Requirements:

• You have a BS/MS/PhD in Computer Science, Engineering, Machine Learning, Applied Mathematics, or a related scientific field, or equivalent experience.
• You have hands-on experience training and fine-tuning models at scale, and deploying them into production systems with real throughput and cost constraints.
• You have a working knowledge of how GPUs work, and a track record of making models run efficiently within real hardware constraints.
• You have strong applied-mathematics fundamentals and reach for them naturally when designing and optimizing models.
• You have a real passion for applied mathematics, software design, and implementation. This role sits at the intersection of the three, and it is a requirement for the position.
• You care about code simplicity and performance, and you can build the data pipelines and the surrounding production code, in addition to the models themselves.
• You can explain complex ideas and trade-offs clearly to engineers and product partners, and you let a solid understanding of the product guide what you build next.

Nice to Haves:

• Bonus: experience with efficient sequence architectures, model interpretability, or large-scale streaming systems.

What You'll Do:

• Design and build custom mid-size models for high-throughput stream processing, and train them at scale.
• Optimize these models from start to finish, working across both the mathematics and the systems, often by finding mathematical reformulations that fit the hardware and software constraints better.
• Build the training data pipelines the work depends on when they do not already exist, from the raw stream to a training-ready dataset.
• Work with engineering to integrate models into production, with a clear focus on GPU utilization, latency, and cost per record on live traffic.
• Plan the roadmap of model and system improvements, based on a solid understanding of the product and of what matters most to users.
• Build an agentic layer on top of the models to analyze, validate, and act on their outputs.
• Build lightweight interpretability tools that make model behavior easier to explain to the people who rely on it.
• Maintain and monitor the models, services, and infrastructure your team owns, and take part in your team's on-call rotation.

Perks and Benefits:

• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• Opportunity to attend and present at conferences and meetups, and to publish your work
• Intra-departmental mentor and buddy program for in-house networking
• An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
• Competitive global benefits