Featherless AI

AI Engineer

• Research and develop new neural network architectures (e.g. alternatives or extensions to Transformers, recurrent / hybrid models, long-context systems)
• Design and run architecture-level experiments (scaling laws, memory mechanisms, compute trade-offs)
• Prototype models end-to-end — from research code to training-ready implementations
• Collaborate with inference and systems engineers to ensure architectures are deployable and efficient
• Analyze model behavior, failure modes, and inductive biases
• Read, reproduce, and extend cutting-edge research papers
• Contribute to internal research notes, benchmarks, and open-source efforts (where applicable)

Machine Learning Engineer – AI Architecture Research

Machine Learning Engineer

• Design and implement knowledge distillation pipelines (teacher–student, self-distillation, multi-teacher, etc.)
• Distill large foundation models into smaller, faster, and cheaper models for inference
• Run and analyze large-scale training experiments to evaluate quality, latency, and cost tradeoffs
• Collaborate with research to translate new distillation ideas into production-ready code
• Optimize training and inference performance (memory, throughput, latency)
• Contribute to internal tooling, evaluation frameworks, and experiment tracking
• (Optional) Contribute back to open-source models, tooling, or research

Machine Learning Engineer – Distillation

• Design, build, and maintain large-scale multilingual datasets across high- and low-resource languages
• Develop data pipelines for collection, cleaning, normalization, deduplication, and labeling
• Implement quality filters using statistical, heuristic, and model-based methods
• Work with researchers to define language coverage, benchmarks, and evaluation metrics
• Analyze dataset bias, coverage gaps, and failure modes across regions and scripts
• Support training, fine-tuning, and distillation workflows with high-quality multilingual data
• Continuously iterate on datasets based on model performance and real-world usage

Machine Learning Engineer – Multilingual Data

• Optimize large-scale model training pipelines (throughput, convergence, stability, and cost)
• Improve distributed training strategies (data, model, and pipeline parallelism)
• Tune optimizers, schedulers, batch sizing, and precision (bf16 / fp16 / fp8)
• Reduce training time and compute cost via profiling, bottleneck analysis, and systems-level improvements
• Collaborate with researchers on architecture-aware training strategies
• Build and maintain robust training infrastructure (checkpointing, fault tolerance, reproducibility)
• Evaluate and integrate new training techniques (e.g. gradient checkpointing, ZeRO, FSDP, custom kernels)
• Own training performance metrics and continuously push them forward

Machine Learning Engineer – Training Optimization

• Optimize inference latency, throughput, and cost for large-scale ML models in production
• Profile and bottleneck GPU/CPU inference pipelines (memory, kernels, batching, IO)
• Implement and tune techniques such as:
• Quantization (fp16, bf16, int8, fp8)
• KV-cache optimization & reuse
• Speculative decoding, batching, and streaming
• Model pruning or architectural simplifications for inference
• Collaborate with research engineers to productionize new model architectures
• Build and maintain inference-serving systems (e.g. Triton, custom runtimes, or bespoke stacks)
• Benchmark performance across hardware (NVIDIA / AMD GPUs, CPUs) and cloud setups
• Improve system reliability, observability, and cost efficiency under real workloads

Machine Learning Engineer – Inference Optimization

Full-stack Engineer

• Own product development end-to-end — from diagraming user flows to shipping production features
• Drive UX decisions — wireframe new features, iterate on existing interfaces, and advocate for user needs
• Build and maintain our web platform — model catalog and explorer, dashboard, documentation site, and developer tools
• Collaborate with the API team — ensure seamless integration between frontend experiences and backend capabilities
• Respond to user feedback — triage issues, identify patterns, and rapidly ship improvements
• Scale with our growth — evolve the platform architecture as we onboard more models, users, and use cases

Full Stack Software Developer

• support and evolve the API gateway to our inference cloud, which is responsible for
• authentication and inference to all models
• subscription management and subscription entitlement (e.g. context-length, concurrency limits)
• providing the necessary API surface for applications and builders
• undertake feature development and bug fixes to keep up with clients, resolve user issues, and onboard new models
• improve the reliability of the existing API (increasing instrumentation and monitoring, right-sizing infrastructure)
• respond to availability incidents
• triage and resolve issues of inference quality and reliability
• manage the infrastructure on which our gateway runs

Software Engineer – API Gateway

Developer Relations

• Empower developers: Help students, researchers, model creators, and early-stage AI startups deploy and scale their projects on Featherless.
• Grow the ecosystem: Expand Featherless’s network of builders, labs, and university communities across Europe.
• Build and share: Help create small examples, demos, starter templates, or reference integrations that show what’s possible with open models.
• Represent Featherless: at hackathons, community events, and developer showcases.
• Collect feedback: Act as the voice of the developer community — surfacing insights that shape our roadmap and improve the developer experience.