Senior ML Ops Engineer – Forward-Deployed

🔥 0 minutes ago

🏄 California – Remote

🏄 California – Remote

💵 $191.5k - $260.8k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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This company has sponsored H1B visas in the past.

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6sense

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2013

💼 Consulting

📦 Logistics

🤖 Artificial Intelligence

💰 $200M Series E on 2022-01

Consulting • Logistics • Artificial Intelligence

6sense is a company specializing in Revenue AI™ solutions, particularly for B2B marketing and sales. The company offers a platform that identifies potential buyers who are ready to purchase, crafts personalized messages to engage them, and automatically qualifies and books meetings. 6sense uses advanced AI to predict buying stages, enrich contact and account data, and uncover buyer intent. Their platform integrates with existing technology investments and supports operations like account intelligence, audience building, and orchestration workflows. 6sense aims to improve marketing efficiency, increase pipeline, and boost revenue growth by revealing hidden buying signals and providing relevant data insights.

📋 Description

• Productionize customer-tailored predictive models, configurations, and scoring workflows with validation, release, monitoring, and rollback paths • Build and improve UI workflows for training triggers, model refreshes, validation outcomes, model status, and operational troubleshooting • Enhance model dashboards covering model health, score changes, data quality, refresh status, and production issues • Build or modify APIs, backend services, data contracts, and orchestration workflows • Automate model retraining, validation, promotion, and deployment workflows for customer engagements • Create safeguards for data quality, score distributions, threshold changes, and edge cases • Monitor models and services for drift, data shifts, reliability issues, and operational regressions • Turn repeatable customer solutions into reusable product and platform capabilities • Build deployment templates, reusable workflow components, and configuration-driven interfaces • Create self-service or assisted tools for training, refresh monitoring, validation review, and troubleshooting • Improve the MLOps platform, including Kubernetes workloads, compute, pipelines, CI/CD, deployment tooling, and observability • Establish runbooks, release checklists, audit trails, and support processes for model operations • Identify recurring customer pain points and convert them into product requirements, platform improvements, or operational tooling • Work closely with the MLOps team in India so customer work reaches production in days, not weeks • Report to the MLOps engineering lead

🎯 Requirements

• 6+ years of experience in MLOps, ML platform engineering, backend engineering, full-stack engineering, or related production engineering roles • Experience owning machine-learning systems in production, including training, deployment, monitoring, retraining, and incident response • Strong Python and SQL skills • Experience with APIs, backend services, asynchronous workflows, testing, CI/CD, and observability • Experience building or materially contributing to internal tools, operational dashboards, or customer-facing product workflows • Comfortable debugging across the stack: UI, APIs, workflows, data pipelines, model artifacts, and production infrastructure • Experience releasing changes safely across multiple customers or production environments, including staged rollout, impact checks, and rollback • Working knowledge of Kubernetes and cloud infrastructure • Strong communication and judgment when balancing customer urgency, usability, reliability, and long-term maintainability • Nice to have: Fast API and Frontend experience • Airflow, Spark, MLflow, model registries, feature pipelines, or workflow orchestration • AWS, S3, Hive, distributed compute, and multi-tenant SaaS experience • Forward-deployed, consulting, solutions, implementation, or customer-facing engineering experience • B2B SaaS, go-to-market data, or predictive modeling experience

🏖️ Benefits

• Health coverage • Paid parental leave • Generous paid time off and holidays • Quarterly self-care days off • Stock options • Equipment and support for working and connecting with teams • Learning and development initiatives • Access to LinkedIn Learning • Quarterly wellness education sessions • Wellness days • ERG-hosted events • 401K employer matching program • Life insurance • Disability insurance • Bonus program or commission plan may be included • Paid holidays

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