Remote AI ML Platform Jobs
Remote AI ML Platform jobs are open across the U.S. in sectors like cloud infrastructure, enterprise software, and data-intensive industries, at companies ranging from remote-first AI startups to large distributed engineering teams. Employers hiring remotely right now include ICAAI, reddit, and BV Teck. See the openings below and apply to the ones that match your experience.
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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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Who's Hiring


Top Industries Hiring
- Technology & Software
- Media & Entertainment
What Employers Look For
The qualifications that appear most often in remote 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 Remote AI ML Platform Job Search
Show your async collaboration skills upfront
Remote ai ml platform teams run on written communication. Highlight experience with async-first tools like Confluence, Notion, or Slack documentation in your materials. Employers screening for remote readiness look for candidates who can unblock themselves and communicate decisions clearly in writing.
Build a public portfolio of platform work
Remote hiring managers can't watch you work, so a public GitHub with real MLOps pipelines, feature stores, or model serving infrastructure does the work for you. Documented projects that show you've built and maintained shared ML platform components are far more persuasive than a job title alone.
Apply early to remote roles that fit
Migrate Mate lists remote ai ml platform openings from across the U.S. in one place. Search the current openings, find roles that match your stack and seniority, and apply directly. Remote roles often close faster than on-site ones because the candidate pool is national.
Prepare for a remote-native technical interview
Remote ai ml platform interviews typically include live system design sessions over video with shared diagramming tools like Miro or Excalidraw. Practice talking through distributed training infrastructure, model serving architectures, and pipeline observability out loud, since remote interviewers are assessing how clearly you communicate technical decisions.
Remote AI ML Platform Jobs: Frequently Asked Questions
How do I get a remote ai ml platform job?
Remote ai ml platform roles go to candidates who can demonstrate self-directed execution and strong async communication alongside deep technical skills. Remote-first software companies and distributed AI teams are the most consistent hirers. Showing proficiency in MLOps tooling, model deployment pipelines, and infrastructure-as-code gives you a concrete edge, and a portfolio of shipped platform work is far more persuasive than credentials alone when you're competing for a fully remote seat.
Which companies hire remote ai ml platforms?
Companies hiring remote ai ml platforms right now include ICAAI, reddit, and BV Teck, based on current remote listings on Migrate Mate as of August 2026. Remote-first technology firms and distributed engineering teams across cloud services, enterprise AI, and data infrastructure are the most consistent sources of these openings.
Can you get a remote ai ml platform job with no experience?
Yes, but remote entry-level ai ml platform roles are harder to land because employers expect you to work independently from day one with minimal hand-holding. Your best path is contributing to open-source MLOps or platform projects, building a public portfolio of pipeline or infrastructure work, and targeting smaller remote-first AI companies that are more willing to invest in developing junior platform engineers.
Do you need a degree for remote ai ml platform jobs?
Not always. Remote employers hiring ai ml platform engineers weigh demonstrated skills, shipped platform projects, and familiarity with tools like Kubernetes, Kubeflow, Ray, or cloud ML services more heavily than a diploma. A strong GitHub history, contributions to ML infrastructure projects, or certifications in cloud and MLOps can substitute effectively, especially at remote-first companies that evaluate candidates primarily on technical output.
Which industries hire the most remote ai ml platforms?
Most remote ai ml platform openings sit in Technology & Software and Media & Entertainment, per current remote listings on Migrate Mate as of August 2026. These sectors rely on distributed engineering teams that build and maintain shared ML infrastructure across multiple product lines and geographies, making remote platform roles structurally natural.
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