Lead ML 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 ML delivery 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
6+ 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 ML formulation, 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

Work arrangement
Hybrid