Mirendil

Mirendil is a tech-first company focused on solving core bottlenecks that unlock step-change acceleration across science and technology. Our first goal is to democratize frontier AI R&D across scientific disciplines. We believe accelerating scientific discovery is one of the most powerful ways to improve the future of humanity, and that AI will play a central role in making that possible.

We are building a frontier AI research company and training our own models end-to-end. Our work spans areas such as model training, reinforcement learning, reasoning systems, and infrastructure for large-scale experiments. Our team includes researchers and engineers from Anthropic, Google DeepMind, xAI, OpenAI, Microsoft, Apple, and MIT.

The Role

We are looking for an engineer to own the inference systems that power our models in production and research. You'll work across the full inference stack, from serving infrastructure down to hardware-level optimization. Some example areas you might work on (not limited to):

• Design and build high-throughput, low-latency inference serving systems for frontier models, optimizing for both research iteration and production deployment

• Optimize inference performance across GPU and accelerator hardware - maximizing FLOPs utilization, memory bandwidth, and compute efficiency for large-scale models

• Enable and extend distributed inference frameworks (e.g. vLLM, SGLang, TensorRT-LLM) to support novel architectures, long-context workloads, and agentic inference patterns

• Implement and validate inference-time optimizations: speculative decoding, quantization, KV cache management, and batching strategies

• Build observability and reliability infrastructure so the team can measure latency, throughput, and cost across every serving configuration

• Partner directly with teams to bring new model architectures and post-training techniques into production quickly

If you're excited about pushing the performance limits of frontier model inference, we'd love to hear from you.

We offer a base salary of $300,000–$400,000 USD and a meaningful equity grant, depending on experience and background, along with competitive benefits.

Mirendil

Mirendil is a tech-first company focused on solving core bottlenecks that unlock step-change acceleration across science and technology. Our first goal is to democratize frontier AI R&D across scientific disciplines. We are building a frontier AI research company and training our own models end-to-end.

The Role

We are looking for a research engineer to build the evaluation infrastructure that tells us whether our models are getting better in ways we care about. You'll own the frameworks, pipelines, and tooling that measure model behavior across capabilities. Some example areas you might work on (not limited to):

• Design and build evaluation frameworks that measure model capabilities along realistic axes, beyond standard benchmarks.

• Build automated eval pipelines and regression-detection systems that run continuously and surface signal quickly.

• Develop agent-assisted workflows for humans to efficiently inspect model behavior.

• Instrument training runs with observability tooling so researchers can understand what's changing in model behavior, and why.

• Partner with post-training and RL teams to close the loop between eval signal and training decisions.

If you're excited about the hard problem of knowing whether a frontier AI system is actually improving, we'd love to hear from you.

We offer a base salary of $300,000–$400,000 USD and a meaningful equity grant, depending on experience and background, along with competitive benefits.

Mirendil

Mirendil is a tech-first company focused on solving core bottlenecks that unlock step-change acceleration across science and technology. Our first goal is to democratize frontier AI R&D across scientific disciplines. We are building a frontier AI research company and training our own models end-to-end.

The Role

We are looking for an engineer to design, implement, and optimize custom ML kernels that bolster our model development stack. Your work will be deep in the system, combining hardware and software insights to optimize performance. Some example areas you might work on (not limited to):

• Design and implement custom performant ML kernels that work at scale

• Identify inefficiencies and optimize compute-intensive workloads to reduce memory bandwidth bottlenecks and improve hardware utilization

• Enable and validate low-precision arithmetic formats and contribute to related compiler or runtime stacks

If you're excited about working at the intersection of hardware and frontier AI research, we'd love to hear from you.

We offer a base salary of $300,000–$400,000 USD and a meaningful equity grant, depending on experience and background, along with competitive benefits.

Mirendil

Mirendil is a tech-first company focused on solving core bottlenecks that unlock step-change acceleration across science and technology. Our first goal is to democratize frontier AI R&D across scientific disciplines. We are building a frontier AI research company and training our own models end-to-end.

The Role

We are looking for research engineers to help build the post-training stack for frontier reasoning models.

This role sits at the point where model capability, training dynamics, data, verification, and infrastructure all meet. You will design and run the experiments that turn a strong base model into a model that can solve difficult tasks reliably: choosing training objectives, shaping data mixtures, building verifiers, debugging reward signals, scaling runs, and understanding why a recipe works or fails.

Researchers are also expected to have strong engineering skills. The best work here will involve both: forming hypotheses about training behavior, implementing them in real systems, running large-scale experiments, reading the resulting traces carefully, and turning the lessons into the next training run.

Some areas you may work on include:

• Post-training recipes: Develop and iterate on RL, SFT, and distillation recipes. Understand how choices in objectives, data mixtures, hyperparameters, rollout generation, and filtering affect efficiency, stability, capability, and final model behavior.

• Scaling RL: Make post-training work at larger scales: more tokens, longer trajectories, larger models, more steps, and larger compute budgets. This includes identifying the bottlenecks that appear only when an approach leaves the small-run regime.

• Long-horizon reasoning: Train models on tasks where success depends on many intermediate decisions. Develop methods for assigning useful feedback across long trajectories, where sparse rewards, credit assignment, exploration, and verification all become harder.

• Off-policy and asynchronous training: Work on training regimes where data is generated by older policies, different policies, or partially filtered policies. Build intuition and tooling for when off-policy data helps, when it hurts, and how to control the resulting instabilities.

• Verification and reward quality: Build robust verification pipelines for tasks where correctness can be checked automatically or semi-automatically. Detect and reduce reward hacking, false positives, brittle verifiers, and other failure modes that make RL look better than it really is.

• Multi-task post-training: Scale recipes across different task families and domains. Study the tradeoffs between specialization and generality, and design training mixtures that improve all capabilities together.

• Experiment analysis and debugging: Develop a deep empirical understanding of training runs. Diagnose regressions, separate real improvements from noise, design better ablations, and build the probes and analyses needed to make post-training less opaque.

• End-to-end execution: Work closely with systems, infrastructure, and data teams to get experiments from idea to production-scale runs. This includes making training pipelines reliable, ensuring data and verifier quality, and turning successful experiments into repeatable and scalable recipes.

If you're excited about building the infrastructure that makes frontier RL research possible at scale, we'd love to hear from you.

We offer a base salary of $300,000–$400,000 USD and a meaningful equity grant, depending on experience and background, along with competitive benefits.