Model Optimization

Model Optimization Data Jobs

4 jobs found

4d ago

Lead Machine Learning Engineer, Human Sensing

Apple Seattle, WA

Seattle, WA

4d ago

$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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1mo ago

Sr. Data Scientist - Network Modeling & Optimization

Best Buy Minneapolis, MN

Minneapolis, MN

1mo ago Closed

$106k - $190k/yr Full time Senior Data Analysis Data Analytics

This position is no longer accepting applications (closed Sep 25, 2026).

Job Description

Best Buy is hiring a senior data science contributor to lead network modeling and optimization work that supports supply chain planning and one-off decision needs. This hybrid role in Minneapolis, MN combines hands-on model development with scenario evaluation, validation, and clear stakeholder communication.

The Sr. Data Scientist focuses on updating, running, and improving existing supply chain network models, and also contributes to readiness for production while helping develop new modeling approaches when required.

What you’ll do

Develop and maintain analytical inputs and optimization models covering facilities, transportation lanes, demand, product flows, costs, capacities, service requirements, and business rules.
Assess strategic and tactical scenarios across facility location, network assignments, capacity, sourcing, inventory placement, transportation, and product flow.
Validate model behavior, diagnose infeasibilities and data-quality issues, and communicate assumptions, limitations, tradeoffs, and recommendations to business and technical stakeholders.
Turn ambiguous supply chain questions into structured analytical problems by defining objectives, decisions, constraints, assumptions, data requirements, and expected outputs.
Translate complex physical processes into simplified functions that preserve key real-world characteristics while remaining usable as model inputs.
Support end-to-end network modeling and optimization efforts from business-problem definition through model development, validation, interpretation, and recommendation.
Provide technical guidance and mentorship to Data Scientists and analysts, establishing reusable modeling practices, documentation standards, and quality controls.

Qualifications

Bachelor’s degree in a quantitative field (Operations Research, Industrial Engineering, Mathematics, Statistics, Computer Science, Economics, Engineering, Supply Chain Analytics, or related) or equivalent experience.
4+ years of relevant experience in data science, operations research, optimization, supply chain analytics, forecasting, simulation, or a related analytical field.
4+ years of leading complex analytical projects and translating ambiguous business questions into structured modeling approaches.
4+ years hands-on experience with data analytics tools (e.g., SQL, Python, R) and machine learning and/or optimization tools (Python, R, Gurobi, Vertex AI).
Strong knowledge of optimization, operations research, statistical modeling, forecasting, simulation, machine learning, or another advanced analytical discipline.
Ability to explain technical concepts, model results, and business recommendations clearly to both technical and non-technical audiences.

Tools and technologies

SQL, Python, R, Gurobi, Vertex AI, AIMMS

Preferred qualifications

Advanced degree in a quantitative field (Operations Research, Industrial Engineering, Mathematics, Statistics, Computer Science, Supply Chain Analytics, or related).
Experience with supply chain network design, transportation, distribution, fulfillment, inventory, logistics, or capacity planning.
Experience developing optimization models using linear programming, mixed-integer programming, network flow, facility location, or assignment modeling.
Experience defining model inputs, decision variables, constraints, objective functions, scenarios, and outputs, including diagnosing model infeasibilities.
Familiarity with AIMMS or comparable optimization platforms, libraries, or solvers (direct AIMMS experience is preferred but not required).
Experience providing technical leadership, mentoring Data Scientists or analysts, and communicating recommendations to business leaders.

Compensation and logistics

Location: Minneapolis, MN (hybrid)
Salary: USD 106,200 - 190,300 per year
Position type: Full time
Application deadline: Minimum of 5 days from the posting date

Benefits

Competitive pay
Great employee discount
Financial savings and retirement resources
Support for physical and mental well-being
Paid time off (vacation or PTO) for full-time and part-time employees based on work location, employment status, salary or hourly status (exempt/non-exempt), and years of continued or bridged service
Leaves of absence (LOA) and potential pay sources based on eligibility (length depends on situation, where you live, full-time/part-time status, and federal and state regulations)
Intermittent or reduced-schedule leave for certain medical or family care leaves
Certain roles may be eligible for incentive pay to drive performance and offer recognition

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1mo ago

Machine Learning Engineer - College Graduate

Qualcomm San Diego, CA

San Diego, CA

1mo ago Closed

$123k - $184k/yr Full time Android Artificial Intelligence Data & Ai

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

Job Description

Qualcomm’s AI Research team in San Diego is looking for a college graduate to help build and deploy on-device GenAI prototypes for the edge.

