Senior AI Infrastructure Engineer - Training Platform Job at Scale (San Francisco)

Senior ML Platform Engineer, AI Platform

We are seeking a skilled and passionate ML Platform Engineer to join our team an...

Location

Singapore , Singapore

Salary:

Not provided

Airwallex

Expiration Date

Until further notice

Requirements

5+ years in backend software development
at least 2+ years focus on AI/ML Platform or MLOps infrastructure
deep expertise in MLOps practices, including automated deployment pipelines, model optimization, and production lifecycle management
proven experience designing and implementing low-latency model serving solutions
proficiency in Python
skill in writing high-quality, maintainable code
experience in design and development of large-scale distributed, high concurrency, low-latency inference, high availability systems
excellent communication and mentoring abilities
a relevant degree in Computer Science, Mathematics or related fields

Job Responsibility

Platform Development: Design, build, and maintain the end-to-end MLOps platform using Kubernetes and Cloud Services
Infrastructure as Code (IaC): Use Terraform or similar tools to manage, provision, and scale all ML-related infrastructure securely and efficiently
Pipeline Automation: Implement and optimize CI/CD/CT (Continuous Integration, Delivery, Training) pipelines to automate model training, testing, packaging, and deployment using tools like Argo and Kubeflow Pipelines
Serving Infrastructure: Build highly available, low-latency, and high-throughput model serving infrastructure
Observability: Implement robust monitoring, alerting, and logging solutions to track infrastructure health, model performance, and data/model drift
Tooling & Support: Evaluate, integrate, and support ML tools such as Feature Stores and distributed model training pipelines
Security & Compliance: Ensure platform security, implement RBAC (Role-Based Access Control), and manage secrets for sensitive data and production environments
Collaboration: Work closely with Data Scientists and ML Engineers to understand their needs and provide technical guidance on best practices for scaling their models

Fulltime

Senior Software Engineer, Managed AI - AI Platform

Be a part of the AI revolution with sustainable technology at Crusoe. Here, you'...

Location

United States , San Francisco, CA; Sunnyvale, CA

Salary:

172425.00 - 209000.00 USD / Year

Crusoe

Expiration Date

Until further notice

Requirements

Advanced degree in Computer Science/Engineering
4-5+ years of industry experience with demonstrated history of consistent success leading a varied portfolio of initiatives across your function
Experience with distributed systems, cloud services (compute, storage, networking, database), and delivering early-stage projects quickly
Experience with Generative AI (LLMs, Multimodal) and familiar with AI infrastructure (training, inference, ETL pipelines)
Proficient with container runtimes (e.g., Kubernetes), microservices, REST APIs, gRPC, and the full software development lifecycle including CI/CD

Job Responsibility

Lead the design and implementation of core AI services, including: Resilient fault-tolerant queues for efficient task distribution
Model catalogs for managing and versioning AI models
Scheduling mechanisms optimized for cost and performance
Architect and scale infrastructure to handle millions of API requests per second
Implement robust monitoring and alerting to ensure system health and 24/7 availability
Collaborate closely with product management, business strategy, and other engineering teams to define the AI platform roadmap
Influence the long-term vision and architectural decisions of the platform
Contribute to open-source AI frameworks and actively participate in the AI community
Prototype and rapidly iterate on emerging technologies and new features

What we offer

Restricted Stock Units
Health insurance package options that include HDHP and PPO, vision, and dental for you and your dependents
Employer contributions to HSA accounts
Paid Parental Leave
Paid life insurance, short-term and long-term disability
Teladoc
401(k) with a 100% match up to 4% of salary
Generous paid time off and holiday schedule
Cell phone reimbursement
Tuition reimbursement

Fulltime

Senior ML Engineer - AI Platform & Agents

We are building agentic AI into the core of our product and need someone who can...

