Machine Learning Operations Engineer I

. Iterate on Kensho’s ML processes to develop robust, auditable, and usable tools, services, and frameworks across the ML workflow .

Posted 10/15/2026full-timeUnited StatesJunior💰 $120,000 - $140,000 per yearWebsite

Tools & technologies

AirflowAWSCloudDistributed SystemsKubernetesPythonRay

Key responsibilities & impact

Iterate on Kensho’s ML processes to develop robust, auditable, and usable tools, services, and frameworks across the ML workflow
Work closely with ML engineers to understand processes, identify pain points, and form effective solutions
Provide stable tooling that enables rapid experimentation and turns research into prototypes and mature products
Evaluate, select, champion, and integrate open-source and third-party solutions across Kensho’s platform ecosystem
Ship scalable, efficient, and automated processes for model fine-tuning, reinforcement learning, and LLM/agent evaluation
Improve LLM and agentic observability to monitor production applications and detect performance, decay, and drift issues
Track emerging tools and frameworks, promote best practices, and strengthen team technical expertise
Collaborate with ML Product, R&D, Core Infrastructure, SRE, and Security teams

What you’ll need

1+ years of experience in ML infra, ML Ops, ML Engineering or some similar skillset
Experience with Kubernetes
Understanding of cloud platforms, especially AWS
Experience with EKS and managed ML services such as Bedrock and SageMaker
Python proficiency
Familiarity with distributed computing frameworks and workflow orchestration such as Ray and Airflow is a big plus
Familiarity with software engineering best practices in an ML context
Basic understanding of ML concepts, LLMs and agents
Ability to debug distributed systems across infrastructure, networking and application layers
Excellent communication skills to drive adoption of new tools and best practices across multiple teams
Curiosity, drive, low ego, and eagerness to learn across engineering disciplines
Experience with agentic AI systems, tools, frameworks and workflows, MCP server patterns, and eval pipelines is advantageous

Comp & perks

Annual incentive bonus
Equity plans
Medical, Dental, and Vision insurance
100% company paid premiums
Unlimited Paid Time Off
26 weeks of 100% paid Parental Leave (paternity and maternity)
401(k) plan with 6% employer matching
Generous company matching on donations to non-profit charities
Up to $20,000 tuition assistance toward degree programs
Up to $4,000/year for ongoing professional education such as industry conferences
Plentiful snacks, drinks, and regularly catered lunches
Dog-friendly office (CAM office)
Bike sharing program memberships
Compassion leave and elder care leave
Mentoring and additional learning opportunities
Opportunity to expand professional network and participate in conferences and events