Responsibilities

Collaborate in a dynamic research setting as part of a multi-disciplinary team of researchers and software engineers
Work with cutting-edge AI frameworks and tools
Architect, design, develop, test, and deploy on-device prototype software for advanced AI use cases

Requirements

Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field
Strong skills in Python and object-oriented software design and implementation
Knowledge of deep learning and ML frameworks (example: PyTorch)
Strong software design abilities with a focus on usability
Strong analytical, debugging, and problem-solving skills

Technologies

Python
Object-oriented software design
PyTorch
Generative AI (GenAI)
Neural network model optimization
Model quantization
On-target deployment
NPUs / ML accelerators
Android programming
C/C++
Embedded software
Hardware acceleration
Edge inference
Algebraic operations optimization for HW cores

Preferred extra experience and skills

Knowledge of Generative AI (GenAI) technologies
Knowledge of neural network model optimization and on-target deployment (plus)
Knowledge of NPUs / ML accelerators
Knowledge of Android programming (plus)
Optimization of algebraic operations in algorithms for HW cores (plus)
Knowledge of C/C++ and embedded software

Location and salary

Location: San Diego, CA (onsite)
Salary: USD 122,800 - 184,200 per year

Benefits

Competitive annual discretionary bonus program
Opportunity for annual RSU grants
Highly competitive benefits package

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1mo ago

Principal Machine Learning Engineer

On behalf of Next Deavor New York, NY

New York, NY

1mo ago Closed

$200k - $250k/yr Full time Ai Ml Artificial Intelligence Data Pipeline

This position is no longer accepting applications (closed Sep 13, 2026).

Job Description

Hybrid in the New York City Metro area (3 days onsite). In this Principal Machine Learning Engineering role, you will own the ML infrastructure behind real-time compliance enforcement systems. Expect a position centered on building the pipelines, evaluation workflow, and production serving needed to train, measure, and deploy models reliably, with a focus on latency, reliability, and cost.

Pay range: USD $200,000 - $250,000 per year.

What you’ll do

Build and own training pipelines including data preparation, reproducible fine-tuning runs, experiment tracking, and release automation.
Develop evaluation infrastructure with automated eval runs, regression gates, dashboards, and dataset versioning.
Own production model serving for low-latency inference, including batching, optimization, autoscaling, and cost management.
Ship model updates safely using versioning, canarying, rollback, and drift monitoring.
Create repeatable workflows to adapt models to new domains and changing customer needs.
Convert expert labels and reviewer feedback into clean training and evaluation datasets.
Help raise the team’s engineering bar for ML infrastructure as the organization grows.

What you’ll bring

8+ years of software engineering experience, including 4+ years building ML or LLM infrastructure for production.
Hands-on experience with the modern LLM stack: PyTorch, distributed training, and fine-tuning at scale (e.g., LoRA, SFT) using inference engines such as vLLM or TensorRT-LLM.
Experience building eval harnesses, regression gates, or dataset pipelines, with strong understanding of precision, recall, and calibration.
Proven ownership of production model serving with real latency, reliability, and cost constraints.
Strong fundamentals in Python, containers, CI/CD, cloud infrastructure, and observability.
Ability to scope work, ship frequently, and make pragmatic build-vs-buy decisions.
Experience collaborating tightly with research partners and defining clear interfaces.

Technologies you’ll work with

PyTorch, LoRA, SFT, vLLM, TensorRT-LLM
Python, containers, CI/CD, cloud infrastructure, observability

Additional qualifications that may help

Experience productionizing small or specialized language models.
Experience with structured-output serving or constrained decoding in production.
Prior work in regulated or high-stakes domains (fintech, healthcare, legal, trust and safety).
Experience deploying models into customer-controlled environments.

Work location: Hybrid remote in New York, NY 10001 (3 days onsite).

This role may fill quickly. Submit your resume to be considered.

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Model Optimization Data Jobs

Lead Machine Learning Engineer, Human Sensing

Sr. Data Scientist - Network Modeling & Optimization

Machine Learning Engineer - College Graduate

Principal Machine Learning Engineer

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