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Open nowFirst seen 34 hours ago

ML Engineer

Catalyst Labs202 open roles

Where
San Francisco, California, United States
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Your applicationOpen nowML EngineerCatalyst Labs · San Francisco, California, United States

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  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

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11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: first seen 34 hours ago

The posting

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.

By turning field conversations into searchable, actionable data, they empower teams to coach more effectively, close more deals, and boost average ticket sizes. Combining cutting-edge AI with a deep understanding of field sales dynamics, this company is redefining how businesses learn from and optimize their in-person customer experiences.

About Us

Catalyst Labs is a leading talent agency with a specialized vertical in Applied AI, Machine Learning, and Data Science. We stand out as an agency that’s deeply embedded in our clients’ recruitment operations.

We collaborate directly with Founders, CTOs, and Heads of AI in those themes who are driving the next wave of applied intelligence from model optimization to productized AI workflows. We take pride in facilitating conversations that align with your technical expertise, creative problem-solving mindset, and long-term growth trajectory in the evolving world of intelligent systems.

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 lifecyclefrom 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 lifecyclefrom 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.
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