drive Technologies Columbus, OH

Columbus, OH

1mo ago Closed

$65k - $85k/yr Full time Analytics Business Analytics Business Intelligence

This position is no longer accepting applications (closed Oct 24, 2026).

Job Description

This role supports logistics, transportation, supply chain, and operations decision-making through analytics, reporting, and KPI dashboards.

Responsibilities

Create reports, dashboards, and KPI metrics to support business decisions.
Analyze logistics and operations data to identify trends and translate findings into actionable insights.

Requirements

Bachelor’s degree in Supply Chain, Logistics, Business Analytics, Data Science, Computer Science, Engineering, or a related field.
3–5 years of experience in data analytics, logistics, transportation, supply chain, or operations.
Advanced SQL and Power BI skills (noted as a plus in the requirements).
Strong Excel skills with experience analyzing large datasets.
Experience building reports, dashboards, and KPI metrics to drive business decisions.
Strong analytical, problem-solving, communication, and attention-to-detail skills.
Ability to analyze data, identify trends, and translate results into actionable business insights.

Preferred Qualifications

Experience with logistics, transportation, freight, or supply chain analytics.
Experience analyzing freight spend, carrier performance, transportation costs, service levels, capacity, or transit metrics.
Familiarity with Transportation Management Systems (TMS).
Experience with process mapping, continuous improvement, or operational excellence.
Experience working with domestic or international transportation data.
Experience collaborating with carriers, logistics providers, procurement, and operations teams.
Spanish language skills (plus).

Tools & Technologies

SQL
Power BI
Excel

Job Details

Job type: Full-time
Location: Columbus, OH (onsite)
Pay: $65,000.00 - $85,000.00 per year
Work location: On the road

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1mo ago

Software Machine Learning Engineer MTS1

The Charles Stark Draper Laboratory, Inc. Cambridge, MA

Cambridge, MA

1mo ago Closed

$63k - $140k/yr Full time Ai Ml Artificial Intelligence Data Analysis

This position is no longer accepting applications (closed Oct 24, 2026).

Job Description

Draper supports a balanced, learning-focused environment with workplace flexibility, employee clubs from photography to yoga, health and finance workshops, off-site social events, and discounts to local museums and cultural activities. In this onsite role in Cambridge, you will design and implement machine learning, artificial intelligence, and data science tools that support a variety of R&D projects, working closely with internal teams and customers.

What you’ll do

Design and develop AI models that align with project requirements
Analyze and improve AI model performance over time
Apply machine learning and deep learning to help systems visualize, learn, and respond to complex situations
Collaborate across departments and with customers to define requirements and understand business problems
Review task-level requirements and assumptions and help solve well-defined problems with guidance
Contribute to independent or team-based research with guidance, as needed
Ask for help when necessary and seek out resources to solve technical problems
Deliver on commitments and communicate challenges clearly
Build knowledge of customer missions and apply that understanding to task work
Work at the task and individual contributor level to improve operations, identify areas for improvement, and raise them to task or group leadership
Perform other duties as assigned

What you’ll bring

Excellent analytical, mathematical, and problem-solving skills
Strong ML/AI software development skills using Python, MATLAB, R, C++, or JAVA
Software best practices, including version control with git
Strong team player mindset
Excellent organizational skills and attention to detail
Excellent time management skills, including the ability to meet deadlines and prioritize tasks
Excellent verbal and written communication skills

Tools you may use

Python
MATLAB
R
C++
JAVA
git

Clearance requirement

Applicants selected for this position will be required to obtain and maintain a US Government Security Clearance.

Location: Cambridge, Massachusetts (onsite)
Compensation: USD 62,500 - 140,000 per year (US base salary range for this full-time position)

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1mo ago

Sr. Machine Learning Engineer, Pricing, Robotaxi

Tesla Palo Alto, CA

Palo Alto, CA

1mo ago Closed

$172k - $258k/yr Full time Artificial Intelligence Data Pipeline Data Processing

This position is no longer accepting applications (closed Oct 24, 2026).

