Ml Pipelines

Ml Pipelines Data Jobs

11 jobs found

Today

Senior Data Scientist, Cloud Gaming - Prescriptive Analytics and Optimization

NVIDIA Santa Clara, CA

Santa Clara, CA

Today

$184k - $288k/yr Full time Senior Analytics Big Data

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Job Description

This senior data scientist role focuses on prescriptive analytics and optimization for real-time cloud gaming, delivering scalable ML/AI and optimization solutions for routing, scheduling, and capacity management at NVIDIA.

Location

Santa Clara, CA (onsite)

Compensation

USD 184,000 - 287,500 per year

Experience and Education

Minimum 6 years of experience with a BS/MS or equivalent experience, or a PhD in Data Science, Computer Science, Operations Research, Statistics, Applied Mathematics, or a related quantitative field.

Responsibilities

Develop and deploy scalable ML, AI, and optimization models to improve demand forecasting, optimize capacity allocation, and implement user-specific feature engineering for real-time cloud gaming services.
Create reusable framework deployments for data ingestion, processing, and analysis to support dynamic user interventions toward targeted business outcomes.
Apply domain knowledge of the product and software stack to identify data inconsistencies and enhance model performance, especially in the context of optimization results.
Analyze complex datasets to identify trends and patterns using supervised and unsupervised learning to inform prescriptive solutions.
Design and enhance real-time prescriptive scheduling pipelines using linear programming and constraint optimization to improve capacity utilization and user retention.
Increase organizational productivity by mining petabytes of data for actionable insights, often delivered as prescriptive recommendations.
Collaborate with a diverse set of partners to gather requirements, design robust solutions, and guide the team toward impactful results.
Leverage agentic AI to deliver advanced automation and programming solutions for complex analytical problems.

Requirements

BS/MS (or equivalent experience) with 6+ years of experience or PhD in Data Science, Computer Science, Operations Research, Statistics, Applied Mathematics, or related quantitative fields, with a focus on prescriptive analytics and optimization.
Strong foundation in probability, statistics, AI/ML, prescriptive modeling, and optimization methodologies such as linear programming, network flow, decision theory, and multi-armed bandit.
Proficient coding skills, primarily Python, with the ability to write readable, testable, maintainable, and extensible code; experience with optimization libraries such as Google OR-Tools.
Experience with data storage and processing tools and the ability to address issues in running large-scale software across substantial clusters.
Solid experience in data cleaning, aggregation, transformation, and extraction, with an understanding of how data quality affects performance.

Technologies

Python
SQL
Delta Lake
Apache Spark
Databricks
MLflow
Grafana
Elasticsearch
Google OR-Tools
Kubeflow

Benefits

Equity
Benefits

What you will be doing

Develop and deploy scalable ML, AI, and optimization models to improve demand forecasting, optimize capacity allocation, and craft user-specific features for real-time cloud gaming services.
Build reusable data ingestion, processing, and analysis frameworks to support dynamic user interventions for targeted business outcomes.
Apply product and software stack knowledge to resolve data inconsistencies and boost model performance within optimization contexts.
Identify and interpret patterns in complex data using supervised and unsupervised methods to inform prescriptive solutions.
Enhance real-time prescriptive scheduling pipelines through linear programming and constraint optimization to maximize capacity utilization and retention.
Drive organizational productivity by mining vast data stores for actionable insights and prescriptive guidance.
Collaborate with partners to define requirements, craft robust solutions, and lead the team toward meaningful results.
Utilize agentic AI to deliver automation and programming solutions for challenging analytical problems.

What we need to see

BS/MS (or equivalent experience) with 6+ years of experience or PhD in Data Science, Computer Science, Operations Research, Statistics, Applied Mathematics, or related quantitative fields, with a strong emphasis on prescriptive analytics and optimization.
Solid background in probability, statistics, AI/ML, prescriptive modeling, and optimization methods (eg, linear programming, network flow, decision theory, multi-armed bandit).
Strong coding ability in Python with experience in optimization libraries such as Google OR-Tools; ability to write clean, maintainable code.
Experience with data storage and processing tools and handling large-scale distributed software across clusters.
Proficiency in data cleaning, aggregation, transformation, and extraction, with awareness of data quality impacts on performance.