Location

France , Bordeaux

Salary:

Not provided

PhantomBuster

Expiration Date

Until further notice

Requirements

5+ years of experience as an ML Engineer, AI Engineer, or Software Engineer with a strong AI focus
Hands-on experience building AI agents using frameworks such as LangChain, Amazon Bedrock AgentCore, or similar
Strong understanding of LLM-based systems: prompt engineering, agent orchestration, tool use, and multi-agent workflows
Familiarity with MCP (Model Context Protocol) and experience integrating agents with external APIs or data sources
Experience working with Agents for Amazon Bedrock AgentCore or similar agent setups
Strong understanding of machine learning algorithms, statistical methods, and data preprocessing techniques
Experience with cloud platforms for model training and deployment, especially AWS
Proficiency in Python, including LangChain, and standard data libraries (Pandas, NumPy, etc.)
Fluency in English

Job Responsibility

Define and evolve our infrastructure to allow for better ML and AI capabilities, with a focus on LLM-based and agentic systems
Contribute to the development and expansion of our agentic AI framework powered by AWS Bedrock, enabling both internal tools and customer-facing features
Identify, source, and refine datasets to allow tuning models, powering retrieval pipelines, or expanding agentic workflows
Pre-process data by using techniques such as data cleaning, feature engineering, and transformation
Train, evaluate, and deploy both LLM-based systems and traditional machine learning models into production
Monitor, debug, and continuously improve deployed models and AI tools
Support machine learning usage throughout the company, including selecting the right modeling approach for the use case (LLM vs. traditional ML)
Support the integration and use of LLMs, including approaches such as fine-tuning, prompt tuning, and retrieval-augmented generation (RAG), to improve accuracy

What we offer

International team
Fun team building events
€40/month for remote work
Flexible working time
Home office budget up to €1500
100% of an Alan Blue subscription
Lunch vouchers - €8 (50% The Phantom Company) / worked day
Partnership with MokaCare
€70 a month benefit for entertainment expenses
Book Allowance and Sharing Program

Senior ML Engineer - AI Platform & Agents

Join PhantomBuster as a Senior ML Engineer to build agentic AI with AWS Bedrock,...

Location

France; Spain; Portugal

Salary:

Not provided

PhantomBuster

Expiration Date

Until further notice

Requirements

5+ years of experience as an ML Engineer, AI Engineer, or Software Engineer with a strong AI focus
Hands-on experience building AI agents using frameworks such as LangChain, Amazon Bedrock AgentCore, or similar
Strong understanding of LLM-based systems: prompt engineering, agent orchestration, tool use, and multi-agent workflows
Familiarity with MCP (Model Context Protocol) and experience integrating agents with external APIs or data sources
Experience working with Agents for Amazon Bedrock AgentCore or similar agent setups
Strong understanding of machine learning algorithms, statistical methods, and data preprocessing techniques
Experience with cloud platforms for model training and deployment, especially AWS
Proficiency in Python, including LangChain, and standard data libraries (Pandas, NumPy, etc.)
Fluency in English

Job Responsibility

Define and evolve our infrastructure to allow for better ML and AI capabilities, with a focus on LLM-based and agentic systems
Contribute to the development and expansion of our agentic AI framework powered by AWS Bedrock, enabling both internal tools and customer-facing features
Identify, source, and refine datasets to allow tuning models, powering retrieval pipelines, or expanding agentic workflows
Pre-process data by using techniques such as data cleaning, feature engineering, and transformation
Train, evaluate, and deploy both LLM-based systems and traditional machine learning models into production
Monitor, debug, and continuously improve deployed models and AI tools
Support machine learning usage throughout the company, including selecting the right modeling approach for the use case (LLM vs. traditional ML)
Support the integration and use of LLMs, including approaches such as fine-tuning, prompt tuning, and retrieval-augmented generation (RAG), to improve accuracy

What we offer

Fully remote working environment (France, Spain, or Portugal)
Real ownership: you will define how agentic AI is built at PhantomBuster, not follow someone else's decisions
Freedom to research and adopt new technologies as the space evolves & to make an impact at a small, self-funded, and profitable tech startup by laying the foundation for machine learning and AI
Collaborative and open-minded culture based on rationality, humility, honesty, and long-term thinking
International team
Fun team building events
€40/month for remote work
Flexible working time
Home office budget up to €1500
100% of an Alan Blue subscription (french-based contracts)

Fulltime

Senior Software Engineer, AI Platform and Enablement

We're building a next-generation AI-powered platform and web application for cre...