Job Description

Join Tesla’s Robotaxi division to build real-time pricing that shapes how riders experience autonomy and how the fleet performs economically. In this role, you will use first-principles thinking to develop and productionize pricing algorithms that balance customer experience with operational revenue and costs.

Location: Palo Alto, CA (onsite)

Compensation: $172,000 - $258,000 per year + cash and stock awards + benefits

What you’ll do

Design, train, and productionize machine learning models for real-time Robotaxi pricing, including base fare, distance and time, demand, traffic, tolls, taxes, government fees, and location-specific surcharges
Build price-elasticity, conversion, and competitive-response models
Develop dynamic and contextual pricing with logical guardrails that protect rider trust and quote fairness
Productionize ML and optimization pipelines across feature engineering, training, low-latency inference, experimentation, monitoring, and rollback
Analyze ride sessions, costs, and competitor prices to continuously improve the pricing stack, and help global expansion with market-specific fees, airport, and event experiences
Partner with growth and profitability teams to help maintain economical scaling of the fleet

What you’ll bring

Degree in Computer Science, Machine Learning, Operations Research, Economics, or a related field, plus proof of exceptional skills in a related field, or equivalent experience
4+ years developing and deploying machine learning models and pricing or marketplace algorithms in large-scale production environments
Experience in rideshare, marketplace, or large-scale dynamic pricing (strongly preferred)
Proficiency in Python and at least one of Go, C++, Java, or Scala, with experience building backend distributed systems and low-latency, user-facing services
Experience with deep learning, causal machine learning, and/or reinforcement learning for pricing or real-time decisioning
Experience with large-scale data systems, deep learning, and ML/RL for pricing and real-time decisioning
Strong experimentation instincts, first-principles economics, and high standards for optimal outcomes
Ability to solve open-ended, difficult technical problems with little guidance, and collaborate effectively across product, finance, billing, and engineering

Benefits

As a full-time Tesla employee, you are eligible for benefits at day 1 of hire:

Medical plans with multiple options and $0 payroll deduction
Family-building, fertility, adoption, and surrogacy benefits
Dental (including orthodontic coverage) and vision plans with options and $0 paycheck contribution
Company-paid HSA contribution when enrolled in the High-Deductible medical plan with HSA
Healthcare and Dependent Care Flexible Spending Accounts (FSA)
401(k) with employer match, Employee Stock Purchase Plans, and other financial benefits
Company-paid Basic Life and AD&D
Short-term and long-term disability insurance (90 day waiting period)
Employee Assistance Program
Sick and Vacation time (Flex time for salary positions, accrued hours for Hourly positions) and paid holidays
Back-up childcare and parenting support resources
Voluntary benefits including critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance
Weight loss and tobacco cessation programs
Tesla Babies program
Commuter benefits
Employee discounts and perks program

Compensation note: Pay offered may vary depending on individualized factors including market location, job-related knowledge, skills, and experience. The total compensation package may also include other elements depending on the position offered.

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1mo ago

Machine Learning Engineer, Advertising & Marketing Performance Intelligence

Amazon.com Services LLC Seattle, WA

Seattle, WA

1mo ago Closed

$144k - $194k/yr Full time Marketing Agentic Ai Ai Agent

This position is no longer accepting applications (closed Oct 24, 2026).

Job Description

On Amazon’s Advertising & Marketing Performance Intelligence (AMPI) team, you will design, deploy, and scale production-grade machine learning and GenAI solutions that support automated decision-making and personalized marketing communications. This onsite role in Seattle pairs ML engineering with infrastructure work, spanning large-scale training systems, monitoring, and cost-aware AI/ML operations.