Ways to stand out from the crowd

Strong collaboration and presentation skills for working with multiple partners, with the ability to explain complex analytical solutions and their business implications.
Experience in time series analysis and forecasting for demand prediction in optimization contexts is advantageous.
Experience operating active ML production pipelines (MLflow, Kubeflow) with a focus on deploying and monitoring optimization models is advantageous.

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Today

Senior AI Engineer

Deloitte Cincinnati, OH

Cincinnati, OH

Today

$128k - $253k/yr Full time Senior Apache Airflow Artificial Intelligence

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Job Description

Join Deloitte's SFL Scientific practice as a Senior AI Engineer, where you’ll help design, build, and deploy scalable AI and GenAI infrastructure and data architectures for diverse client engagements. This onsite role is based in Cincinnati, OH, and involves close collaboration with data scientists, machine learning engineers, and industry experts to translate complex data into actionable AI capabilities. The compensation range is USD 128,000 to 252,500 per year.

Responsibilities

Collaborate with clients to design, develop, and deploy architectures that support machine learning and automation applications
Apply advanced skills in modern data architecture, data science engineering, data transformation, and management of structured and unstructured data using cloud or on‑premises technologies
Lead the development of scalable, high‑performance data architectures that enable both business goals and AI/GenAI use cases
Support and enhance data architectures and pipelines, and define database schemas (Graph, SQL, NoSQL) to enable algorithm scalability and deployment aligned with agile priorities
Participate in architectural and deployment discussions to ensure solutions are scalable, secure, and highly available in cloud or on‑prem environments
Adopt engineering best practices in automation, high‑performance computing, and AI/GenAI infrastructure and design patterns
Define and lead technology proof of concepts to validate the feasibility of new data and cloud technology solutions
Demonstrate thought leadership and execution in modern data architecture principles and technology modernization
Mentor and coach junior team members on technical best practices and professional development

Requirements

Bachelor’s degree in a STEM field or equivalent experience
4+ years in data engineering, data science, software engineering, or MLOps with emphasis on AI and ML deployment
4+ years designing cloud solutions and supporting production projects, with hands‑on experience in AWS (or Azure/GCP equivalents)
4+ years programming with Linux Shell/CLI, Python, SQL, and PowerShell
2+ years leading technical delivery teams on complex projects
2+ years of DevOps and CI/CD experience using tools such as Puppet, Ansible, Chef, Airflow, Terraform, and Jenkins
2+ years of database development and ETL/ELT pipelines across relational, NoSQL, and graph databases (Neo4j)
2+ years deploying and optimizing systems with Kubernetes, Docker, NVIDIA TensorRT/Triton, RAPIDs, Kubeflow, MLflow, and Kafka
Residence within commuting distance to a Deloitte consulting office
Ability to travel about 10% on average based on client assignments
Limited immigration sponsorship may be available

Technologies

Python, SQL, Linux Shell/CLI, PowerShell
Puppet, Ansible, Chef, Airflow, Terraform, Jenkins
Kubernetes, Docker, NVIDIA TensorRT, Triton, RAPIDs, Kubeflow, MLflow, Kafka
Graph databases and Neo4j, NoSQL, relational databases
AWS, Azure, GCP
CUDA and OpenCL

The Team

Our Strategy offering architects bold strategies to achieve business and mission goals, enabling growth, competitive advantage, technology modernization, and continuous digital and AI transformation. Specifically, SFL Scientific, a Deloitte Business, is a data science professional services practice focused on strategy, technology, and solving business challenges with Artificial Intelligence (AI).

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1d ago

Senior Machine Learning Engineer

bp Houston, TX

Houston, TX

1d ago

Full time Senior Ai Ml Big Data

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Job Description

bp offers a path to tackle ambitious ML and AI challenges at global scale, backed by a culture that prioritizes scientific rigor, engineering excellence, and continuous learning. This hybrid role in Houston invites an accomplished machine learning engineer to design, build, and deploy production-grade systems across NLP, optimization, simulation, and generative AI, collaborating with cross-disciplinary teams to deliver deployable ML products that drive real impact.

