Machine Learning Operations Engineer I

Kensho•Cambridge, MA

1d•$120,000 - $140,000•Remote

About The Position

Kensho’s MLOps team serves as its machine-learning platform team, building processes, tools, and infrastructure that help ML engineers iterate quickly, build reliably, and identify production issues early. The team works across infrastructure and machine learning, collaborating with ML product and research teams as well as Core Infrastructure, SRE, and Security. Its work supports AI projects throughout Kensho and combines platform development with hands-on exploration of emerging ML and agentic technologies. The MLOps Engineer will develop and support a mature ML platform and internal tools that improve the developer experience and help engineers deliver high-quality, production-ready models, agents, and products. The position involves working with teams that have different products and workflows, evaluating and integrating tools into Kensho’s platform, and improving processes for model development and production monitoring. The role offers the opportunity to work from anywhere in the United States, with required travel to the Cambridge, Massachusetts, or New York City Kensho hubs for some team meetings or company events. The anticipated base salary range is $120,000–$140,000, and the role is eligible for an annual incentive bonus and equity plans. Compensation depends on the facts and circumstances of each case.

Requirements

At least 1 year of experience in ML infrastructure, MLOps, ML engineering, or a similar area.
Experience with Kubernetes.
Understanding of cloud platforms, particularly AWS.
Proficiency in Python.
Familiarity with software engineering best practices in an ML context.
Basic understanding of machine learning concepts, LLMs, and agents.
Ability to debug distributed systems across infrastructure, networking, and application layers.
Excellent communication skills and ability to drive adoption of tools and best practices across multiple teams.
Curiosity, drive, humility, and eagerness to learn across engineering disciplines.

Nice To Haves

Familiarity with distributed computing frameworks and workflow orchestration, such as Ray and Airflow.
Experience with agentic AI systems, tools, frameworks, and workflows.
Experience with MCP server patterns.
Experience with evaluation pipelines.
Experience with AWS tools including EKS, Bedrock, and SageMaker.

Responsibilities

Develop tools, services, and frameworks that make every stage of Kensho’s ML workflow robust, auditable, and usable.
Work with ML engineers to understand their processes, identify pain points, and develop effective solutions.
Provide stable tooling that enables engineers to experiment rapidly and turn research into demonstrable prototypes and mature products.
Evaluate, select, and promote open-source and third-party solutions, driving adoption across teams and integrating solutions into Kensho’s platform.
Build scalable, efficient, and automated processes for model fine-tuning, reinforcement learning, and LLM and agent evaluation.
Improve LLM and agent observability to monitor production applications and detect performance, decay, and drift issues.
Track emerging tools and frameworks, promote best practices, and strengthen the team’s technical expertise.

Benefits

Medical, dental, and vision insurance with 100% company-paid premiums.
Unlimited paid time off.
26 weeks of 100% paid parental leave.
401(k) plan with 6% employer matching.
Company matching on donations to nonprofit charities.
Up to $20,000 in tuition assistance toward degree programs, plus up to $4,000 per year for ongoing professional education such as industry conferences.
Snacks, drinks, and regularly catered lunches.
Dog-friendly Cambridge office.
Bike-sharing program memberships.
Compassion leave and elder care leave.
Mentoring and additional learning opportunities.
Opportunities to expand professional networks and participate in conferences and events.
Annual incentive bonus and equity plans.