Mid Level Machine Learning Jobs
Mid level machine learning jobs go to engineers ready to own model development end to end, make architectural decisions with limited oversight, and guide junior teammates through production challenges. Openings run across 30% remote and hybrid settings in Technology & Software, Electronics & Hardware, and Artificial Intelligence, with employers like Apple, Scale AI, and Adobe hiring at this level now.
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Showing 5 of 733+ Mid Level Machine Learning jobs
Imagine what you could do here. At Apple, great ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.
Do you love solving complex distributed systems challenges at massive scale? Are you passionate about Kubernetes scheduling, resource management, and building platforms that power the next generation of Machine Learning and Data workloads? Do you thrive in designing and operating highly reliable, large-scale job scheduling and orchestration systems that serve as the backbone of AI and Data infrastructure? If so, join the Apple Data Platform team to design and build a scalable batch and ML infrastructure platform used across Apple.
As part of Apple Data Platform, you will play a meaningful role in designing, developing, and deploying high-performance systems that power batch and ML workloads across Apple's global infrastructure spanning public clouds and Apple data centers. This enormous scale brings unique and complex challenges in resource scheduling, workload orchestration, and operational excellence that require extraordinarily creative problem-solving.
Description
Apple Batch is a fully managed platform within the Apple Data Platform that supports large-scale batch and ML workloads across Apple data centers and AWS/GCP. It orchestrates containerized workloads such as Spark, Ray, and LLM batch inference using YuniKorn/Kueue for advanced multi-cluster scheduling. The platform delivers org/team quota management, automatic node repair, end-to-end observability, strong security, and granular cost reporting.
As part of the Apple Batch team, you will have a meaningful role in designing, developing, and deploying high-performance systems that power large-scale batch processing and ML workloads daily. We are building critical infrastructure that provides scalable batch execution, intelligent Kubernetes-native job scheduling, multi-tenant resource management, and efficient workload orchestration for ML training, inference, and data processing workloads across multi-cloud and on-premises environments.
We are looking for a strong, enthusiastic engineer with deep expertise in Kubernetes scheduling and distributed systems. You will have significant individual responsibility and influence over critical platform services. You are someone with ideas and a real passion for building infrastructure that improves reliability, efficiency, and simplicity at Apple scale.
","responsibilities":"Design, build, and deploy highly reliable, large-scale distributed systems for batch processing and ML infrastructure across public clouds and Apple data centers using Go, Java, or Python
Architect and operate Kubernetes-native scheduling systems such as Kueue and YuniKorn, building custom operators and CRDs to manage complex ML and data workloads
Implement advanced scheduling strategies including gang scheduling, topology-aware routing, bin-packing, and fair-share queuing to maximize GPU efficiency and hardware utilization
Build and manage secure, multi-tenant Kubernetes environments with strict resource isolation, quota governance, and priority-based preemption
Drive end-to-end observability, monitoring, and incident response practices to ensure high availability and fault tolerance of production systems
Collaborate with ML researchers, data engineers, SRE, and product teams to integrate scheduling solutions into Apple's broader AI and data platform ecosystem
Contribute to platform adoption by guiding internal customers, gathering requirements, and delivering impactful platform capabilities
Preferred Qualifications
GPU scheduling, accelerator-aware placement, and optimization for large-scale AI/ML workloads
Experience with distributed data and ML frameworks such as Apache Spark, Ray, PyTorch, JAX, or Flink at scale
Experience contributing to open-source projects in Kubernetes scheduling, container technologies, or ML infrastructure ecosystems such as Apache YuniKorn, Kueue, or similar systems
Experience using GenAI technologies to improve developer productivity, streamline engineering processes, and accelerate team execution
Minimum Qualifications
5+ years of experience designing, developing, and operating highly available, large-scale distributed systems and data or ML infrastructure
Strong software engineering skills with deep programming expertise in Go, Java, or Python
Advanced knowledge of Kubernetes internals including custom controllers, scheduler architecture, resource quotas, and workload lifecycle management
Hands-on experience with Kubernetes-native batch scheduling frameworks such as Kueue or YuniKorn and advanced scheduling concepts like gang scheduling, bin-packing, and priority preemption
Experience with cloud-native infrastructure across multi-cloud environments including AWS, GCP, and on-premises systems
Strong commitment to operational excellence, system observability, and continuous improvement for mission-critical services
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $171,600 and $302,200, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. 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.
Imagine what you could do here. At Apple, great ideas have a way of becoming phenomenal products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.
Do you love solving complex distributed systems challenges at massive scale? Are you passionate about Kubernetes scheduling, resource management, and building platforms that power the next generation of Machine Learning and Data workloads? Do you thrive in designing and operating highly reliable, large-scale job scheduling and orchestration systems that serve as the backbone of AI and Data infrastructure? If so, join the Apple Data Platform team to design and build a scalable batch and ML infrastructure platform used across Apple.
As part of Apple Data Platform, you will play a meaningful role in designing, developing, and deploying high-performance systems that power batch and ML workloads across Apple's global infrastructure spanning public clouds and Apple data centers. This enormous scale brings unique and complex challenges in resource scheduling, workload orchestration, and operational excellence that require extraordinarily creative problem-solving.
