Software Engineer - Model Products

The Consensus; New York (NY); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Competitive compensation including equity

100% coverage of medical, dental, and vision insurance

Flexible PTO policy

Paid parental leave

Fertility and family-building stipend

Company-facilitated 401(k)

Job summary

The Consensus is seeking a skilled engineer to join our Model Performance team, focusing on building and optimizing APIs used in AI applications. You will contribute to enhancing the performance and reliability of our model serving infrastructure, ensuring it meets the needs of developers.

This role requires experience with distributed systems and a strong foundation in backend services, allowing you to work effectively within our small, high-impact team. Enjoy competitive compensation, health insurance, flexible PTO, and an inclusive workplace at The Consensus.

Qualifications

• 3+ years of experience building and operating distributed systems or large-scale APIs.
• Proven track record of low-latency, reliable backend services.
• Comfortable debugging complex systems from runtime internals to GPU execution traces.

Responsibilities

• Design, build, and operate Model APIs with advanced inference capabilities.
• Profile and optimize TensorRT-LLM kernels and analyze CUDA kernel performance.
• Productionize performance improvements across runtimes with deep understanding.
• Build comprehensive benchmarking frameworks for real-world performance measurement.
• Collaborate closely with teams to deliver robust model serving experiences.

Skills

Distributed systems

Large-scale APIs

Profiling and tracing

Debugging complex systems

Strong written communication

Tools

TensorRT

CUDA

Kubernetes

Job description

ABOUT BASETEN

Baseten powers mission‑critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting‑edge models into production. We’re growing quickly and recently raised our $1.5B Series F, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products.

THE ROLE:

Baseten’s Model Performance (MP) team is responsible for ensuring the models running on our platform are fast, reliable, and cost‑efficient. As part of this team, you’ll focus on Model API's — the infrastructure powering our hosted API endpoints for the latest open‑source models. This work spans distributed systems, model serving, and developer experience. You’ll join a small, high‑impact team operating at the intersection of product, model performance, and infra, helping to define how developers interact with AI models at scale.

RESPONSIBILITIES:

• Design, build, and operate the Model APIs surface with focus on advanced inference capabilities: structured outputs (JSON mode, grammar‑constrained generation), tool/function calling and multi‑modal serving
• Profile and optimize TensorRT‑LLM kernels, analyze CUDA kernel performance, implement custom CUDA operators, tune memory allocation patterns for maximum throughput and optimize communication patterns across multi‑GPU setups
• Productionize performance improvements across runtimes with deep understanding of their internals: speculative decoding implementations, guided generation for structured outputs, custom scheduling and routing algorithms for high‑performance serving
• Build comprehensive benchmarking frameworks that measure real‑world performance across different model architectures, batch sizes, sequence lengths, and hardware configurations
• Productionize performance improvements across runtimes (e. TensorRT, TensorRT‑LLM): speculative decoding, quantization, batching, and KV‑cache reuse.
• Instrument deep observability (metrics, traces, logs) and build repeatable benchmarks to measure speed, reliability, and quality.
• Implement platform fundamentals: API versioning, validation, usage metering, quotas, and authentication.
• Collaborate closely with other teams to deliver robust, developer‑friendly model serving experiences.

REQUIREMENTS:

• 3+ years experience building and operating distributed systems or large‑scale APIs.
• Proven track record of owning low‑latency, reliable backend services (rate‑limiting, auth, quotas, metering, migrations).
• Infra instincts with performance sensibilities: profiling, tracing, capacity planning, and SLO management.
• Comfortable debugging complex systems, from runtime internals to GPU execution traces.
• Strong written communication; able to produce clear design docs and collaborate across functions.

NICE TO HAVE:

• Experience with LLM runtimes (vLLM, SGLang, TensorRT‑LLM) or contributions to open‑source inference engines (vLLM, TensorRT‑LLM, SGLang, TGI)
• Knowledge of Kubernetes, service meshes, API gateways, or distributed scheduling.
• Background in developer‑facing infrastructure or open‑source APIs.
• We value infra‑leaning generalists who bring strong engineering fundamentals and curiosity. ML experience is a plus, but not required.

BENEFITS

• Competitive compensation, including meaningful equity.
• 100% coverage of medical, dental, and vision insurance for employee and dependents
• Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
• Paid parental leave
• Fertility and family‑building stipend through Carrot
• Company‑facilitated 401(k)
• Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.

At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.

We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).