Benefits

Competitive compensation and a comprehensive benefits package.
Opportunity to work on advanced ML and AI problems at global scale.
A culture that emphasizes scientific rigor, engineering excellence, and lifelong learning.
Hybrid work arrangements that support work-life balance.
Career development pathways within a world-class technology organization.

Responsibilities

Design, build, and maintain scalable, production-grade ML systems and pipelines with modern engineering practices such as CI/CD, testing, monitoring, and observability.
Translate ML science into reliable, scalable products that progress from experimentation through production delivery and operational use.
Develop impactful ML products using statistical modeling, deep learning, and AI techniques across operational, scientific, and R&D domains.
Turn complex scientific and business problems into well-scoped ML solutions that yield actionable insights and deployable capabilities.
Architect and optimize ML systems for performance, scalability, and reliability in production environments.
Collaborate closely with data scientists, data engineers, software engineers, and domain experts within cross-disciplinary teams.
Advocate for engineering and data science guidelines, including design reviews, unit testing, monitoring and alerting, code reviews, and documentation.
Present technical results, trade-offs, and product outcomes to peers and senior stakeholders.
Contribute to improving developer velocity, engineering standards, and shared tooling.
Mentor junior team members and contribute to the technical growth of the wider team.

Requirements

MSc or PhD in a quantitative field (e.g., Computer Science, Mathematics, Physics, Engineering) or equivalent experience.
Typically 5+ years of hands-on experience designing, prototyping, productionizing, maintaining, and scaling ML or data science products in sophisticated environments.
Strong expertise in ML algorithms, statistical modeling, and optimization with a track record of delivering production-grade solutions.
Applied knowledge of the data science lifecycle and ML tooling across all stages.
Solid understanding of statistics, machine learning foundations, and scientific computing.
Proficiency in one or more object-oriented languages (Python, Go, Java, C++).
Advanced SQL abilities.
Experience with modern ML engineering practices including MLOps, model lifecycle management, CI/CD, and monitoring.
Experience with experimental design and scientific methodology.
Customer-focused and pragmatic, delivering value swiftly while maintaining rigor and attention to detail.
Strong stakeholder management and the ability to influence across teams and organizations.
Commitment to continuous learning and improvement.
Experience with big data technologies (Hadoop, Hive, Spark).
Experience with generative AI, large language models, or retrieval-augmented generation (RAG).
Exposure to Agentic AI concepts, including autonomous agents and orchestration frameworks.
Experience applying ML/AI to scientific or R&D workflows, focusing on deployable ML products from research (e.g., simulation, optimization, physics-informed models).
Familiarity with model interpretability, uncertainty quantification, and advanced experimental methodologies.
Proven publications, invention disclosures, or patents in ML or AI.
Energy industry experience is not required.

Technologies

Python
Go
Java
C++
SQL
Hadoop
Hive
Spark

Location and Work Model

Location: Houston, TX. This is a hybrid role combining in-person and remote work. The position requires negligible travel and relocation assistance is not provided.

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1mo ago

Machine Learning Engineer, Motion Planning & Prediction

Avride Austin, TX

Austin, TX

1mo ago Closed

Full time Ai Ml Artificial Intelligence Big Data

This position is no longer accepting applications (closed Jul 16, 2026).

Job Description

Avride is seeking a Machine Learning Engineer to join its autonomous vehicle team focused on motion planning and prediction in Austin, TX (onsite).

Responsibilities

Design, train, and deploy advanced ML models for behavioral prediction and motion planning.
Build robust data pipelines to process, clean, and label large-scale vehicle sensor and simulation datasets.
Leverage transformers and other deep learning architectures to capture temporal interactions among traffic agents.
Define and own model performance metrics; develop evaluation frameworks aligned with on-road safety and performance.
Collaborate with software engineers to integrate and optimize models for real-time on-vehicle embedded hardware inference.
Stay current with ML research, including imitation learning and reinforcement learning, and apply novel techniques to the systems.