What you’ll do

Work with Data and Applied Scientists to process structured and unstructured data, scaling ML and LLM infrastructure while optimizing infrastructure costs, GPU utilization, memory management, and production training workflows (including techniques such as optimizer state offloading and massive parallelization).
Build and deliver reusable technical assets that enable faster adoption of ML, optimization, and GenAI across different science initiatives.
Design and maintain production-grade, large-scale distributed training systems for ML, Causal, GenAI, and multi-modal foundation model workloads.
Optimize AWS AI/ML infrastructure for training efficiency, including GPU utilization, latency, costs, and fine-tuning on massive datasets.
Create monitoring and debugging tooling to improve reliability and performance of training workflows, including support for piloting LLMs and diagnosing system issues.
Partner with Engineers, Data, and Applied Scientists to evaluate design approaches, prototype new GenAI and ML models, assess technical feasibility, and resolve complex problems.

Basic qualifications

3+ years of non-internship professional software development experience.
2+ years of non-internship design or architecture experience for new and existing systems, including design patterns, reliability, and scaling.
Experience programming with at least one software programming language.
Experience in machine learning, data mining, information retrieval, statistics, or natural language processing.

Technologies you may work with

Machine Learning, large language models (LLMs), and large quantitative models (LQMs)
AI/ML workflows
AWS Bedrock, SageMaker
Agentic AI (RAG, agentic architectures, vector databases), Amazon Q
Containerized deployments, Hugging Face, LangChain
Guardrail implementations
Foundational models such as Qwen, Anthropic’s Claude, Mistral
RCTs

Compensation and location

Location: Seattle, WA (onsite)
Salary: USD 143,700 - 194,400 per year

Benefits

Sign-on payments
Restricted stock units (RSUs)
Health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance, with option for Supplemental life plans), EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage
401(k) matching
Paid time off
Parental leave

Preferred qualifications

3+ years of full software development life cycle experience, including coding standards, code reviews, source control management, build processes, testing, and operations.
Bachelor’s degree in computer science or equivalent.
1+ year building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization, or search.

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1mo ago

Machine Learning Engineer - R126

Expedition Technology Herndon, VA

Herndon, VA

1mo ago Closed

Full time Artificial Intelligence Cloud Operations Data & Ai

This position is no longer accepting applications (closed Oct 24, 2026).

Job Description

Expedition Technology is seeking an experienced Deep Learning Software Engineer to design and deliver end-to-end deployable computer vision systems for novel applications. The role focuses on building deep learning architectures and pipelines, implementing with Python and PyTorch, and providing tested solutions running in an AWS environment.

Location

Herndon, VA (onsite)

Education and Experience

Minimum experience: 2 years
Education: MS or BS
Equivalent qualifications: MS with 2+ years, or BS with 4+ years in a technical field such as Computer Science, Electrical Engineering, Physics, Statistics, or Applied Mathematics

Responsibilities

Collaborate with a team to develop deep learning architectures and pipelines for novel applications
Implement solutions in Python using PyTorch
Deliver high-quality software with careful testing
Participate in team brainstorming sessions to shape innovative solutions
Research academic literature to stay current on theories, techniques, and tools

Requirements

Security clearance: an active Secret clearance (TS/SCI preferred)
Citizenship: US Citizenship and must be eligible to maintain a US-Government issued security clearance of Top Secret or higher
Proven proficiency in PyTorch, TensorFlow, or other modern machine learning frameworks
Strong theoretical understanding of deep neural networks and their applications, particularly in computer vision
Proven experience architecting, training, testing, and deploying machine learning solutions within an AWS environment
Experience using collaborative source code management tools, including Git
Strong technical presentation and writing skills

Technologies

Python
PyTorch
TensorFlow
Git
AWS

Benefits

Company-paid medical, dental, and vision insurance
11 paid holidays and up to 34 days of PTO
401(k) Plan with up to 12% company contribution (including a 3% safe harbor, 6% match, and up to 3% additional profit sharing)
Student Loan Repayment (reallocate a portion of 401(k) match funds to repay student loans)
Paid Parental Leave (six weeks for the primary caregiver and two weeks for the secondary caregiver)
Tuition Reimbursement up to $5,250/year
Referral Bonus Program
Exclusive Entertainment Perks (free tickets to sporting events, theater, concerts, and more)
Onsite amenities including a free onsite fitness center, treadmill and bike desks, and an onsite cafeteria with reduced-cost options
Inspiring work culture