Location

United States , San Francisco

Salary:

180000.00 - 286000.00 USD / Year

Descript

Expiration Date

Until further notice

Requirements

Experience in deploying and managing AI models in production
Experience with the tools of large volume data pipelines like spark, flume, dask, etc.
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and container technologies (Docker, Kubernetes)
Knowledge of DevOps and MLOps best practices
Strong problem-solving abilities and excellent communication skills

Job Responsibility

Build, maintain, and standardize third-party model integrations, including consulting for other engineering teams with AI model integration needs
Design, implement, and maintain our AI infrastructure supporting our machine learning life cycle, including data ingestion pipelines, training developer experience and infrastructure, evaluation frameworks, and deployments / GPU infrastructure
Collaborate with Product Managers, Research Engineers, and AI Researchers to understand their infrastructure needs and ensure our AI systems are robust, scalable, and efficient
Optimize and scale our models and algorithms for efficient inference
Deploy, monitor, and manage AI models in production

What we offer

Generous healthcare package
401k matching program
Catered lunches
Flexible vacation time

Fulltime

Senior ML Infrastructure Engineer - Embodied AI

At General Motors, our product teams are redefining mobility. Through a human-ce...

Location

United States , Sunnyvale

Salary:

153200.00 - 234100.00 USD / Year

General Motors

Expiration Date

Until further notice

Requirements

3+ years of experience working on large-scale distributed systems, applications, or ML infrastructure
Experience designing robust services or frameworks with durable, well-designed APIs
Solid understanding of machine learning workflows and hands-on experience applying ML systems in production environments
Experience building reliable, high-performance, and cost-efficient systems on modern cloud infrastructure
Practical experience across the ML development lifecycle, including model training, deployment, and MLOps practices
Strong cross-functional collaboration skills across teams and organizations
Strong coding skills in Python or C++
Interest in autonomous driving and large-scale ML systems
BS, MS, or PhD in Computer Science, Mathematics, or equivalent practical experience

Job Responsibility

Design, implement, and deploy scalable platforms and tools supporting machine learning training and evaluation workflows across GM
Drive complex technical projects with strong ownership of implementation, code quality, and system reliability
Contribute to technical design discussions and architectural decisions while collaborating with senior engineers and technical leads
Work closely with partner teams to ensure platforms meet real-world ML development needs and maximize adoption
Identify technical improvements and help prioritize platform investments to improve performance, reliability, and developer productivity
Contribute to a strong engineering culture through high-quality code reviews, documentation, and operational excellence
Support onboarding and mentoring of junior engineers and interns

What we offer

medical
dental
vision
Health Savings Account
Flexible Spending Accounts
retirement savings plan
sickness and accident benefits
life insurance
paid vacation & holidays
tuition assistance programs

Fulltime

New

Senior Machine Learning Engineer, AI Platform

The AI Platform team is responsible for building the foundational infrastructure...

Location

United States; Canada

Salary:

139000.00 - 218000.00 USD / Year

Mozilla

Expiration Date

Until further notice

Requirements

Bachelor’s degree with 4–6 years of relevant industry experience, or Master’s degree with significant hands-on experience building and operating production ML systems, or work experience equivalent
Strong experience developing in Python for machine learning systems, backend services, or distributed data processing
Proven experience deploying and operating ML workloads in cloud environments, including production-grade infrastructure
Solid understanding of model serving architectures, inference pipelines, and performance tradeoffs (latency, throughput, cost, scaling strategies)
Hands-on experience working with GPU-based workloads and accelerated computing in production settings
Experience designing CI/CD pipelines and development workflows that support reliable ML system deployment
Ability to independently scope and drive technical initiatives while balancing product and operational priorities
Strong problem-solving skills and the ability to debug performance and reliability issues in distributed systems
Clear and effective communication skills, with experience collaborating across engineering, product, and infrastructure teams

