Senior Machine Learning Engineer

Talanto; San Francisco, Northern (CA, KY); Hybrid; USD 150,000 - 250,000; Full time

Benefits offered by this job

Fully remote

Performance-based bonus

Learning & development stipend

Health benefits

High-scale impact projects

Job summary

Talanto is seeking a Senior Machine Learning Engineer to lead the design, training, and deployment of ML systems in production, combining deep ML expertise with strong software engineering to own models from research through scaled inference.

This fully remote role offers flexible hours, a performance-based bonus, learning stipend, and health benefits. You will collaborate across time zones and mentor junior engineers while shipping high-impact ML solutions.

Qualifications

• 5+ years of ML engineering experience with production ownership.
• Deep knowledge of PyTorch or TensorFlow.
• Experience deploying models at scale.
• Hands-on with cloud ML infrastructure.
• Strong software engineering fundamentals.

Responsibilities

• Design, train, and deploy ML models at production scale
• Lead architecture decisions for training, evaluation, and inference pipelines
• Build evaluation frameworks and monitoring for model quality and drift
• Partner with data engineering on training data pipelines and feature stores
• Mentor junior ML engineers and contribute to ML practice
• Stay current on research and bring proven techniques into production

Skills

Python

PyTorch

TensorFlow

Production ML

Cloud ML

Software engineering

Tools

AWS Sagemaker

GCP Vertex

Databricks

CI/CD

Job description

Python PyTorch TensorFlow AWS GCP

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• Role: Senior Machine Learning Engineer
• Location: Remote
• Employment Type: Full-time

Compensation: Competitive salary commensurate with experience, qualifications, and location. Indicative range: $150,000 – $250,000 (USD equivalent), plus benefits.

Role Overview

We are hiring a Senior Machine Learning Engineer to lead the design, training, and deployment of ML systems in production. The role combines deep ML expertise with strong software engineering — owning models from research through scaled inference.

Key Responsibilities

• Design, train, and deploy machine learning models at production scale
• Lead architecture decisions for training, evaluation, and inference pipelines
• Build evaluation frameworks and monitoring for model quality and drift
• Partner with data engineering on training data pipelines and feature stores
• Mentor junior ML engineers and contribute to ML practice
• Stay current on research and bring proven techniques into production

Required Skills and Qualifications

• 5+ years in machine learning engineering with production ownership
• Strong Python skills and deep experience with PyTorch or TensorFlow
• Experience training and serving models at scale (deep learning, LLMs, or classical ML)
• Hands-on with cloud ML infrastructure (AWS Sagemaker, GCP Vertex, Databricks, or similar)
• Strong software engineering fundamentals (testing, code review, system design)
• Track record of shipping ML systems that delivered measurable business value

What You'll Bring

• Curiosity about how models behave and why they fail
• Rigor in following guidelines and flagging ambiguity early
• Strong written communication, including ability to document findings clearly
• Comfort working asynchronously across time zones

What We Offer

• Fully remote, flexible work hours
• Performance-based bonus structure
• Annual learning & development stipend
• Health and wellness benefits (varies by location)
• Opportunity to work on high-scale, real-world impact projects

Equal Opportunity Statement

This is an equal opportunity role. Applications are welcomed from all qualified individuals regardless of race, color, ethnicity, nationality, gender, gender identity or expression, sexual orientation, age, religion, disability, marital status, or any other characteristic protected by applicable law. All hiring decisions are based solely on qualifications, skills, and demonstrated ability.