ML Platform Engineer

Bright Vision Technologies•North Brunswick, NJ

1d•$100,000 - $160,000•Remote

About The Position

Bright Vision Technologies is seeking an ML Platform Engineer to design, build, and operate high-performance, reliable inference platforms that serve large machine learning models in production. The work focuses on systems engineering for AI deployment, including request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse model workloads. The ideal candidate has strong distributed systems and performance engineering expertise, has shipped serving systems at scale, and understands trade-offs among latency, throughput, cost, and quality in ML serving. This is a full-time, direct W2 position that is 100% remote in the United States. The stated salary range is $100,000–$160,000 annually. Candidates who are U.S. citizens, green card holders, EAD holders, or eligible for an H-1B transfer are encouraged to apply; the employer cannot sponsor new H-1B petitions.

Requirements

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 vLLM 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.

Nice To Haves

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.

Responsibilities

Design and operate model-serving platforms for 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 highly available AI services and drive durable reliability improvements.
Collaborate with ML and product teams to support new model releases and capability rollouts.
Implement serving-layer security controls, including request signing, content filtering, and abuse detection.
Document operational procedures, performance characteristics, and tuning guidance for internal teams.
Stay current with AI serving research and translate advances into production capabilities.