[All remote jobs](https://jobicy.com/jobs.md)Open role[![Quilter logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/08/138e7ef9-221.png)](https://jobicy.com/company/quilter.md)Remote opportunity at[Quilter](https://jobicy.com/company/quilter.md) # Staff ML Systems Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/quilter.md)Share25 Aug 2026Published37Listing views0Application actions24 Sep 2026Apply before Opportunity details ## About this role. AI SummaryQuilter is seeking a senior-to-staff-level ML systems engineer to productionize machine learning for automated PCB component placement. The role owns the end-to-end ML platform, including training and data pipelines, evaluation, serving, monitoring, experiment management, orchestration, A/B testing, and CI/CD. The engineer will lead design reviews, identify architectural risks, and establish scalable engineering practices for a research-heavy team. Success requires substantial production ML experience, strong software and systems-design fundamentals, GPU workload expertise, and comfort operating independently in a distributed environment. This is a high-impact infrastructure role supporting optimization-oriented ML and long-range platform architecture. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a staff-level role requiring 7+ years of production ML systems experience and ownership across a broad, technically demanding lifecycle. The combination of research-to-production delivery, GPU orchestration, platform architecture, and process leadership makes the role highly complex. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianMarket rate$190,000US market range$175k–$240k0$264k AI insightThe disclosed annual base salary range is USD 180,000 to USD 200,000, producing a job median of USD 190,000. For a US-based Staff ML Systems Engineer, an estimated market base-salary range is USD 175,000 to USD 240,000 annually; total compensation may be higher when equity and other benefits are included. The offered range is competitive but sits below the upper end of the US staff-level ML infrastructure market. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning Infrastructure](https://jobicy.com/jobs?search_keywords=Machine%20Learning%20Infrastructure.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[ML Systems](https://jobicy.com/jobs?search_keywords=ML%20Systems.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Training Pipelines](https://jobicy.com/jobs?search_keywords=Training%20Pipelines.md)[Model Serving](https://jobicy.com/jobs?search_keywords=Model%20Serving.md)[Experiment Management](https://jobicy.com/jobs?search_keywords=Experiment%20Management.md)[GPU Orchestration](https://jobicy.com/jobs?search_keywords=GPU%20Orchestration.md)[CI/CD](https://jobicy.com/jobs?search_keywords=CICD.md)[Systems Design](https://jobicy.com/jobs?search_keywords=Systems%20Design.md) Sample interview questionsDescribe an ML platform you took from an early research workflow to reliable production operation.I would explain the initial research constraints, then describe how I standardized data contracts, versioned datasets and models, automated training and evaluation, implemented deployment gates, and added monitoring. I would quantify improvements such as reproducibility, deployment frequency, training reliability, inference latency, or incident reduction. How would you design reproducible training and experiment-management infrastructure for a research-heavy team? I would define immutable dataset and code versions, tracked configurations, environment pinning, artifact storage, lineage metadata, and a consistent experiment interface. I would make the compliant path easy through templates and orchestration so researchers can iterate quickly while results remain comparable and recoverable. What factors do you consider when designing GPU-accelerated training and serving infrastructure? I consider workload profiles, GPU utilization, scheduling and queueing policies, distributed-training needs, data locality, fault tolerance, cost controls, observability, and capacity planning. For serving, I also evaluate model loading, batching, latency objectives, autoscaling behavior, rollback procedures, and hardware compatibility. How do you identify and communicate architectural risks during a design review? I begin by clarifying requirements, assumptions, scale expectations, and failure modes, then assess tradeoffs around reliability, security, maintainability, cost, and delivery speed. I document risks with their impact and likelihood, propose practical mitigations, and facilitate alignment on a decision that can be revisited as evidence changes. How would you evaluate whether a new model or placement-system change should be released through an A/B test? I would define the hypothesis, eligible traffic or workload segmentation, primary quality metrics, safety guardrails, sample-size requirements, and rollback criteria before launch. I would monitor both model performance and system health, analyze results for statistically and operationally meaningful effects, and promote only when the change meets pre-agreed thresholds. ### About Quilter At Quilter, we are helping electrical engineers save time and accomplish more by automating the tedious and time-consuming task of designing printed circuit boards (PCBs). Our small team is composed of experts in electrical engineering, electromagnetic simulation, ML/AI, and high-performance computing (HPC). We are inventing and leveraging novel techniques to solve the decades-old problem of automating circuit board design where today hundreds of billions of dollars are spent. We have raised $25 million in Series B funding from some of the very best and are charging full-speed toward our goal. No matter where we come from, we’re united by a common vision for the future and a core set of values we think will get us there: * Focus on the mission * Build great things that help humans * Demonstrate grit * Never stop learning * Pursue excellence We’re looking for a Senior or Staff ML Systems Engineer to join Quilter’s Placer Team and build the infrastructure that moves research from prototype to production reliably and efficiently. ### The Role The Placer is responsible for automated component placement on PCBs. This role focuses on the systems and infrastructure that support the full ML lifecycle: training pipelines, data generation and cleaning, experiment management, orchestration, serving, A/B testing, and CI/CD. You’ll be building the infrastructure that moves research from prototype to production reliably and efficiently. You’ll also play a key role in systems design review and long-range architectural planning as the team scales. This is a fully distributed team. We expect high autonomy and high ownership. ### What Youʼll Do * Design, build, and maintain ML infrastructure across training, evaluation, serving, and monitoring * Own data pipelines including generation, cleaning, validation, and versioning * Build and improve experiment tracking, orchestration, and reproducibility tooling * Implement and maintain CI/CD pipelines and A/B testing infrastructure * Lead and formalize design review processes across the team * Identify architectural risks early and guide the team toward sustainable systems decisions ### What Weʼre Looking For * 7+ years of industry experience building and operating ML systems in production * Proven track record as a key player in the success of a production-grade ML system end-to-end * Deep familiarity with training pipelines, serving infrastructure, and experiment management * Strong software engineering fundamentals and systems design sensibility * Experience driving design reviews and improving engineering processes within a team * Comfort operating with high autonomy in ambiguous problem spaces * Experience with GPU-accelerated workloads and orchestration * Strong communication and collaboration skills ### Preferred * 7+ years of industry experience * Familiarity with ML workflows involving optimization, RL, or combinatorial problems * Experience building infrastructure for small, research-heavy teams Please note: We are an equal opportunity employer. At this time, we are focused on hiring primarily within the US, with occasional exception to accommodate exceptional talent. ### What we offer: * Interesting and challenging work * Competitive salary and equity benefits * Health, dental, and vision insurance * Regular team events and offsites (~4x / year) * Unlimited paid time off * Paid parental leave Want to learn more about Quilter, our vision, and our investors? Visit our [About](http://quilter.ai/about) page and visit our [Blog](https://quilter.ai/blog). Show more [Apply now >](https://jobicy.com/jobs/151571-staff-ml-systems-engineer.md) * ![Upload CV](data:image/svg+xml;base64,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) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content. Next step ## Apply now. Follow the employer’s application method and review Jobicy’s safety guidance before sharing personal information. Keep exploring ## Related remote jobs. Matched by job category10 related opportunities[Software Engineering](https://jobicy.com/categories/engineering.md) [Browse all jobs](https://jobicy.com/jobs.md) * ![Ashby logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/08/63864ee6-221.png) Ashby Aug 25 ### [Engineering Manager – Americas](https://jobicy.com/jobs/151638-engineering-manager-americas.md) Hi 👋🏾, I’m Abhik, Ashby’s Co-Founder and VP of Engineering. 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