Machine Learning Operations Engineer IIS&P Global • Cambridge, Massachusetts, United States

Apply Now

Machine Learning Operations Engineer II

S&P Global

Cambridge, Massachusetts, United States

Cambridge, Massachusetts, United States

Apply Now

About

About Kensho Kensho is S&P Global’s hub for AI innovation and transformation. We develop and deploy novel solutions to innovate and drive progress at S&P Global and its customers worldwide, focusing on business and financial generative AI applications, agents, data retrieval APIs, data extraction, and more. Our MLOps team is the de‑facto ML platform team, working at the intersection of infrastructure and ML to empower engineers with state‑of‑the‑art processes, tooling, and infrastructure. What You’ll Do Iterate on Kensho’s ML processes to develop tools, services, and frameworks that make every stage of the ML workflow robust, auditable, and usable. Work closely with ML engineers to understand their unique processes, identify pain points, and form effective solutions. Empower engineers with stable tooling to rapidly experiment and actualize research into demonstrable prototypes and mature products. Provide resources and training for ML teams on best practices, enabling efficient productionization of their work. Evaluate, select, and champion open‑source and third‑party solutions, driving their adoption across teams and integrating them into Kensho’s platform ecosystem. Ship scalable, efficient, and automated processes for model fine‑tuning, reinforcement learning, and evaluation of LLMs/Agents. Improve LLM and agentic observability to monitor agentic applications in production, detecting performance, decay, and drift issues. Stay at the frontier by tracking emerging tools and frameworks, promoting best practices, and strengthening the technical expertise of the team. What You’ll Need 2+ years of experience in ML infra, ML Ops, ML Engineering, or a similar skill set. Experience managing distributed systems with Kubernetes (understanding concepts and trade‑offs). Cloud Platform (AWS) knowledge, including EKS and managed ML services such as Bedrock and SageMaker. Python proficiency (we are a Python shop). Familiarity with distributed computing frameworks and workflow orchestration (e.g., Ray, Airflow). Understanding of 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. A curious, driven, low‑ego mindset eager to learn across a range of engineering disciplines. Technologies & Tools We Use Development: Python, Bash, LangGraph, PyTorch Infrastructure: Ray, Amazon EKS, Airflow, Jsonnet, Terraform Ops: Git, GitHub, AWS, LangFuse, Sentry, Prometheus, W&B Benefits Base salary range: $130,000–$175,000, plus annual incentive bonus and equity plans. Medical, Dental, and Vision insurance (100% company‑paid premiums). Unlimited Paid Time Off. 26 weeks of fully 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, plus up to $4,000 per year for 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. Equal Opportunity Kensho is an equal opportunity employer that welcomes future Kenshins with all experiences and perspectives. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin.
#J-18808-Ljbffr

Cambridge, Massachusetts, United States

Languages

English

Notice for Users

This job comes from a TieTalent partner platform. Click "Apply Now" to submit your application directly on their site.

Apply Now