Senior AI/ML Engineer

Department: Engineering \& Technology

Function: Artificial Intelligence, Machine Learning \& Data Science

Reports To: VP of Engineering

Direct Reports: Scope scales with organizational growth

Location: NYC \- Onsite / Remote / Hybrid] (Full\-Time)

Travel: As required

Employment: Full\-Time \| Exempt

ABOUT GESTURE

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Gesture is where technology meets humanity \- a place where innovation, emotion, and impact collide. We're 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. From our mobile app to our B2B Reach360 platform, Gesture blends data, emotion, and automation to build the future of human connection, at scale.

Inside our NYC headquarters, you'll find an environment that moves with the pace and precision of Silicon Valley but with the heart of something far greater.

We run on cutting\-edge tools, creative experimentation, and raw ambition. Every model you build, every signal you score, every system you deploy here matters \- because it's felt and experienced by real people around the world. At Gesture, you'll work alongside some of the smartest, most driven engineers and operators in the industry \- people who think big, move fast, and care deeply about the work they do.

This is a front\-row seat to the future of connection. If you want to help build something that's changing how the world interacts, welcome to Gesture.

WHERE WE ARE HEADED

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Gesture is 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: how users are activated, how campaigns are scored, how brands understand and reach people at the exact moment it matters.

At Gesture, you're not joining a company that bolts AI onto an existing product. You're joining a team building the Intelligence Engine \- our proprietary AI system \- from the ground up, as the core of everything we do.

THE OPPORTUNITY

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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.

What we need now is an engineer who can build, ship, and own AI and ML systems that perform in production \- not in a notebook, not in a pilot, and not behind a research wall. Someone who treats model quality the same way a revenue operator treats a missed number: personally.

The Senior AI/ML \& Data Engineer Senior AI/ML Engineer is the technical backbone of the Intelligence Engine. You sit 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.

Let's be direct about the environment: this is not a place for researchers who hand off to "implementation teams." It is not a place for engineers who prototype endlessly without shipping, who treat model accuracy as someone else's problem, or who need perfect data to start building. 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.

WHAT THIS ROLE ACTUALLY IS

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You will personally own:

• The Intelligence Engine \- Scoring \& Activation \- The behavioral scoring algorithms that power user\-facing activation across B2C and B2B; you own the logic, the lift, and the outcomes
• ML Pipeline Architecture \- The full lifecycle: data ingestion, feature stores, model training, evaluation, deployment, monitoring, and retraining triggers
• LLM Integration \& Optimization \- Integration, fine\-tuning, and production deployment of large language models for contextual inference, personalization, and behavioral pattern recognition
• Behavioral Signal Processing \- Extraction of meaningful features from structured and unstructured behavioral data; you define what signals matter and why
• Model Evaluation \& Governance \- Evaluation frameworks, A/B testing infrastructure, bias detection, and drift monitoring; model integrity in production is non\-negotiable
• Data Infrastructure \- Scalable pipeline and schema design that supports real\-time and batch inference, built in collaboration with engineering
• Research to Production \- The full path from experimentation to deployed, monitored, and maintained production systems; nothing lives in a notebook forever
• Cross\-Functional Technical Partnership \- Direct collaboration with product, engineering, and leadership to translate business outcomes into machine learning problem definitions

You will own the intelligence layer of the product. You will not "support" it.

WHAT THIS ROLE IS NOT

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Let's be clear:

• This is not a research role with no path to production
• This is not a data analyst position
• This is not a "build models and hand off to engineering" job
• This is not a role for engineers who treat deployment, monitoring, and retraining as someone else's problem
• This is not a role for people who need clean data, perfect infrastructure, or a fully staffed ML team to be effective

If you have never owned a model in production \- including when it breaks, drifts, or underperforms * \- this will be painful

WHAT YOU WILL BE HELD ACCOUNTABLE FOR

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You own:

• Model performance in production, not just in evaluation; lift, precision, recall, and business impact are your metrics
• End\-to\-end ML pipeline reliability: data in, scored output out, on time, every time
• Speed from experimentation to production; the faster a good idea gets to users, the better
• Signal quality and feature engineering that makes models better than the data suggests they should be
• Model governance: version control, drift detection, bias auditing, and documentation that holds up to scrutiny
• Infrastructure decisions that scale without requiring a rewrite every six months
• Cross\-functional clarity: engineering, product, and leadership should always understand what the models do, why they do it, and how to measure whether it's working
• The technical roadmap for the Intelligence Engine; where we are, where we're going, and what's blocking progress

When the Intelligence Engine performs and the product is smarter because of your work, you get credit. When model quality degrades and no one caught it, that falls on you.

