Role Description The Hartford is seeking a Staff AI Machine Learning Engineer to build Machine Learning Operations (MLOps) services for the Middle and Large Business Applied AI team. You will play a critical role in designing, building, and operationalizing production-grade AI and ML solutions, partnering closely with product, engineering, and platform leaders to deliver measurable impact. - Research, experiment with, and implement suitable Generative AI and ML algorithms, tools, and technologies. - Participate in identifying and assessing opportunities, i.e., value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage. - Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations. - Accountable for deployment design, development, and maintenance of both traditional ML and AI models. - Work with junior engineers and peers to provide mentorship and thought leadership. Be comfortable presenting new concepts to technical audiences. - Collaborate with partners in Enterprise Data, Applied AI, Business, Cloud Enablement Team, and Enterprise Architecture teams. - Delivery of critical milestones for model deployment in the AWS and GCP clouds. - Adopt and promote MLOps best practices to the Data Science community. Qualifications - Bachelor’s degree in related field or 7+ years of equivalent experience in a Research, Engineering, Data Science, or DevOps Role. - Development experience developing solutions within AWS, GCP, or both. - Experience developing repeatable architectural patterns; ability to identify redundancies and eliminate them with these patterns. - Experience building and deploying API services within the Cloud. - Experience building CICD pipelines. - Experience with IAC (Infrastructure as Code) including Cloud Formation, Terraform, or equivalents. - Experience in Unix, git, and strong object-oriented development experience using Python. - Experience in end-to-end model development lifecycle, from ideation through post-production monitoring. - Experience with workflow automation platforms (Apache Airflow, Autosys, similar). - Basic understanding of Data Science model development life cycle. - Familiarity with emerging data-centric technologies such as generative AI, Agentic workflows, and embedding LLMs into automated processes. Requirements - Must be authorized to work in the U.S. now and in the future. - Candidate must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position. Benefits - The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency, and demonstration of competencies required for the role. - The annualized base pay range for this role is: $125,760 - $188,640. - Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition.

• Develop and maintain machine learning models that are integral to our production decision-making system and directly influencing business outcomes. • Adopt or build new technologies for training and serving ML models (e.g. support large models, increase developer velocity, etc) • Monitor the latest ML research for functional ideas that the team could try. • Optimize ML Pipelines – Build and scale efficient pipelines for real-time and batch processing. • Model Monitoring & Improvement – Track performance, detect drift, and automate retraining. • Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences • Collaborate with a team of world-class engineers with diverse backgrounds. • Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and code review.

Role Description Block lends, moves money, and screens for financial crime at enormous scale, and one bad model can mean millions in credit losses, suspicious activity that goes unreported, or a fair lending violation. Model Risk Management is the independent function that decides whether a model is sound enough to put in front of customers and regulators. The failures that matter rarely announce themselves: - A model can clear every headline metric and still be broken underneath. - It can pass clean at launch and then quietly drift as the population shifts, until the loss it was supposed to prevent surfaces months later. The hard part is finding what looks right and is wrong, then proving it well enough to hold up under questioning. Much of the work arrives under-specified, so you scope it into a defensible plan, ask the questions that surface the real requirements, and defend your tradeoffs to the people who built the model you are challenging. The same scrutiny you apply to models applies to AI. We build the tooling that lets a lean team validate at scale, so you critically evaluate what it produces and own the evaluation that confirms its output is reliable enough to act on. That work matters most for the GenAI and agentic systems most teams have not figured out how to oversee yet. As a senior individual contributor, you lead through technical depth and cross-team scope, and you partner widely across the organization. You work with the first-line modelers you challenge, the Legal, Compliance, and fair-lending teams who rely on your analysis, and the auditors and bank partners who carry it into regulatory engagements. This role is remote-friendly within approved US locations. You Will - Independently challenge model owners across lending, fraud, and AML: reproduce their results, set and defend the acceptance thresholds, and own the call on whether a model is sound. - Hunt the silent errors that make metrics lie, and prove them out before they reach production. - Choose evaluation that holds up under real conditions: rare events, shifting populations, and drift that only shows up after launch. - Work hands-on in codebases you did not write, learning the data, configs, and conventions, and ship production code in the tooling you build to validate them. - Build the agentic validation tooling the team depends on, orchestrating agents that run in parallel. - Reason about ML systems end to end — how features, training, serving, monitoring, and scale fit together — to evaluate and challenge an owner's design. - Tie explainability and fair-lending findings on consumer credit models back to the model and product decisions that follow. - Help define how Block validates the systems at the frontier of production AI, setting standards where none exist yet. Qualifications - A quantitative degree or equivalent experience, and senior-IC depth building or validating models in a high-stakes domain such as credit, fraud, or financial crime. - Command of effective-challenge methodology: reproduction, conceptual-soundness review, benchmarking, stress testing, and outcomes analysis, with an eye for how a model holds up after launch and where its assumptions break. - Deep applied ML and statistics across model families, from regression and tree ensembles to deep learning, with sound judgment about evaluation, calibration, and generalization. - Experimentation and statistical rigor: holdout and experiment design, reasoning about uncertainty, and evaluating a model beyond aggregate accuracy. - Solid software and data engineering: production-quality Python, SQL on large datasets, and reproducible, tested code. - Fluency with modern AI: building with LLMs and agentic tools, and the judgment to know when their output can be trusted. - Familiarity with model risk management frameworks and fair-lending standards, with the specifics learnable on the job. - The communication to explain and defend your conclusions to model owners and senior stakeholders, and the independence to operate under ambiguity. Technologies We Use and Teach - Python (NumPy, Pandas, scikit-learn, LightGBM, XGBoost, PyTorch) - AI dev tools: Claude Code, Cursor, Copilot; agent skills and frameworks for building LLM-powered tooling - MLflow / Databricks; Prefect on GCP Vertex AI - Snowflake and cloud object storage - GitHub and CI (ruff, pytest) - Jira and Linear - GCP and AWS Benefits - Remote work - Medical insurance - Flexible time off - Retirement savings plans - Modern family planning

• Contribute to advancing Reddit's growth by designing and implementing content discovery algorithms that prioritize a seamless and highly personalized user experience. • Deeply understand the Reddit Notifications product and drive the vision for the notifications relevance team. • Enhance core recommendation capabilities, including candidate retrieval, ranking models, and budgeting optimization, while designing and testing new pipeline components. You will also deploy ML models, integrate LLMs, and ensure robust monitoring and smooth product integration throughout the process. • Serve as the primary ML domain expert, deploying state-of-the-art models at scale, driving architectural decisions, and ensuring robust monitoring and smooth product integration across the engineering organization. • Collaborate across disciplines and with ML, Product, Infrastructure, and DS teams at Reddit to find technical solutions to complex challenges. • Mentor and guide senior and staff engineers in the team. • Partner closely with senior leadership and cross-functional org leads to shape long-term roadmaps, balancing immediate operational wins with strategic technical objectives.