Staff/Principal DevOps Engineer, AI Inference

Lila Sciences; Cambridge (MA); On-site; USD 192,000 - 272,000; Full time

Benefits offered by this job

Equity in company equity

Medical, dental, vision coverage

Generous PTO and holidays

Job summary

Lila Sciences is seeking a Staff/Principal DevOps Engineer for AI Inference in Cambridge, MA. You will design and optimize GPU-accelerated infrastructure for scalable ML model serving, spanning Kubernetes clusters, Terraform/Helm deployments, and multi-region pipelines.

You will collaborate with ML researchers, software engineers, and data scientists to build reliable inference platforms with maximum GPU utilization and minimal latency.

Qualifications

• Expertise in DevOps/SRE/Platform Engineering for GPU/accelerator infra at scale.
• Deep experience with Kubernetes for ML workloads and GPU scheduling.

Responsibilities

• Design and optimize GPU/accelerator infrastructure on Kubernetes and cloud platforms.
• Build model serving platforms with low latency and high throughput.
• Implement CI/CD pipelines and IaC for GPU-based EKS clusters.

Skills

DevOps

SRE

Platform Engineering

Kubernetes

Python

Tools

Terraform

Helm

AWS

Triton Inference Server

Job description

Staff/Principal DevOps Engineer, AI Inference

Cambridge, MA USA

The Staff/Principal DevOps Engineer - AI Inference will drive the design, implementation, and optimization of infrastructure purpose-built for serving machine learning models at scale. This role bridges platform engineering, site reliability, and ML infrastructure, building the systems that power low-latency, high-throughput inference across GPU clusters and cloud accelerators. You will collaborate with ML engineers, research scientists, and software engineers to build inference platforms that serve models reliably to production users while maximizing compute efficiency.

What You'll Be Building

• GPU/accelerator infrastructure on Kubernetes: scheduling, resource isolation, multi-tenant GPU sharing, device plugins, and topology-aware placement for inference workloads
• Model serving platforms using frameworks such as vLLM, Triton Inference Server, TGI, or custom serving stacks with optimized batching, caching, and request routing
• Intelligent request routing and load balancing across heterogeneous accelerator fleets (NVIDIA GPUs, AWS Inferentia/Trainium) to maximize utilization and minimize latency
• Autoscaling systems that dynamically match inference compute supply with demand across production, research, and experimental workloads
• Production-grade deployment pipelines for ML models: canary rollouts, A/B testing, model versioning, and safe rollback across multi-region deployments
• Infrastructure-as-code with Terraform and Helm for GPU-accelerated EKS clusters, including node pools, spot/on-demand strategies, and accelerator-specific networking
• Observability and performance optimization: GPU utilization monitoring, inference latency profiling, token throughput dashboards, and SLO/SLI tracking for model endpoints
• CI/CD pipelines for model artifacts: container image builds with CUDA/driver dependencies, model registry integration, and automated inference benchmarking in CI
• AWS cloud infrastructure for ML: EKS with GPU node groups, EC2 accelerated instances (P4/P5, Inf2, Trn1), S3 model storage, EFA/high-bandwidth networking, and IAM least privilege
• Cost optimization and capacity planning: right-sizing accelerator instances, spot instance strategies for inference, and fleet-wide efficiency reporting

What You'll Need to Succeed

• Expertise in DevOps, SRE, or Platform Engineering with significant experience operating GPU/accelerator infrastructure at scale
• Deep experience with Kubernetes for ML workloads: GPU scheduling, resource quotas, node affinity, and accelerator device management
• Strong proficiency deploying to AWS using infrastructure-as-code (Terraform, Helm) with hands-on experience managing GPU-based compute (EKS, EC2 P-series/Inf/Trn instances)
• Experience with model serving infrastructure: inference servers, request batching, KV-cache optimization, or LLM serving frameworks
• Strong understanding of networking for distributed inference: high-bandwidth interconnects, NCCL, VPC/PrivateLink, and load balancing at L4/L7
• Strong proficiency in Python for automation, tooling, and integration with ML frameworks

Bonus Points For

• Experience with LLM inference optimization: continuous batching, speculative decoding, quantization (GPTQ, AWQ, FP8), tensor parallelism, and pipeline parallelism
• Hands-on experience with multiple accelerator families (NVIDIA A100/H100, AWS Inferentia2, Trainium, AMD MI300X) and maintaining hardware-agnostic serving infrastructure
• Multi-region deployment experience with geographic routing and failover for latency-sensitive inference endpoints
• Proficiency in Rust or Go for performance-critical infrastructure components
• SRE practices for ML systems: chaos engineering on GPU workloads, incident management, capacity modeling for bursty inference traffic
• Experience with model registries, artifact versioning, and ML supply chain security
• Observability platform expertise: building custom metrics for token-level throughput, time-to-first-token, and per-request GPU memory profiling
• Prior startup/high-growth experience balancing velocity with reliability in rapidly scaling AI systems

Compensation

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.

U.S. Benefits.

Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.

International Benefits.

Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.

Expected Base Salary Range

$192,000 - $272,000 USD

About LILA

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.

LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.

Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

Lila Sciences iscommitted to equal employment opportunityregardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy .

A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.