WHAT YOU WILL OWN

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I. The Intelligence Engine \- Scoring \& Behavioral Intelligence

• 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

II. ML Pipeline \& Infrastructure

• Own the full ML lifecycle from raw data to deployed inference: ingestion, transformation, training, evaluation, deployment, and monitoring
• This includes owning the raw data layer \- you are not handed clean features. You build and maintain ETL/ELT pipelines from third\-party data sources (APIs, webhooks, flat files), handle auth, rate limits, and schema drift, and design event\-driven ingestion using Pub/Sub and Dataflow for real\-time signal processing.
• 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

III. LLM Integration \& Applied AI

• 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

IV. Data Science \& Experimentation

• 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

V. Model Governance \& Quality

• 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

VI. Cross\-Functional Technical Leadership

• 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

WHAT YOU MUST HAVE (NON\-NEGOTIABLE)

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Do not apply unless you can honestly say yes to all of the following:

• You have 5\+ years of hands\-on ML engineering or applied AI experience with models running in production, not just in research or evaluation
• You have personally owned the full ML lifecycle: data, features, training, deployment, monitoring, and retraining
• You are proficient in Python,SQL and have production experience with ML frameworks (PyTorch, TensorFlow, scikit\-learn, XGBoost, or equivalent)
• You have designed and deployed LLM\-powered features: RAG pipelines, fine\-tuning workflows, vector embeddings, or semantic search in production
• You understand data modeling, feature engineering, and pipeline design for both real\-time and batch inference at scale
• You have experience with ML orchestration tools (Airflow, Prefect, Kubeflow, or similar)
• You have built and maintained production data pipelines from external sources \- not just consumed them; you've handled API integration failures, schema drift, and data quality issues upstream of any model
• You have experience with event\-driven pipeline architecture: Pub/Sub, Dataflow, or equivalent

You write clean, production\-grade code * \- not prototype code that needs to be rewritten before it ships

• You are 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.
• You can make model architecture decisions under ambiguity and course\-correct fast when the data tells you to
• You communicate technical concepts to non\-technical stakeholders without losing accuracy

BONUS POINTS

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• 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

HOW YOU OPERATE

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Systems Thinking: You see the full stack, from raw signal to user\-facing outcome, and you build for that entire path \- not just your slice of it

Production Orientation: A model that doesn't work reliably in production is a model that doesn't work. You never lose sight of that

Bias for Action: You build, test, and ship with incomplete information and improve from real\-world feedback

Accountability: You hold yourself to the performance of your models in production, not just at evaluation time

Ego\-Free: Your success is measured by what the Intelligence Engine does for the product and the business, not your own visibility within the team

Range: Scoring architecture in the morning, LLM evaluation at noon, pipeline debugging by 3pm. You navigate all of it

Rigor: You care about statistical validity, model fairness, and production reliability not because someone is checking, but because the alternative is unacceptable

WHO SHOULD NOT APPLY

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You should not apply if:

• You prefer research and experimentation without a path to production
• You treat deployment, monitoring, and model maintenance as someone else's job
• You need a large ML team, perfect data, or a stable infrastructure to be effective
• You have never personally owned a model in production and felt it when it broke
• You communicate in jargon instead of outcomes
• You are looking for a polished, process\-heavy engineering environment where someone else sets the technical direction
• You've never been the engineer a product or leadership team actually depended on to make the AI work

COMPENSATION

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• Base salary benchmarked competitively for a senior individual contributor with production ML ownership
• Performance bonus tied to Intelligence Engine performance targets, model quality metrics, and individual contribution
• Equity participation commensurate with scope and tenure
• Professional development budget

We are not looking for someone who manages models. We are looking for someone who builds intelligence that works in the real world and drives the product forward while doing it. If you are the engineer who sees what the system should be doing before anyone else, closes the gap between experimentation and production without being asked, and holds quality standards high when everything around you is moving fast, we want to hear from you.

OTHER GESTURE BENEFITS AND PERKS

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• Full benefits: health, dental, vision, equity and stock options
• Make a Gesture Day: Each employee receives the chance to earn credit to send a surprise gift to someone they admire \- living the company mission firsthand
• 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

APPLICATION PROCESS

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Interested candidates should submit the following:

• A resume outlining relevant experience
• A brief cover letter highlighting your key skills, achievements, and what excites you about this opportunity
• Any project work, portfolio samples, GitHub repositories, or published research that demonstrate your passion and abilities

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