Requirements

Strong Python skills and hands-on experience with modern deep learning frameworks such as PyTorch, TensorFlow, or JAX.
Solid grounding in ML fundamentals, including neural network architectures, training methodologies, and evaluation techniques.
Experience across the ML lifecycle, from data exploration and prototyping to deployment and monitoring.
Proficiency in C++ for high-performance model inference code.

Technologies

Python
PyTorch
TensorFlow
JAX
C++
MLflow
Kubeflow
Weights & Biases
Spark
Ray

Nice to Have

Strong track record in ML competitions such as Kaggle or notable contributions to major open-source ML projects.
Experience applying ML to robotics problems like behavioral prediction, motion planning, or computer vision.
Experience with MLOps tools and platforms such as MLflow, Kubeflow, Weights & Biases.
Experience with large-scale distributed data processing and training frameworks such as Spark or Ray.
Publications in top ML or robotics conferences (e.g., NeurIPS, ICML, CVPR, ICLR, CoRL, RSS).

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1mo ago

Machine Learning Engineer

Robert Half Los Angeles, CA

Los Angeles, CA

1mo ago Closed

$200k - $260k/yr Full time Ai Ml Artificial Intelligence Azure Databricks

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

Job Description

Robert Half invites a Machine Learning Engineer to join onsite in Los Angeles, focusing on deploying and operating scalable ML infrastructure and GenAI/LLM systems. The role centers on MLOps platforms, feature stores, vector search, RAG, and CI/CD automation on Databricks. The position offers a competitive annual salary of USD 200,000 to 260,000, along with a comprehensive benefits package and opportunities for professional development.

Benefits

Medical insurance
Vision insurance
Dental insurance
Life insurance
Disability insurance
401(k) plan
Free online training

Responsibilities

Lead the design, implementation, and ongoing maintenance of scalable ML infrastructure on Databricks, including MLflow for experiment tracking, a model registry, and model serving endpoints.
Oversee the development of the ML Ops platform and automated pipelines for deploying, monitoring, and maintaining models in production environments.
Implement robust solutions for model versioning, systematic retraining, and artifact management using Databricks Unity Catalog for ML governance.
Design and manage Databricks Feature Store to ensure consistent feature engineering across training and inference pipelines.
Architect and implement Retrieval-Augmented Generation (RAG) systems for document Q&A, enabling business teams to query fund documents, investor letters, and market research.
Design, deploy, and manage vector database solutions (Databricks Vector Search, Pinecone, or similar) for semantic search and retrieval across enterprise documents.
Lead LLM fine-tuning and customization initiatives, training models like Claude or open-source alternatives with CIM proprietary data while ensuring data privacy and compliance.
Develop and optimize document processing pipelines including PDF parsing, chunking strategies, and embedding generation for RAG applications.
Implement prompt engineering best practices and LLM evaluation frameworks to ensure output quality, relevance, and factual accuracy.
Build guardrails and safety measures for GenAI applications, including hallucination detection, output validation, and source attribution.
Design and implement extensive automation across the ML workflow, covering model training, testing, validation, and deployment using Databricks Workflows and Asset Bundles.
Set up robust CI/CD pipelines for both traditional ML models and GenAI applications, leveraging GitHub Actions, Azure DevOps, or similar tools.
Automate complex data and model workflows utilizing orchestration tools such as Airflow, Prefect, or Databricks Workflows.

Technologies

Databricks
MLflow
Databricks Unity Catalog
Databricks Feature Store
Databricks Vector Search
Pinecone
Claude
GitHub Actions
Azure DevOps
Airflow
Prefect
Databricks Workflows
Asset Bundles
Python
TensorFlow

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1mo ago

Sr. Machine Learning Engineer, Network Engineering

Tesla Fremont, CA

Fremont, CA

1mo ago Closed

$140k - $300k/yr Full time Backend Developer Artificial Intelligence Data Engineer

This position is no longer accepting applications (closed Jun 29, 2026).

Job Description

Senior ML Engineer, Network Engineering role at Tesla in Fremont, CA onsite, focusing on LLM training, network data processing, and intelligent diagnostics.