Security Clearance Required

Top Secret/SCI

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1mo ago

Machine Learning Engineer II - Operations

Milwaukee Electric Tool Corporation Milwaukee, WI

Milwaukee, WI

1mo ago Closed

Full time Artificial Intelligence Data Pipeline Data Platform

This position is no longer accepting applications (closed Oct 24, 2026).

Job Description

Milwaukee Electric Tool Corporation is looking for a Machine Learning Engineer II to help design, build, and deploy machine learning solutions that improve how Milwaukee Tool manufactures and services products. This onsite role in Milwaukee supports end-to-end work across the machine learning lifecycle, pairing data engineering with model development, deployment, and ongoing monitoring.

You will collaborate with operations-related teams worldwide to deliver data-driven solutions in real operational environments, using Azure and Databricks as core platforms.

What you’ll do

Design, develop, and deploy machine learning solutions that improve manufacturing and service workflows.
Partner cross-functionally with operations, quality, supply chain, engineering, and service teams to address real business and operational challenges on a global scale.
Deliver full lifecycle machine learning work, from data engineering and model development through deployment and monitoring on Azure and Databricks.
Support Global and Service Teams by deploying, validating, and maintaining machine learning solutions in operational environments, ensuring models create measurable value where used.
Develop and implement data-driven solutions in operational environments globally.

Minimum qualifications

Bachelor of Science degree in Computer Science, Computer Engineering, Electrical Engineering, or another scientific or engineering discipline.
Completed coursework or specialization in Machine Learning and/or Data Science using deep learning frameworks such as PyTorch, TensorFlow, or Keras.
At least one year of hands-on experience applying machine learning principles and algorithms to dynamic, real-world problems.
Demonstrated experience applying fundamental machine learning algorithms and techniques outside coursework, including areas such as unsupervised or supervised learning, classification/regression, dimensionality reduction, and model optimization.
Demonstrated experience with machine learning and AI methods such as CNNs, transformers, or computer vision.
Proficiency in big data transformation using Spark, SQL, and Python (NumPy, pandas, scikit-learn, Matplotlib).
Strong mathematical foundation in statistics, linear algebra, calculus, and optimization.
Experience with ML deployments using CI/CD pipelines (Azure, Databricks, MLFlow) and edge environments (GPU, containerization, Linux).
Excellent problem-solving and technical communication skills to explain complex ML deployments to non-technical audiences.
Experience collaborating with global teams, including willingness to adjust working hours to match international time zones and align on delivery.

Technologies you’ll work with

PyTorch, TensorFlow, Keras
Spark, SQL, Python, NumPy, pandas, scikit-learn, Matplotlib
Azure, Databricks, MLFlow
CI/CD, GPU, containerization, Linux
CNNs, transformers

Preferred

Master’s degree or PhD in Machine Learning or a related field.
At least three years of hands-on experience applying machine learning principles and algorithms to dynamic, real-world problems.
Experience with time-series modeling for use cases such as demand forecasting, predictive maintenance, yield prediction, or process anomaly detection.
Experience with computer vision for use cases such as defect detection, missing part detection, part quality inspection, part counting, and related applications.
Proven track record of developing, deploying, and scaling AI or ML solutions tied to measurable operations outcomes (examples include scrap reduction, throughput, OEE, on-time delivery, or inventory turns).
Desktop or web application development experience (for building tools or UIs that help plant and operations users use models).
Hands-on data engineering experience building pipelines on Databricks/Spark against large operational datasets such as MES, ERP, SCADA, or IoT/sensor telemetry.
Experience applying generative AI or LLMs to operations problems such as knowledge retrieval, document processing, or assistive tooling for plant teams.
Experience developing, maintaining, and using MLOps pipelines to support efficient deployment, monitoring, and scaling.
Experience developing and deploying machine learning algorithms to edge environments.

