Senior AI/ML Engineer

Gesture • New York, NY

60d • Hybrid

About The Position

Gesture is a fast-growing tech company using AI, machine learning, and intelligent logistics to power a first-of-its-kind platform that connects people and brands through real-world, tangible experiences. We are investing aggressively in AI-driven intelligence to power the next generation of human activation and behavioral commerce. Our roadmap focuses on building intelligence that anticipates, adapts, and compounds -- predicting intent, scoring behavior in real time, and delivering deeply personalized experiences driven by real-world signal, all through systems that continuously learn and improve with every interaction. This is not surface-level automation; we are building intelligence directly into the core of how Gesture operates. Gesture is a late-stage, venture-backed technology company in full hyper-scale mode. We are past the experimental stage. The product works, the market is real, and the Intelligence Engine is what powers our differentiation. We need an engineer who can build, ship, and own AI and ML systems that perform in production. The Senior AI/ML & Data Engineer is the technical backbone of the Intelligence Engine, sitting at the intersection of data science, machine learning, and software engineering -- designing the systems that score behavior, surface insight, and activate outcomes across every surface of the Gesture platform. This is not a place for researchers who hand off to 'implementation teams,' nor for engineers who prototype endlessly without shipping. There are no layers to hide behind, no slow approval chains, and no tolerance for the gap between what models do in testing and what they do in production. If your instinct when a model underperforms is to document it in a ticket, stop reading here. If your instinct is to fix it, keep going.

Requirements

• 5+ years of hands-on ML engineering or applied AI experience with models running in production, not just in research or evaluation.
• Personally owned the full ML lifecycle: data, features, training, deployment, monitoring, and retraining.
• Proficient in Python, SQL and have production experience with ML frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost, or equivalent).
• Designed and deployed LLM-powered features: RAG pipelines, fine-tuning workflows, vector embeddings, or semantic search in production.
• Understand data modeling, feature engineering, and pipeline design for both real-time and batch inference at scale.
• Experience with ML orchestration tools (Airflow, Prefect, Kubeflow, or similar).
• Built and maintained production data pipelines from external sources - not just consumed them; handled API integration failures, schema drift, and data quality issues upstream of any model.
• Experience with event-driven pipeline architecture: Pub/Sub, Dataflow, or equivalent.
• Write clean, production-grade code -- not prototype code that needs to be rewritten before it ships.
• Comfortable with cloud-native ML infrastructure (GCP preferred: Vertex AI, BigQuery, Cloud Run).
• Designing and building scalable systems for model development, training, and deployment, managing large-scale data pipelines and distributed compute environments.
• Can make model architecture decisions under ambiguity and course-correct fast when the data tells you to.
• Communicate technical concepts to non-technical stakeholders without losing accuracy.

Nice To Haves

• Experience building behavioral analytics, engagement scoring, or personalization engines in a consumer or B2B product context.
• Familiarity with vector databases (Pinecone, Weaviate, pgvector) and embedding-based retrieval workflows.
• Background in experiment design, multi-armed bandits, or causal inference for production decision systems.
• Exposure to mobile-first AI systems or consumer product intelligence at scale.
• Comfort operating lean -- resourceful and inventive when the infrastructure isn't perfect and the playbook doesn't exist yet.
• Experience scaling an ML function and mentoring engineers as the team grows.

Responsibilities

• Design, build, and continuously improve the behavioral scoring system that powers Gesture's core activation model across B2C and B2B.
• Own signal selection, feature engineering, and model architecture decisions for all scoring use cases.
• Define and enforce evaluation standards for every model that touches a user-facing surface.
• Build the feedback loops that keep models improving as behavior data compounds.
• Own the full ML lifecycle from raw data to deployed inference: ingestion, transformation, training, evaluation, deployment, and monitoring.
• Design pipeline architecture for both real-time and batch inference use cases.
• Build and maintain the feature store, experiment tracking, and model registry that make the ML organization productive at scale.
• Partner with engineering to ensure ML infrastructure is production-grade, not prototype-grade.
• Integrate and optimize large language models for contextual inference, personalization, and behavioral pattern detection across Gesture's platform.
• Evaluate and implement retrieval-augmented generation, fine-tuning, and embedding workflows based on product requirements.
• Make rigorous tradeoff decisions on model selection: cost, latency, accuracy, and reliability in production.
• Build and maintain vector search and semantic retrieval capabilities that power personalization at scale.
• Design and execute experiments that produce statistically valid, business-relevant results.
• Own A/B testing infrastructure and experiment design standards across the ML team.
• Develop causal inference and multi-armed bandit frameworks where appropriate to accelerate learning velocity.
• Translate experiment results into model improvements, product decisions, and actionable recommendations for leadership.
• Build and maintain model monitoring dashboards that surface degradation, drift, and anomalies before they become product issues.
• Own bias auditing and fairness evaluation across all scoring and classification systems.
• Maintain documentation, versioning, and rollback capability for all production models.
• Establish and enforce model quality standards across the team as the ML function scales.
• Translate business problems into machine learning problem definitions with clear success criteria.
• Communicate model behavior, performance, and limitations to non-technical stakeholders without losing precision.
• Partner with product and engineering to sequence ML work against business priorities.
• Build the technical foundation that allows a growing ML team to operate without creating chaos.

Benefits

• Health, dental, vision
• Equity and stock options
• Professional development budget
• Make a Gesture Day: Each employee receives the chance to earn credit to send a surprise gift to someone they admire
• Gesture Swag Pack: Exclusive branded gear (hoodie, water bottle, or cap)
• Beautiful HQ Experience: Modern New York office with creative work zones, media studio, and collaboration spaces
• Paid Team Lunches or Coffee Hours: Casual networking with leadership and cross-department peers

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed