AI ML Platform Jobs
AI ML Platform jobs are open across technology, finance, healthcare, and defense, from new-grad to staff and principal engineer, with specializations in MLOps, model serving infrastructure, and distributed training pipelines. Find a role that fits from the openings below and apply directly.
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Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.
This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.
Job Title: ML Platform Engineer
Location: 100% Remote (U.S.)
Position Type: Full-time, Direct W2
Salary Range: $100,000–$160,000 Annually
Experience Required: 10+ years
Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.
Job Summary:
We are seeking a ML Platform Engineer to design, build, and operate high-performance, highly reliable inference platforms for serving large machine learning models in production. The role focuses on the systems engineering side of AI deployment, including request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse model workloads. The ideal candidate brings strong distributed systems and performance engineering expertise, has shipped serving systems at scale, and understands the trade-offs between latency, throughput, cost, and quality in ML serving.
Key Responsibilities
- Design and operate model serving platforms supporting diverse workloads including LLMs, vision models, and recommendation systems.
- Optimize inference performance using continuous batching, paged attention, speculative decoding, and request multiplexing.
- Implement multi-tenant routing, rate limiting, and quality-of-service policies across model endpoints.
- Build autoscaling and capacity management systems that balance latency, throughput, and cost.
- Tune GPU utilization, memory management, and KV cache strategies for LLM serving workloads.
- Integrate model serving with API gateways, identity systems, and observability platforms.
- Implement caching, prompt deduplication, and response reuse strategies where appropriate.
- Drive end-to-end observability including latency histograms, queue dynamics, GPU utilization, and error tracking.
- Develop deployment workflows including canary releases, shadow testing, and automated rollback.
- Operate incident response for high-availability AI services and drive durable reliability improvements.
- Collaborate with ML and product teams to support new model releases and capability rollouts.
- Implement security controls including request signing, content filtering, and abuse detection at the serving layer.
- Document operational procedures, performance characteristics, and tuning guidance for internal teams.
- Stay current with AI serving research and translate advances into production capabilities.
- Bachelor’s or Master’s degree in Computer Science or a related field.
- 10 or more years of experience in distributed systems, infrastructure, or ML platform engineering.
- Strong proficiency in Python and a systems language such as Go, Rust, or C++.
- Deep experience operating high-throughput, low-latency services in production.
- Hands-on experience with LLM or large model inference frameworks such as vcLLM or TensorRT-LLM.
- Strong understanding of GPU architecture, memory hierarchies, and accelerator utilization.
- Familiarity with Kubernetes, autoscaling, and modern cloud platforms.
- Experience with observability stacks including metrics, tracing, and structured logging.
- Solid grounding in performance engineering and capacity planning.
- Strong communication and incident response skills.
- Open-source contributions to model serving infrastructure.
- Experience with multi-region or globally distributed AI serving.
- Familiarity with model quantization, distillation, and compression techniques.
- Exposure to FinOps for AI workloads and cost-efficient serving design.
- Experience supporting external-facing AI APIs at scale.
How to Apply
Would you like to know more about this opportunity? For immediate consideration, please send your resume to venkat.r@bvteck.com or contact us at (908) 505-3899. Learn more about Bright Vision Technologies at www.bvteck.com.
Bright Vision Technologies is an Equal Opportunity Employer.
Equal Employment Opportunity (EEO) Statement
Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.
BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.
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Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.
This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.
Job Title: ML Platform Engineer
Location: 100% Remote (U.S.)
Position Type: Full-time, Direct W2
Salary Range: $100,000–$160,000 Annually
Experience Required: 10+ years
Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.
Job Summary:
We are seeking a ML Platform Engineer to design, build, and operate high-performance, highly reliable inference platforms for serving large machine learning models in production. The role focuses on the systems engineering side of AI deployment, including request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse model workloads. The ideal candidate brings strong distributed systems and performance engineering expertise, has shipped serving systems at scale, and understands the trade-offs between latency, throughput, cost, and quality in ML serving.
Key Responsibilities
- Design and operate model serving platforms supporting diverse workloads including LLMs, vision models, and recommendation systems.
- Optimize inference performance using continuous batching, paged attention, speculative decoding, and request multiplexing.
- Implement multi-tenant routing, rate limiting, and quality-of-service policies across model endpoints.
- Build autoscaling and capacity management systems that balance latency, throughput, and cost.
- Tune GPU utilization, memory management, and KV cache strategies for LLM serving workloads.
- Integrate model serving with API gateways, identity systems, and observability platforms.
- Implement caching, prompt deduplication, and response reuse strategies where appropriate.
- Drive end-to-end observability including latency histograms, queue dynamics, GPU utilization, and error tracking.
- Develop deployment workflows including canary releases, shadow testing, and automated rollback.
- Operate incident response for high-availability AI services and drive durable reliability improvements.
- Collaborate with ML and product teams to support new model releases and capability rollouts.
- Implement security controls including request signing, content filtering, and abuse detection at the serving layer.
- Document operational procedures, performance characteristics, and tuning guidance for internal teams.
- Stay current with AI serving research and translate advances into production capabilities.
- Bachelor’s or Master’s degree in Computer Science or a related field.
- 10 or more years of experience in distributed systems, infrastructure, or ML platform engineering.
- Strong proficiency in Python and a systems language such as Go, Rust, or C++.