Benefits

Robust health, dental, and vision insurance plans.
Generous 401(K) savings plan.
Education assistance.
On-site wellness, fitness center, food, and coffee service.

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1mo ago

Staff Machine Learning Engineer

Adobe San Jose, CA

San Jose, CA

1mo ago Closed

$173k - $307k/yr Full time Agentic Ai Ai Agent Ai Ml

This position is no longer accepting applications (closed Oct 24, 2026).

Job Description

Adobe’s Brand AI Services team is hiring a Staff Machine Learning Engineer to build production-grade multimodal and agentic generative AI for Firefly AI Assistant and Creative Cloud workflows.

Responsibilities

Lead end-to-end design, development, and deployment of multimodal and generative AI systems across vision, language, and other modalities
Build and productionize generative AI models and systems, including transformers, diffusion models, LLMs, and vision-language models (VLMs) for content creation, understanding, and transformation
Develop agentic AI systems capable of reasoning, tool use, model and service interaction, and completing complex multi-step creative workflows
Create intelligent capabilities for Firefly AI Assistant and Creative Cloud workflows to support creative professionals from intent and ideas to high-quality outcomes
Develop scalable services and APIs that embed AI and machine learning capabilities into Adobe products
Own the ML lifecycle: problem formulation, modeling, experimentation, evaluation, deployment, monitoring, and iteration
Partner with engineering, product, design, and research teams to translate customer needs into effective ML solutions
Improve performance, scalability, reliability, and quality of AI systems in high-traffic production environments
Provide technical leadership, mentor engineers, and help raise the engineering and machine learning bar across the team
Identify opportunities to apply generative and agentic AI to real-world challenges for creative professionals and enterprise customers

Requirements

MS or PhD in Computer Science, Machine Learning, or a related field, or equivalent practical experience
5+ years of experience building and deploying machine learning systems in production
Hands-on experience designing and building agentic AI systems, including tool use, agent orchestration, multi-step workflows, planning and reasoning, retrieval, memory, or human-in-the-loop systems
Experience with agent interoperability and tool integration, including Model Context Protocol (MCP), function/tool calling, or similar frameworks and protocols
Expertise in computer vision, generative AI, and/or multimodal machine learning, with hands-on experience using transformers, diffusion models, LLMs, or VLMs
Strong foundation in probability, statistics, machine learning, and model evaluation
Proficiency in Python and experience with machine learning frameworks such as PyTorch
Experience designing and building scalable APIs, distributed services, or production ML infrastructure
Strong software engineering fundamentals: data structures, algorithms, testing, code quality, and code reviews
Experience with cloud platforms such as AWS or Azure plus containerization and orchestration technologies such as Docker and Kubernetes
Familiarity with modern AI-assisted development tools and workflows, including ChatGPT, Claude, Cursor, or similar tools, for development, experimentation, or productivity

Technologies

Python, PyTorch
Transformers, diffusion models, LLMs, VLMs
Model Context Protocol (MCP), function/tool calling
AWS, Azure
Docker, Kubernetes
ChatGPT, Claude, Cursor

Nice to Have

Experience building production agentic AI platforms or multi-agent systems, including agent evaluation, observability, reliability, or safety
Experience with multimodal learning across video, audio, or 3D data
Background in video understanding or generation, temporal modeling, or streaming ML systems
Experience fine-tuning, adapting, or optimizing large-scale foundation models
Knowledge of AI evaluation, safety, and responsible AI practices
Experience with agent frameworks, orchestration platforms, retrieval systems, or enterprise knowledge integration
Experience building AI-powered tools or workflows for creative professionals, content creation, or creative applications
Contributions to research, open-source projects, or applied machine learning innovation

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The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation