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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineering Lead - **Company:** Sightly Enterprises - **Location:** New York, NY, United States (Remote available) - **Experience:** Expert - **Salary:** $175,000.0 - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Artificial Intelligence, Artificial Neural Networks, Computer Vision, Acceptance Test-Driven Development, Big Data, Code Review, Continuous Integration, Information Engineering, Distributed Computing Environment, Python (Programming Language), PostgreSQL, Machine Learning, NumPy, Recommender Systems, Tensorflow, Pytorch, Large Language Models, Snowflake, Git, Fastapi, Pandas, Scikit Learn, HuggingFace, Machine Learning Operations, Docker - **Published:** June 5, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=b280fdac3d2c0e26 ## About the Role Do you have experience in Unsupervised learning?, * Strong foundation across classical ML, neural networks, and Transformers, reaching for the right tool rather than the trendiest one * Comfortable with both supervised and unsupervised paradigms: classification, regression, clustering, dimensionality reduction, representation learning * Practical fluency with NLP and at least working familiarity with computer vision for image and video enrichment * Understanding of when a simple model beats a complex one, and the discipline to ship the simple one Experimentation & Research Rigor * Track record of structuring and running experiments end-to-end: hypothesis, design, instrumentation, analysis, decision * Comfortable with ad hoc statistical testing, picking the right test for the task, reasoning about power, controlling for confounds * Knows the difference between a model that benchmarks well offline and one that holds up in production * Research mindset paired with a shipping mindset: rigorous, but allergic to research-for-its-own-sake Optimization * Experience building optimization systems, whether mathematical optimization, heuristics, or learned policies, applied to a real-world domain * Comfortable reasoning about objective functions, constraints, and tradeoffs in messy business contexts * Advertising or adtech optimization experience is a strong plus Production ML Engineering * Strong Python and the standard ML stack: scikit-learn, PyTorch, TensorFlow, HuggingFace, NumPy, pandas * FastAPI and async/await patterns for serving models and building ML-facing services * Experience working with data at scale, including the practical realities of billions of records: partitioning, sampling, distributed processing, cost management * GCP for training, serving, and infrastructure, such as Vertex AI, Cloud Run, GCS, or equivalent * PostgreSQL and Snowflake for working with large-scale data * Docker and CI/CD pipelines for reproducible, deployable ML workloads * Comfortable with the realities of production ML: data drift, retraining cadence, monitoring, cost management Leadership & Collaboration * Experience leading or mentoring engineers, even informally, through code review, technical direction, and raising the bar on quality * Strong collaboration habits with Data Engineering, and the ability to translate fluently between technical and business audiences * Can sit with an Account Manager or Performance Manager, understand what they actually need, and turn it into a tractable modeling problem Software Engineering Fundamentals * Clean code habits, sensible architecture, strong typing discipline * Test-driven mindset for ML code: covering data assumptions, edge cases, and regression paths, not just happy paths * Comfortable with modern dev practices: Git, code review, CI/CD, * Advertising, adtech, or media industry experience * Familiarity with LLMs and modern AI tooling, useful context for the broader engineering org but not the focus of this role * Causal inference or uplift modeling background * Experience with recommendation systems or ranking * 5+ years of ML experience, ideally with a foundation built before the LLM era ## Description * Multi-modal enrichment for image and video signals from social platforms, complementing our text-heavy core * Ad optimization systems built from the ground up, including bid optimization, budget allocation, creative selection, audience targeting, or related problems, grounded in historical performance data and well-reasoned heuristics * Experimentation design and execution: framing the question, choosing the right test, instrumenting it, and producing results the business can act on * Production ML infrastructure on GCP: training, evaluation, deployment, monitoring, and the glue that keeps models reliable as data shifts * Technical leadership for a small ML team, including code review, mentorship, prioritization, and raising the bar on rigor without slowing delivery * Cross-functional partnership with Data Engineering on pipeline integration, and with Account Management and Performance Managers to translate business problems into model problems ## Related Videos - [Navigating the AI Revolution in Software Development](https://www.wearedevelopers.com/videos/1266-navigating-the-ai-revolution-in-software-development) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Vectorize all the things! 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