- Deep experience operating high-throughput, low-latency services in production.
- Hands-on experience with LLM or large model inference frameworks such as vcLLM or TensorRT-LLM.
- Strong understanding of GPU architecture, memory hierarchies, and accelerator utilization.
- Familiarity with Kubernetes, autoscaling, and modern cloud platforms.
- Experience with observability stacks including metrics, tracing, and structured logging.
- Solid grounding in performance engineering and capacity planning.
- Strong communication and incident response skills.
- Open-source contributions to model serving infrastructure.
- Experience with multi-region or globally distributed AI serving.
- Familiarity with model quantization, distillation, and compression techniques.
- Exposure to FinOps for AI workloads and cost-efficient serving design.
- Experience supporting external-facing AI APIs at scale.
How to Apply
Would you like to know more about this opportunity? For immediate consideration, please send your resume to venkat.r@bvteck.com or contact us at (908) 505-3899. Learn more about Bright Vision Technologies at www.bvteck.com.
Bright Vision Technologies is an Equal Opportunity Employer.
Equal Employment Opportunity (EEO) Statement
Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.
BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.
sVZC0iVuJh
AI ML Platform Jobs by Experience Level
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Who's Hiring
Top Industries Hiring
- Insurance10
- Technology & Software9
- Science & Research1
- Agriculture & Farming1
- Media & Entertainment1
What Employers Look For
The qualifications that appear most often in AI ML platform jobs.
- Proficiency in Python and experience building or maintaining ML pipelines at scale
- Hands-on experience with containerization and orchestration tools such as Docker and Kubernetes
- Familiarity with at least one managed ML platform such as SageMaker, Vertex AI, or Azure ML
- Experience designing or operating distributed training and model serving infrastructure
- Understanding of CI/CD principles applied to model training, evaluation, and deployment workflows
- Bachelor's or master's degree in computer science, engineering, or a closely related quantitative field
Tips for Your AI ML Platform Job Search
Quantify your infrastructure impact clearly
Hiring managers want to see throughput, latency, or cost numbers tied to work you shipped. Replace vague descriptions like 'improved model deployment' with concrete outcomes such as reduced pipeline runtime or cut cloud spend on inference workloads.
Separate MLOps from software engineering roles
AI ML platform openings split into infrastructure-heavy and research-adjacent tracks. Read job descriptions carefully for keywords like Kubeflow, Ray, or Triton versus SageMaker or Vertex AI to apply to roles that actually match your stack and experience level.
Apply early to roles that fit
Migrate Mate lists ai ml platform openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Build a public artifact before your system design round
AI ML platform interviews almost always include a system design exercise. Pushing a reproducible training pipeline or a feature store prototype to GitHub before you interview gives you a real example to reference when describing architectural trade-offs.
Tailor your cover letter to the orchestration stack
Most teams list their orchestration tools in the job description. Mentioning Airflow, Prefect, or Argo Workflows by name, with context on how you used them, signals you'll ramp faster than candidates who write generic platform experience statements.
Negotiate scope alongside compensation
In AI ML platform roles, ownership of the platform roadmap varies widely between companies. Ask during the offer stage which components the team controls end-to-end versus which are handed off to data science or DevOps, so you know the actual scope before accepting.
AI ML Platform Jobs: Frequently Asked Questions
Which companies are hiring the most ai ml platforms?
The companies hiring the most ai ml platforms right now include GEICO, AMD, and Braze, with the largest share of openings in California, New York, and Texas, based on current listings on Migrate Mate as of November 2026. Demand is concentrated in companies scaling inference infrastructure or moving model development from research into production.
How many ai ml platform jobs are remote?
About 86% of ai ml platform openings are fully remote or hybrid as of November 2026, reflecting strong demand for distributed engineering talent. Model serving, pipeline observability, and feature engineering sub-roles tend to be the most remote-friendly, while roles involving on-premise GPU cluster management are more likely to require on-site presence.
How do you become a ai ml platform?
Start by building a solid foundation in Python, distributed systems, and at least one cloud provider. Work on end-to-end ML pipeline projects, even personal ones, to develop hands-on experience with orchestration, versioning, and model deployment. Contributing to open-source MLOps tools strengthens your portfolio. Moving into the role often means transitioning from a software engineering or data engineering background while picking up ML-specific tooling on the job.
Can you get an ai ml platform job with little experience?
Yes, entry-level ai ml platform roles exist, particularly at companies building out their platforms for the first time. Focus on demonstrating working knowledge of a pipeline orchestration tool, containerization basics, and a completed end-to-end project. Applying to smaller companies or startups where the platform team is early-stage gives you a better chance of being evaluated on potential rather than years of experience.
What does the ai ml platform interview process look like?
Most ai ml platform interviews include a recruiter screen, a technical phone interview covering Python and systems fundamentals, and an on-site or virtual loop with a machine learning system design round, a coding exercise focused on data structures or distributed concepts, and a cross-functional interview with data scientists or product managers. Some companies also include a take-home that asks you to design or debug a pipeline component.
Where can I find and apply to ai ml platform jobs?
You can find and apply to ai ml platform jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your experience and specialization, then apply directly to each one that fits. New openings are added regularly, so checking back frequently gives you access to roles as soon as they're posted.
See All 53 AI ML Platform Jobs
Find roles that match your experience and apply in just a few clicks.
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