Posted today · be early
Senior AI Engineer
LottieFiles
WorldwideremotePosted 1 day ago
Skill Required
AI-EngineeringMachine-Learning-EngineeringLLM-DevelopmentGenerative-AIMotion-Design-TechnologySenior-AI-EngineerSenior-AI-EngineeringSenior-Lead-AI-EngineerSenior-AI-Software-EngineerSenior-AI-ML-EngineerSenior-Software-AI-EngineerSenior-Applied-AI-EngineerSenior-ML-EngineerAI EngineerAIsoftware engineeringdata engineeringObservabilityEngineeringdevelopingbuildingPyTorchdesignRustAPIsandGenerative AIMachine LearningFulltime
Key highlights
- Role builds AI systems that generate production-quality motion from natural language using frontier LLMs and a custom Motion DSL.
- Must-have: hands-on experience building and operating production AI/ML systems (not prototypes).
- Must-have: practical experience with supervised fine-tuning and modern post-training workflows.
- Preferred: experience with code-generation models, DSLs, compilers, or structured output generation.
- Benefits include fully remote work, flexible hours, unlimited leave, medical insurance, and a home workstation bonus.
- Role provides end-to-end ownership of projects with measurable product impact.
Role overview
This role focuses on building AI systems that generate production-quality motion from natural language, combining frontier language models, an AI generation harness, and a custom text-native Motion DSL designed for structured, editable animation. The role spans two interconnected areas: improving the existing production generation system, and developing specialized models that can generate the Motion DSL directly with higher quality, lower latency, and better cost efficiency. It is a hands-on engineering role at the intersection of LLM systems, post-training, code generation, compilers, evaluation, data engineering, and motion design, offering end-to-end ownership and measurable product impact.
Responsibilities
- Build and improve production generative systems
- Design and ship improvements across prompt interpretation, model orchestration, routing, retrieval, tool use, structured generation, validation, repair, and visual verification.
- Diagnose recurring failure modes and turn them into durable improvements in prompts, data, system logic, constraints, or evaluation.
- Build compiler-backed feedback loops and deterministic quality gates that prevent invalid or low-quality outputs from reaching users.
- Develop experiments and fixed evaluation batteries that show whether a change genuinely improves output quality.
- Train specialized generative models
- Design supervised fine-tuning datasets, training recipes, and post-training experiments for direct Motion DSL generation.
- Explore distillation, preference optimization, synthetic-data generation, reinforcement-learning approaches, and constrained generation where they are the right tools.
- Select checkpoints using robust evaluations across correctness, visual quality, reliability, latency, and cost - not training loss alone.
- Determine whether a model failure is best addressed through data, training, inference, evaluation, or the underlying language/runtime.
- Build the data and evaluation foundation
- Turn production generations into high-quality training and evaluation datasets using filtering, provenance, versioning, deduplication, and contamination controls.
- Design train, validation, and evaluation splits that minimize leakage and preserve meaningful generalization tests.
- Create failure taxonomies, hard negatives, regression suites, and representative prompt batteries.
- Combine deterministic checks, model-based judges, render evidence, and human review into a reliable evaluation system.
Requirements
- Strong ML and software engineering: You have built and operated production AI or machine-learning systems, not only prototypes. You are comfortable moving across model behavior, data pipelines, APIs, infrastructure, evaluation, and product code.
- Hands-on LLM training experience: You have practical experience with supervised fine-tuning and modern post-training workflows. You understand how dataset construction affects model behavior and can explain how you prevent leakage, contamination, and misleading evaluation results.
- Strong evaluation instincts: You know that generative systems improve only when they can be measured. You can design experiments, regression suites, automated graders, and evaluation datasets that distinguish real gains from noise.
- Experience with structured or code generation: Experience with code-generation models, DSLs, grammars, parsers, compilers, structured outputs, constrained decoding, or program synthesis is especially relevant. The generated output is executable structured code, so syntactic and semantic correctness both matter.
- Production engineering judgment: You treat observability, reliability, latency, inference cost, caching, failure recovery, and maintainability as part of the ML system itself.
- Product and visual judgment: You can distinguish technically valid output from work that feels polished. Experience with animation, motion design, graphics, creative tooling, or multimodal systems is valuable, but not required.
Nice to have
- Experience fine-tuning or evaluating code-generation models.
- Experience with multimodal or vision-language models.
- Experience building model-based, human-in-the-loop, or rubric-driven evaluation systems.
- Experience with compilers, interpreters, language tooling, or program analysis.
- Experience with Rust, PyTorch, or distributed training infrastructure.
- Experience with preference optimization, reinforcement learning, or synthetic-data pipelines.
- Experience with animation, graphics, rendering, or creative software.
Benefits
- Fully Remote Working Environment
- Flexible Work Hours
- A welcome gift and LottieFiles swag pack
- Bonus to set up your workstation at home
- Unlimited Leave Days
- Medical Insurance
- Generous learning budget
- Gym membership
- Co-working space membership
Additional details
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- Originally posted on Himalayas