Middle Machine Learning Engineer

About the Role

As a Member of Technical Staff, Machine Learning, you will build and improve core ML components while working on real production systems from day one. You will gain hands-on experience with how large-scale machine learning behaves beyond research environments, developing strong systems judgment by shipping, debugging, and iterating on real-world ML solutions under production constraints such as latency, cost, reliability, and safety.

Core Focus & Responsibilities

• Component Development: Build, improve, and maintain ML components across data, training, evaluation, and inference pipelines.
• Model Fine-Tuning: Fine-tune and adapt machine learning models as integral components of larger production systems.
• Evaluation & Testing: Develop rigorous evaluation and testing frameworks to measure and understand non-deterministic model behavior.
• Data Pipelines: Build and maintain reliable data pipelines supporting both real-world and synthetic datasets.
• Debugging & Maintenance: Investigate and resolve model issues, performance bottlenecks, and production incidents.
• Iterative Shipping: Ship improvements continuously, incorporating real-world user feedback and working closely with senior ML engineers and product teams.

What We’re Looking For

• ML Foundations: Strong theoretical and practical understanding of machine learning and modern neural network architectures.
• Hands-On ML Experience: Practical experience training, fine-tuning, evaluating, or deploying machine learning models.
• Production Engineering Skills: Ability to write production-quality code and quickly learn new tools, frameworks, and technologies.
• Mindset: Curious, coachable, and motivated to learn through hands-on experience with real-world production systems.
• Execution & Ownership: Ability to navigate ambiguity with guidance, progressively take on greater ownership, and maintain a strong bias toward shipping and continuous improvement.

Tech Stack

• Languages & Frameworks: Python, PyTorch / JAX
• Infrastructure: Production ML systems running on GPUs

Expected Outcomes

• Production Targets: Ensure production ML models meet expected accuracy, latency, reliability, and quality targets.
• Rapid Resolution: Identify production issues quickly, debug effectively, and address underlying root causes.
• Robust Pipelines: Build and maintain reproducible, reliable data pipelines, training workflows, and inference systems.
• Data-Driven Iteration: Continuously improve models and systems based on real-world signals, experimentation, and measurable performance metrics.

How We Work & Application Process

We are a small, world-class team with a high talent density. We operate at a rapid pace while balancing high-quality engineering with continuous learning, experimentation, and iteration.

• Interview Process: If there appears to be a mutual fit, we will schedule 3–4 interviews with members of the technical team, conducted virtually and/or on-site. We prioritize transparency and efficiency and aim to make timely, well-informed decisions throughout the process.