Description
Apple Batch is a fully managed platform within the Apple Data Platform that supports large-scale batch and ML workloads across Apple data centers and AWS/GCP. It orchestrates containerized workloads such as Spark, Ray, and LLM batch inference using YuniKorn/Kueue for advanced multi-cluster scheduling. The platform delivers org/team quota management, automatic node repair, end-to-end observability, strong security, and granular cost reporting.
As part of the Apple Batch team, you will have a meaningful role in designing, developing, and deploying high-performance systems that power large-scale batch processing and ML workloads daily. We are building critical infrastructure that provides scalable batch execution, intelligent Kubernetes-native job scheduling, multi-tenant resource management, and efficient workload orchestration for ML training, inference, and data processing workloads across multi-cloud and on-premises environments.
We are looking for a strong, enthusiastic engineer with deep expertise in Kubernetes scheduling and distributed systems. You will have significant individual responsibility and influence over critical platform services. You are someone with ideas and a real passion for building infrastructure that improves reliability, efficiency, and simplicity at Apple scale.
","responsibilities":"Design, build, and deploy highly reliable, large-scale distributed systems for batch processing and ML infrastructure across public clouds and Apple data centers using Go, Java, or Python
Architect and operate Kubernetes-native scheduling systems such as Kueue and YuniKorn, building custom operators and CRDs to manage complex ML and data workloads
Implement advanced scheduling strategies including gang scheduling, topology-aware routing, bin-packing, and fair-share queuing to maximize GPU efficiency and hardware utilization
Build and manage secure, multi-tenant Kubernetes environments with strict resource isolation, quota governance, and priority-based preemption
Drive end-to-end observability, monitoring, and incident response practices to ensure high availability and fault tolerance of production systems
Collaborate with ML researchers, data engineers, SRE, and product teams to integrate scheduling solutions into Apple's broader AI and data platform ecosystem
Contribute to platform adoption by guiding internal customers, gathering requirements, and delivering impactful platform capabilities
Preferred Qualifications
GPU scheduling, accelerator-aware placement, and optimization for large-scale AI/ML workloads
Experience with distributed data and ML frameworks such as Apache Spark, Ray, PyTorch, JAX, or Flink at scale
Experience contributing to open-source projects in Kubernetes scheduling, container technologies, or ML infrastructure ecosystems such as Apache YuniKorn, Kueue, or similar systems
Experience using GenAI technologies to improve developer productivity, streamline engineering processes, and accelerate team execution
Minimum Qualifications
5+ years of experience designing, developing, and operating highly available, large-scale distributed systems and data or ML infrastructure
Strong software engineering skills with deep programming expertise in Go, Java, or Python
Advanced knowledge of Kubernetes internals including custom controllers, scheduler architecture, resource quotas, and workload lifecycle management
Hands-on experience with Kubernetes-native batch scheduling frameworks such as Kueue or YuniKorn and advanced scheduling concepts like gang scheduling, bin-packing, and priority preemption
Experience with cloud-native infrastructure across multi-cloud environments including AWS, GCP, and on-premises systems
Strong commitment to operational excellence, system observability, and continuous improvement for mission-critical services
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $171,600 and $302,200, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. 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.
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Find JobsMid Level Machine Learning Job Market
Who's Hiring
- Apple131
- Scale AI19
- Adobe18
- Zoox16
- Iterable14
Top Industries Hiring
- Technology & Software285
- Electronics & Hardware135
- Artificial Intelligence67
- Banking & Financial Services48
- Retail33
Mid Level Machine Learning Jobs: Frequently Asked Questions
How do I get a mid level machine learning job?
Position yourself around ownership, not just contribution. Highlight projects where you drove a model from research through deployment, made design trade-offs, and measured real-world impact. Tailor your resume to show depth in a specific area, such as NLP, computer vision, or recommendation systems, and demonstrate that you can operate with limited day-to-day direction. That combination is what mid level hiring managers screen for.
Which companies hire mid level machine learnings?
Companies hiring mid level machine learnings right now include Apple, Scale AI, and Adobe, based on current listings on Migrate Mate as of July 2026. Hiring at this level comes from a wide range of organizations, including established tech companies, growth-stage startups scaling their AI capabilities, and large enterprises building out dedicated applied machine learning teams.
Are there remote mid level machine learning jobs?
Yes, and flexibility at this level is common. About 30% of mid level machine learning openings are remote or hybrid as of July 2026, reflecting how well the work translates to distributed teams. Fully on-site roles do exist, typically at companies with strict data security requirements or where close collaboration with hardware and infrastructure teams is essential.
How do I move up to a mid level machine learning role?
The path from entry level to mid level is built on demonstrated ownership over time. Engineers who advance focus on shipping models to production rather than only running experiments, take on progressively larger scopes with less guidance, and develop a specialty where they can go deep. Documenting measurable outcomes, such as accuracy improvements or latency reductions, builds the portfolio that makes the jump credible to hiring teams.
Which industries hire the most mid level machine learnings?
Mid Level machine learning roles concentrate in Technology & Software, Electronics & Hardware, and Artificial Intelligence, based on current listings on Migrate Mate as of July 2026. These sectors drive hiring at this level because they have both the data volume and the product complexity that make mid level ML expertise, rather than generalist software engineering, the right fit for their core technical challenges.