Machine Learning Engineer

Responsibilities

• Build, validate, and ship applied ML models that require real ML judgment, including channel rankers using learning-to-rank and gradient-boosted approaches, peer-network embeddings and similarity, statistical normalization of noisy LLM outputs, and confidence calibration; deploy them into production behind clean, maintainable contracts
• Integrate ML models and LLM-based components into decoupled, asynchronous pipelines and multi-agent graphs, ensuring they are reliable, observable, and safe under real concurrent production load
• Stand up evaluation, monitoring, and data-quality discipline that confirms whether a model is actually working in production, including offline metrics that predict online behavior, drift detection, and reconciliation of projected vs. real impact
• Build fast, reproducible data pipelines in the cloud by reflex, reaching for managed cloud services over one-off scripts, automating data movement, cleaning, and preparation so that the data layer never becomes the bottleneck to MLdelivery velocity
• Decide when not to use ML and defend the simpler answer; reach for the simplest approach that will work best—a rules-based scorer before a trained ranker, a clean baseline before a complex model—and earn the right to add complexity with evidence
• Ship thin first versions into real environments fast, measure against reality, and iterate in tight loops; prioritize putting something narrow in front of real data or real users early rather than waiting for a complete spec
• Own ML-powered systems and products end to end—from problem framing through production and operation—including when models drift, when data is dirty, and when latency spikes
• Design for failure before success: identify silent-failure modes, build guardrails, and treat the risk register as your responsibility, including prompt-injection paths on live data and bad recommendations before clients see them
• Set technical direction and raise the delivery bar for others on the team; make the people around you better through mentorship, code quality, and sound architectural judgment
• Iterate with the broader organization as the team moves from prototypes to products, building delivery momentum through frequent shipping and learning from real usage

Qualifications

Minimum Qualifications

• 5+ years of experience in applied machine learning or a closely related engineering discipline
• Demonstrated, production-proven ML depth: able to reason from a commercial problem to an appropriate MLformulation, choose and justify the method, and explain how you would know it is working, including the failure modes such as leakage, drift, and offline/online metric divergence
• Track record of shipping ML to production—models that served real traffic and real clients, not only notebooks, papers, or proofs of concept
• End-to-end ownership of at least one meaningful ML-powered system or product, from problem framing through production and ongoing operation
• Proficiency in Python and the standard ML/data stack, including LLM/agent orchestration and RAG, vector databases, and AWS cloud infrastructure
• Demonstrated ability to build fast, reproducible data pipelines using managed cloud services, with a consistent pattern of automating data movement and preparation rather than hand-rolling one-off scripts
• Strong software-engineering fundamentals sufficient to write production-quality code and own a service end to end, not only throwaway scripts or exploratory notebooks
• Comfort starting in ambiguity without a detailed spec, and a visible bias toward iterative delivery over big-bang completion
• Experience using AI tools (ChatGPT, Copilot, Claude, etc.) to accelerate and elevate your outcomes; including but not limited to communication drafting, data analysis, prompt engineering, and/or documentation

Preferred Qualifications

• Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, Statistics, or a related field
• Experience shipping a recommender, ranker, forecasting, or similar applied-ML product that demonstrably moved a business metric
• Retail, e-commerce, or marketplace domain experience
• Experience making LLM/agentic systems reliable and safe in production, including guardrails, evaluation suites, and monitoring, beyond prototyping
• AWS-native, event-driven or serverless architecture experience (the platform these products run against is AWS, TypeScript, and streaming/event-driven)
• Experience founding, co-founding, or joining a product company early, demonstrating ownership of outcomes rather than only models
• Strong communication skills—able to explain ML trade-offs, failure modes, and design decisions clearly to technical and non-technical stakeholders
• Low ego and high agency, you ask for help fast rather than spinning, makes colleagues better, and treats data-quality work and pipeline plumbing as core to doing ML well