Our Mission:
6sense's mission is to multiply what matters: growth, retention, and efficiency. We envision a future where companies, teams and people reach their full potential.
Our People:
People are the heart and soul of 6sense. We serve with passion and purpose. We live by our Being 6sense values of Win as One Team, Stay Curious, Do The Right Thing, Own the Outcome, and Create Belonging. Every 6sensor plays a part in defining the future of our industry-leading technology. 6sense is a place where difference-makers roll up their sleeves, take risks, act with integrity, and measure success by the value we create for our customers. We want 6sense to be the best chapter of your career.
About the Role:
We’re hiring a Sr. Machine Learning Engineer to join the ML Engineering team. Our team builds the production systems that make 6sense’s machine learning dependable at massive scale: model-training and refresh pipelines, evaluation and release controls, high-throughput batch and online inference, and the shared platforms that let Data Science and product teams ship safely.
This is a hands-on engineering role for someone who enjoys owning the hard middle between a strong model and a reliable customer capability. You will work closely with Data Scientists, platform engineers, and product teams to turn experimentation into governed, observable, scalable production systems. You will help shape how models and AI agents are evaluated, promoted, monitored, and improved—not simply deploy them once.
What You’ll Do :
- Own production ML capabilities end to end: turn a business or modeling need into a well-designed pipeline or service, then operate and improve it in production.
- Build and evolve scalable training, model-refresh, feature/data-validation, and inference workflows across batch and real-time use cases.
- Design reliable model lifecycle controls: experiment tracking, evaluation gates, model/version promotion, rollback, lineage, reproducibility, and monitoring.
- Build platform primitives that enable Data Science and AI product teams to ship faster without compromising reliability, security, or cost.
- Improve the performance, resilience, and observability of distributed ML workloads and model-serving systems.
- Partner with Data Science on evaluation design, data quality, model-health signals, and production debugging.
- Contribute to LLM/agent evaluation and serving infrastructure where appropriate, including offline and online evaluation, tracing, quality gates, and regression detection.
- Lead technical design for ambiguous projects, influence architecture across teams, and mentor engineers through code reviews and hands-on guidance.
- Communicate decisions, risks, and operational status clearly to engineering, product, and leadership stakeholders.
What We’re Looking For :
Required :
- 6+ years of industry experience building and operating production machine-learning or data-intensive distributed systems, including substantial end-to-end ownership.
- Strong Python engineering skills and practical experience designing maintainable, testable services and pipelines.
- Demonstrated MLOps depth: experiment tracking, model registry/versioning, CI/CD, reproducible training, data/model validation, deployment strategies, rollback, and production monitoring.
- Experience with distributed data and ML infrastructure such as Spark, Ray, Databricks, Kubernetes, AWS, or equivalent platforms.
- Strong understanding of model-training and inference trade-offs: data quality, feature engineering, evaluation, latency/throughput, cost, reliability, and model drift.
- Experience productionizing at least one of: classical ML models, deep-learning/NLP models, embedding/retrieval systems, or LLM/agent workflows.
- Solid judgment in incident response and operational ownership; able to diagnose failures across data, model, infrastructure, and serving layers.
- Ability to translate ambiguous product and Data Science requirements into a pragmatic technical plan and drive it to completion.
- Clear written and verbal communication with both technical and non-technical partners.
Nice to Have :
- Hands-on experience with MLflow, Databricks, Ray, Kubernetes, Triton/managed model serving, or similar ML platform tooling.
- Experience operating high-volume batch scoring or low-latency online inference systems.
- Experience with LLM/agent evaluation frameworks, tracing/observability, RAG, vector search, LangGraph/LangSmith, or Amazon Bedrock.
- Experience with feature stores, data contracts, schema validation, and data-quality systems.
- Experience in B2B SaaS or a high-scale data platform where reliability and customer impact matter.
Our Benefits:
Full-time employees can take advantage of health coverage, paid parental leave, generous paid time-off and holidays, quarterly self-care days off, and stock options. We’ll make sure you have the equipment and support you need to work and connect with your teams, at home or in one of our offices.
We have a growth mindset culture that is represented in all that we do, from onboarding through to numerous learning and development initiatives including access to our LinkedIn Learning platform. Employee well-being is also top of mind for us. We host quarterly wellness education sessions to encourage self care and personal growth. From wellness days to ERG-hosted events, we celebrate and energize all 6sense employees and their backgrounds.
Equal Opportunity Employer:
6sense is an Equal Employment Opportunity and Affirmative Action Employers. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender perception or identity, national origin, age, marital status, protected veteran status, or disability status. If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to [email protected].
We are aware of recruiting impersonation attempts that are not affiliated with 6sense in any way. All email communications from 6sense will originate from the @6sense.com domain. We will not initially contact you via text message and will never request payments. If you are uncertain whether you have been contacted by an official 6sense employee, reach out to jobs@6sense.com
Skills Required
- 6+ years of industry experience building and operating production machine-learning or data-intensive distributed systems, with substantial end-to-end ownership
- Strong Python engineering skills and experience designing maintainable, testable services and pipelines
- MLOps experience including experiment tracking, model registry/versioning, CI/CD, reproducible training, data/model validation, deployment strategies, rollback, and production monitoring
- Experience with distributed data and ML infrastructure such as Spark, Ray, Databricks, Kubernetes, AWS, or equivalent platforms
- Strong understanding of model-training and inference trade-offs, including data quality, feature engineering, evaluation, latency, throughput, cost, reliability, and model drift
- Experience productionizing classical ML, deep-learning/NLP, embedding/retrieval, or LLM/agent workflows
- Incident response and operational ownership skills, including diagnosing failures across data, model, infrastructure, and serving layers
- Ability to translate ambiguous product and Data Science requirements into a pragmatic technical plan and complete it
- Clear written and verbal communication with technical and non-technical partners
- Hands-on experience with MLflow, Databricks, Ray, Kubernetes, Triton, managed model serving, or similar ML platform tooling
- Experience operating high-volume batch scoring or low-latency online inference systems
- Experience with LLM/agent evaluation frameworks, tracing/observability, RAG, vector search, LangGraph, LangSmith, or Amazon Bedrock
- Experience with feature stores, data contracts, schema validation, and data-quality systems
- Experience in B2B SaaS or a high-scale data platform where reliability and customer impact matter
6sense Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about 6sense and has not been reviewed or approved by 6sense.
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Fair & Transparent Compensation — Pay is considered strong overall, with widespread satisfaction in salary, benefits, and equity. Compensation is also positioned as comparatively competitive for multiple roles, including sales and engineering.
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Leave & Time Off Breadth — Time-off policies are described as flexible, including an unlimited-style vacation approach and expectations of being work-free while out. This breadth in paid time off is repeatedly positioned as a standout part of the overall package.
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Healthcare Strength — Health coverage is characterized as comprehensive, with dental and vision coverage called out as employer-paid in some descriptions. Wellness-related supports such as wellness days and stipends add to the perceived strength of the health-and-wellbeing offering.
6sense Insights
What We Do
The 6sense Account Engagement Platform helps revenue teams identify and close more opportunities by putting the power of AI, big data and machine learning behind every member of the B2B revenue team.
