Machine Learning Engineer ($400k - $600k salary)

Baton Corporation Ltd; New York (NY); On-site; USD 140,000 - 210,000; Full time

Job summary

Baton Corporation is seeking a Machine Learning Engineer to build and deploy production-grade recommendation systems and related ML infrastructure. You will own models, data pipelines and supporting systems for real-time personalization and decision making.

Work closely with product teams to deliver low-latency inference, experiment with advanced ML techniques and improve platform quality for massive user engagement.

Qualifications

• Experienced ML engineer with strong algorithms and statistics.
• Experience building production-ready recommendation systems with measurable impact.
• Familiar with embeddings, candidate generation and cold-start challenges.
• Proficient in Python and ML frameworks; strong software engineering.

Responsibilities

• Design, develop and deploy recommendation models for discovery and engagement.
• Build scalable data pipelines for large datasets and features.
• Develop ML infrastructure for scalable training and inference.
• Collaborate with teams to integrate models with low-latency needs.
• Design experiments and use A/B tests to measure impact.

Skills

Machine Learning

Recommendation systems

Python

TensorFlow

PyTorch

Data pipelines

Model deployment

A/B testing

NLP

Reinforcement learning

On-chain data

Blockchain interest

Collaboration

Tools

TensorFlow

PyTorch

Job description

The Company

Baton Corporation is the development company that builds and operates the entire technology stack behind pump.fun, the largest memecoin launchpad in production today. The systems are low latency, high throughput, live under constant load, and break if you get them wrong.

What You’ll Do

As a Machine Learning Engineer, you'll work alongside engineers and product teams to build algorithms that power personalisation, recommendations, search and real-time decision-making. Your primary focus will be recommendation systems, with opportunities to apply machine learning to fraud detection and content moderation.

You’ll take projects from initial problem definition through to deployment and iteration, owning the models, data pipelines and supporting infrastructure needed to deliver results.

• Design, develop and deploy recommendation models that improve discovery, personalisation, engagement and retention.

• Build scalable data pipelines and use large datasets to develop features, train models and inform product development.

• Develop and optimise machine learning infrastructure, ensuring training and inference systems are scalable, efficient and reliable in production.

• Work closely with product teams to integrate models into the platform, supporting low-latency inference and real-time data processing.

• Design experiments and combine offline evaluation with A/B testing to measure impact and guide improvements.

• Experiment with techniques across deep learning, natural language processing and reinforcement learning where they can solve meaningful product problems.

• Monitor, optimise and iterate on deployed models as user behaviour, data and product needs evolve.

• Develop models for fraud detection, abuse prevention and content moderation to improve platform quality and user experience.

• Collaborate with engineers and data scientists to solve challenges involving high-volume, on-chain and user activity data.

• Identify useful developments in ML research and turn them into practical improvements.

Who You Are

• An experienced Machine Learning Engineer with a strong foundation in algorithms, data structures and statistical modelling.

• Experienced in building and deploying recommendation systems in production, with measurable improvements to user experience or business outcomes. This is our highest-priority area of experience.

• Familiar with retrieval, ranking and personalisation techniques, including embeddings, candidate generation and challenges such as cold starts and sparse data.

• Proficient in Python and frameworks such as TensorFlow or PyTorch, with strong software engineering skills and hands-on experience deploying models.

• Skilled in building and maintaining ML infrastructure, including data pipelines, model deployment, monitoring and systems that scale to large datasets.

• Comfortable working across the entire ML lifecycle, from research and experimentation to deployment and maintenance.

• Experienced working in startups, delivering at a high pace through ambiguity, shifting priorities and limited resources.

• Hands-on and resourceful, willing to build the models, pipelines and infrastructure yourself to get a solution into production.

• High-agency and ownership-driven, capable of identifying problems, making decisions and leading projects end to end with minimal guidance.

• Highly collaborative, with the ability to communicate technical concepts clearly to engineers, product teams and non-technical stakeholders.

• Experience with production fraud detection, abuse prevention or content moderation systems will be highly advantageous.

• An interest in blockchain, crypto and SocialFi technologies is a plus.

What it's like to work here

• We work in person

• Hours can be long and unconventional

• The pace is intense

• Expectations are high, and impact is immediate

• Working at Baton is not for everyone

Why Join Us?

• Unmatched ownership and autonomy

• Exposure to systems operating at the edge of crypto scale

• The ability to ship fast and see real-world impact immediately

If you're motivated by responsibility, speed, and building products used by massive audiences, you'll feel at home here.