ML Engineer

Catalyst Labs; Fort Worth (TX); On-site; USD 100,000 - 150,000; Full time

Job summary

A tech-focused startup is seeking a Machine Learning Engineer to design and deploy production-grade ML systems, focusing on audio data. Successful candidates will have a Bachelor's or Master's in Computer Science or a related field and 1-6 years of ML engineering experience. Responsibilities include creating AI-powered solutions and engaging with clients for feedback. An attractive compensation package includes above market base salary, bonuses, and equity options, with a work location in New York.

Qualifications

• Experienced in machine learning engineering with a focus on audio data.
• Strong programming skills in Python and familiarity with TypeScript.
• Capable of communicating with customers to understand their needs.

Responsibilities

• Design, build, and deploy production-grade ML systems.
• Architect AI solutions for natural speech interaction.
• Handle the entire AI lifecycle including model training and monitoring.

Skills

Machine Learning

Python

Data pipeline design

Communication skills

Real-time inference

Education

Bachelor’s or Master’s in Computer Science or related field

Tools

PyTorch

TensorFlow

AWS

FastAPI

PostgreSQL

Job description

Our Client is a rapidly growing Tier 1 VC‑backed startup based in New York with $60 million in funding, revolutionizing how outside sales and service teams work. Their AI technology captures and analyzes real-world conversations, providing full visibility into every customer interaction without the need for traditional ride‑alongs.

About the Job

The company turns field conversations into searchable, actionable data, empowering teams to coach more effectively, close more deals, and boost average ticket sizes. By combining cutting‑edge AI with a deep understanding of field sales dynamics, it redefines how businesses learn from and optimize in‑person customer experiences.

Location

New York, NY

Work type

Full Time

Compensation

Above market base + bonus + equity

Roles & Responsibilities

• Design, build, and deploy production‑grade ML systems with end‑to‑end ownership of the model lifecycle from conception to deployment and maintenance.
• Architect and deliver AI‑powered solutions enabling natural speech interaction and real‑time audio understanding.
• Develop and optimize ML models focused on audio data to extract business‑critical insights from previously unstructured voice data.
• Build agents capable of operating natively on real‑world audio inputs.
• Collaborate with cross‑functional teams to shape the foundations of the AI stack, improve tooling, and drive innovation in LLM and audio ML applications.
• Work directly with customers to identify needs, gather feedback, and deliver impactful real‑world solutions.
• Handle the entire AI lifecycle, including data acquisition, preprocessing, model training, deployment, inference, and monitoring in production environments.
• Participate in continuous improvement of the ML infrastructure and processes for scalability and performance.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 1-6 years of professional experience in ML engineering.
• Strong programming skills in Python (TypeScript experience is a plus).
• Hands‑on experience with ML frameworks such as PyTorch or TensorFlow.
• Familiarity with cloud environments and infrastructure (preferably AWS).
• Strong understanding of data pipeline design, real‑time inference, and model monitoring.
• Excellent communication skills with the ability to engage directly with customers and stakeholders.

Core Experience

• Proven experience building and deploying ML models into production environments.
• Demonstrated ability to own the full model lifecycle from data ingestion and model development to deployment and monitoring.
• Experience with audio‑focused ML projects or similar domains involving unstructured data.
• Proficiency in building scalable data pipelines for model training and evaluation.
• Familiarity with FastAPI, OpenAI APIs, Baseten, LiteLLM, LiveKit, PostgreSQL, Redis, and S3 is a plus.
• Solid grasp of ML systems architecture, feature engineering, evaluation strategies, and deployment best practices.