Job Responsibility

Design, build, and operate core AI platform components used to train, deploy, and serve machine learning models in production environments
Own model serving and inference workflows end-to-end, driving improvements in reliability, scalability, performance, and operational excellence
Lead efforts to optimize inference systems for throughput, latency, and cost efficiency across CPU and GPU workloads
Design and manage GPU-based inference and training workloads, including performance tuning, capacity planning, and resource utilization optimization
Own and improve critical parts of the model lifecycle, including packaging, versioning, testing strategies, validation, and deployment automation
Implement and evolve observability practices (metrics, logging, tracing, alerting) to improve visibility and operational resilience of ML services and pipelines
Partner closely with product, infrastructure, security, and data teams to design scalable platform capabilities that enable AI-powered features
Contribute to technical design discussions, propose architectural improvements, and mentor junior engineers through code reviews and knowledge sharing
Participate in and help improve operational processes, including incident response, on-call rotations, and post-incident reviews

What we offer

Generous performance-based bonus plans
Rich medical, dental, and vision coverage
Generous retirement contributions with 100% immediate vesting
Quarterly all-company wellness days
Country specific holidays plus a day off for your birthday
One-time home office stipend
Annual professional development budget
Quarterly well-being stipend
Considerable paid parental leave
Employee referral bonus program

Fulltime

Senior Machine Learning Engineer, ML Training Platform

Location

United States

Salary:

216700.00 - 303400.00 USD / Year

Expiration Date

Until further notice

Requirements

5+ years of software engineering experience, with a focus on Platform Engineering, ML Infrastructure, or Backend Systems
Deep Kubernetes Expertise: You know K8s beyond just 'deploying pods.' You understand CRDs, Controllers and the Operator pattern
Jupyter Ecosystem Knowledge: Experience customizing JupyterHub, JupyterLab extensions, or building similar interactive computing platforms
Strong Coding Skills: Proficiency in Python (for the ML ecosystem) and Go (for Kubernetes controllers/infrastructure tooling)
GPU Experience: Hands-on practice with CUDA environments, GPU virtualization/containerization, and doing it all within Kubernetes
Cloud Provider Experience: Familiarity with both managed ML offerings (Vertex AI, Sagemaker, etc) and building custom ML components in AWS and/or GCP
Experience working with distributed training frameworks, including Ray and Kubernetes
Comfortable with distributed systems, big data (Petabyte scale) and data-intensive systems
Strong focus on scalability, reliability, performance, and ease of use. You are an undying advocate for platform users and have a deep intuition for the machine learning development lifecycle
Strong organizational & communication skills

Job Responsibility

Lead the building, testing, and maintenance of ML training infrastructure at Reddit
Play a pivotal role in designing, building, and optimizing the infrastructure and tooling required to support large-scale machine learning workflows
Evolve the MLE experience, from provisioning interactive GPU environments through large-scale training, supporting on-demand and self-service workflows
Kubernetes Automation: Write custom Kubernetes Controllers and Operators to manage the lifecycle of interactive Jupyter workspaces and long-running ML training jobs, handle auto-idling, and ensure fault tolerance
GPU Orchestration: Work with the underlying compute team to ensure MLEs have efficient access to training hardware resources and handle resource contention gracefully
Developer Experience (DevX): Treat internal MLEs as your customers. Conduct user research, reduce friction in the 'Idea-to-Prototype' loop, and standardize software environments (Docker images, Python dependency management)

What we offer

Comprehensive Healthcare Benefits and Income Replacement Programs
401k Match
Family Planning Support
Gender-Affirming Care
Mental Health & Coaching Benefits
Flexible Vacation & Reddit Global Days off
Generous paid Parental Leave
Paid Volunteer time off

Fulltime

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Senior AI Infrastructure Engineer - Training Platform

Job Description

Job Responsibility

Requirements

Nice to have

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