Responsibilities

Design, implement, and maintain end-to-end pipelines for LLM pretraining, supervised fine-tuning, reward modeling, PPO/RLHF, and model evaluation
Build scalable backend services and distributed training infrastructure to support large-scale ML workloads
Create data ingestion and transformation pipelines for networking datasets including device inventory, topology data, ARP/MAC tables, routing state, metrics, telemetry, and logs
Normalize and model heterogeneous networking data to support ML workflows and agent inference
Develop intelligent agent capabilities, including intent classification, context tracking, troubleshooting logic, and action routing workflows
Collaborate with networking domain experts to translate operational needs into model and platform capabilities
Implement CI/CD, model versioning, orchestration, and production-grade deployment pipelines
Drive architectural decisions to ensure scalability, modularity, reliability, and performance

Requirements

5+ years of experience in applied ML, ML systems, or backend platform engineering
Strong experience with LLM training, including SFT, LoRA/PEFT, RLHF, reward modeling, and PPO
Proficiency with PyTorch and distributed training technologies
Solid backend engineering skills in Python, including APIs, microservices, data modeling, and containerization
Strong understanding of networking fundamentals such as IP addressing, Ethernet, VLANs, routing protocols (BGP/OSPF/ISIS), and topology concepts
Experience working with networking telemetry or operational data (SNMP, flow data, metrics, logs, routing/forwarding tables)
Ability to interpret and model complex network states, device relationships, and diagnostic patterns

Technologies

PyTorch
Python

Benefits

Medical plans with zero payroll deduction
Family-building, fertility, adoption and surrogacy benefits
Dental and vision plans with zero employee contributions
Company-paid Health Savings Account (HSA) contribution
Healthcare Flexible Spending Account (FSA)
Dependent Care Flexible Spending Account (FSA)
401(k) with employer match
Employee Stock Purchase Plan (ESPP)
Company-paid Basic Life and AD&D insurance
Short-term disability insurance
Long-term disability insurance
Employee Assistance Program
Paid time off including sick and vacation
Paid holidays
Back-up childcare resources
Parenting support resources
Critical illness insurance
Hospital indemnity insurance
Accident insurance
Theft & legal services
Pet insurance
Weight loss program
Tobacco cessation program
Tesla Babies program
Commuter benefits
Employee discounts and perks program

What to expect

We seek a highly skilled Senior Software Engineer to lead the development of a platform that unifies large-scale LLM training with advanced network data processing and intelligent diagnostic capabilities. The role requires strong machine-learning engineering expertise, robust software engineering fundamentals, and a solid understanding of network systems.

Expected compensation

Salary: $140,000 - $300,000 per year, plus cash and stock awards and benefits. The final offer may vary based on location, expertise, skills, and experience. Additional elements may be included as part of the overall compensation package, with details provided if an employment offer is extended.

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2mo ago

Sr. Distinguished Machine Learning Engineer

Capital One McLean, VA

McLean, VA

2mo ago Closed

$315k - $359k/yr Full time Senior Cloud Architecture DevOps

This position is no longer accepting applications (closed Jun 9, 2026).

Job Description

Capital One offers a performance-based incentive program with cash bonuses and long-term incentives. This onsite, McLean, VA opportunity places you in a senior leadership role focused on bringing machine learning to production at scale. You will guide architecture and modeling efforts, oversee the development of robust ML systems, and mentor engineers across cross-functional teams.

Responsibilities

Deliver machine learning models and software components that address complex business problems in financial services, collaborating with Product, Architecture, Engineering, and Data Science teams.
Shape and advance ML models and supporting software to enable advanced, intelligent systems.
Lead large scale ML initiatives with the end user in mind.
Utilize cloud based architectures and technologies to deploy optimized ML models at scale.
Refine data pipelines to feed ML models efficiently.
Work with Python, Scala, and C or C++ across development tasks.
Leverage compute frameworks such as Dask and RAPIDS to accelerate processing.
Promote best practices across engineering and modeling lifecycles.
Contribute to recruiting, developing, and retaining top engineering talent.

Requirements

Bachelor’s degree.
At least 10 years designing and building data‑intensive solutions using distributed computing.
At least 7 years programming in C, C++, Python, or Scala.
At least 4 years of experience with the full ML development lifecycle in a business critical setting.