Sr. ML Ops Engineer, Forward-Deployed

at 6sense

San Francisco, California, United States, senior, full time, USD 1,91,475–2,60,830 / year, 6+ years

posted 27 Oct 2026

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Airflow
S3
Hive
CI/CD
MLflow
FastAPI
Spark
AWS
Python
SQL
Kubernetes

About the role

Posted: 2026-10-27

Forward Deployed Engineering (FDE), Predictive track, in Data Science & AI Engineering

REPORTS TO MLOps engineering lead

LEVEL Senior (6+ years)

This is a hands-on MLOps engineering role. You will work across model training and refresh workflows, APIs, orchestration, data pipelines, production monitoring, and user-facing experiences such as model dashboards and training controls.

When a customer needs a better model, a refresh workflow, or visibility into model health, you will help make it real: build the experience, connect it to reliable backend systems, validate the outcome, and operate it safely in production.

You'll be our first MLOps engineer in the US, working closely with the MLOps team in India so customer work reaches production in days, not weeks. Most of your work is behind the scenes, with customer conversations when the technical details matter.

What you would do

Build for the customer

- 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 that make model health, score changes, data quality, refresh status, and production issues understandable to internal users and customers.

- Build or modify the APIs, backend services, data contracts, and orchestration workflows behind those experiences.

- Automate model retraining, validation, promotion, and deployment workflows for customer engagements.

- Create safeguards for data quality, score distributions, threshold changes, and edge cases before model changes reach production.

- Monitor models and services for drift, data shifts, reliability issues, and operational regressions.

Productize for everyone

- Turn repeatable customer solutions into reusable product and platform capabilities with Product, Data Science, and Platform Engineering.

Read from boards.greenhouse.io, last seen 2d ago.

What they are looking for

- 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, including 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.

What Success Looks Like

Within 3 months

- Understand the predictive-model lifecycle and improve at least one meaningful deployment, support, or visibility bottleneck.

- Bring clear release, monitoring, and support plans to active customer model engagements.

Within 6 months

- Common training, refresh, validation, and deployment workflows are materially more automated.

- Model dashboards or internal workflows provide clear visibility into model status, health, and refresh progress.

- Customer model changes move to production faster, with stronger operational controls.

Within 12 months

- At least one customer-specific solution has become a reusable product or platform capability.

- The team can reliably deliver and operate customer-tailored models without fragile manual processes or overnight handoffs.

Why this role is different: You won’t only maintain ML infrastructure. You’ll build the product experiences and production systems that make predictive models easier to launch, understand, refresh, and trust—then turn what works for one customer into something that benefits many.

Base Salary Range: $191,475.00 - $260,830.00 . The base salary range represents the anticipated low and high end of the base salary range for this position. Actual salaries may vary and may be above or below the range based on various factors, including but not limited to work location and experience. The base salary is one component of 6sense’s total compensation package for this position. Other compensation may include a bonus program or commission plan, and stock options if approved by 6sense’s board. In addition, 6sense provides a variety of benefits, including generous health insurance coverage, life, and disability insurance, a 401K employer matching program, paid holidays, self-care days, and paid time off (PTO). #Li-remote

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About the role

What you would do

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