Machine Learning Engineer developing consumer - facing AI features powered by LLMs at Quora. Collaborating with engineers and optimizing production AI systems for enhanced user engagement.
About the role
- Lead AI/ML & MLOps Engineer executing projects from data foundations to model deployment. Collaborating with sales to drive AI/ML engagements for our clients.
Responsibilities
- Delivery and technical leadership
- Lead the architecture and hands-on implementation of end-to-end ML systems: data ingestion, pipelines, feature stores, training, evaluation, serving, and monitoring
- Own technical decisions across the full stack, data platform, training environment, model serving, and MLOps tooling
- Set engineering standards for ML projects: experiment tracking, model versioning, reproducibility, governance, observability, drift monitoring, and CI/CD for ML
- Coach and uplift other engineers on the team in modern ML and MLOps practices
- Stay accountable for quality, security, and operational soundness of what we ship
- Pre-Sales and pipeline support
- Partner with the sales leadership team across pre-sales activity: discovery calls, scoping workshops, technical briefings, and LOE preparation
- Lead architecture and solutioning conversations with prospects and customers, translate business problems into credible, defensible technical approaches
- Provide dedicated technical support to opportunities flowing through the partners sales process, including positioning their products as part of broader data and AI architectures, joint solutioning sessions, and partner-aligned proposals
- Contribute to thought leadership and demand generation: blog posts, webinars, capability decks, conference talks, and reference architectures
Requirements
- Machine Learning fundamentals
- Strong grounding in the full ML lifecycle: data pipeline creation, feature engineering, model training, evaluation, deployment, and monitoring
- Production experience designing and building data pipelines that feed ML workloads (batch and streaming)
- Solid hands-on understanding of model training: hyperparameter tuning, validation strategies, dealing with class imbalance, leakage, common failure modes
- Ability to select appropriate model families (classical ML, deep learning, large language models) for the problem at hand and justify the choice
- Hands-on production experience with the core MLOps building blocks: Model registry and model versioning Experiment tracking and reproducibility Training pipelines and orchestration CI/CD for ML (model and data) Model serving (online, batch, streaming) Model observability, performance, drift, data quality, and operational metrics Governance, lineage, and access control
- Experience with at least one major MLOps / experiment platform, for example MLflow, Weights & Biases, Vertex AI, SageMaker, Azure ML, or Databricks, is required. Cross-platform experience is preferred
- Production experience building and operating ML systems on at least one major cloud: GCP, AWS, or Azure
- Strong comfort with the data and AI services on that cloud (e.g. BigQuery / Vertex AI, Redshift / SageMaker, Synapse / Azure ML)
- Cross-cloud experience and the ability to make pragmatic platform recommendations is a strong plus
- Practical experience with model explainability techniques: SHAP, LIME, feature attribution, partial dependence, model cards
- Familiarity with responsible AI practices: bias evaluation, fairness, calibration, uncertainty quantification, and confidence-aware UX patterns (e.g. withholding low-confidence predictions)
- Awareness of what it takes to make a model trustworthy in regulated or high-stakes domains
- Hands-on experience designing and shipping agentic AI solutions in production or production-adjacent settings
- Strong understanding of common agent design patterns, ReAct, plan-and-execute, tool use, reflection, multi-agent orchestration, human-in-the-loop
- Working experience with one or more agent frameworks (e.g. LangChain / LangGraph, LlamaIndex, CrewAI, etc.) and vector databases
- Strong working knowledge of modern data platforms, relational, NoSQL, warehouse, and lakehouse.
- MongoDB experience (Atlas, Atlas Vector Search, change streams, schema design for analytical and AI workloads) is highly valued
- Familiarity with BigQuery, Snowflake, and Databricks is a plus
- Comfortable in a consulting setting: multiple concurrent engagements, ambiguity, scoping under time pressure, and frequent client interaction
- Strong written and verbal communication, able to hold a technical conversation with a CTO and explain a model decision to a non-technical or business stakeholder in the same hour
- Prior experience supporting pre-sales activity (scoping, technical proposals) is strongly preferred
- Comfortable being on camera and in the room with prospects and partners.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
Report this job
Found something wrong with the page? Please let us know by submitting a report below.
Staff Machine Learning Engineer on Merchant team at Pinterest. Leading ML initiatives to enhance merchant quality and improve user shopping experiences.
Machine Learning Engineer at Sumsub building ML solutions combating fraud and identity theft. Work with global teams in a fully remote setting allowing product autonomy.
Associate Deep Learning Engineer implementing deep learning and computer vision algorithms at NBCUniversal. Collaborating with cofounders to meet technical milestones and deploying code on geospatial datasets.
Staff Product Manager leading Vegetation Intelligence at Overstory, harnessing machine learning to enhance utility vegetation management. Overseeing product strategy and development for critical vegetation insights.
Machine Learning Engineering Intern developing innovative AI solutions for contact centers at Cresta. Collaborating with top researchers and engineers in AI to unlock customer experience potential.
MLOps/LLMOps Architect designing AI solutions for a next - gen AI platform in Canada. Collaborating with teams on architecture, deployment, and governance in AI environments.
Staff Deep Learning Engineer at NBCUniversal implementing core algorithms for 2D and 3D media datasets. Collaborating with cofounders and deploying large - scale algorithms for high - fidelity content.
Deep Learning Engineer at NBCUniversal implementing algorithms in computer vision and graphics. Transforming high dimensional data into high - fidelity content using advanced techniques.
Staff MLOps Engineer responsible for building infrastructure for AI/ML at NBCUniversal. Managing large media datasets, deploying models, and automating data pipelines.