AI Research Engineer
Syntas is seeking an AI Research Engineer to push the boundaries of what is possible with AI while keeping one foot firmly planted in practical application. You will explore cutting-edge techniques, evaluate emerging models, build experimental systems, and translate research advances into production-ready solutions for our clients. This role combines the intellectual excitement of research with the satisfaction of seeing your work deployed in the real world. This position is ideal for engineers with strong ML fundamentals who want to stay at the frontier of AI without losing touch with practical impact. You will work on problems like local model deployment, novel retrieval architectures, multi-agent coordination, and domain-specific fine-tuning—always with an eye toward what can be productionized.
About the Role
As an AI Research Engineer at Syntas, you will be our scout at the frontier of AI capabilities. Your job is to understand what is becoming possible, separate hype from substance, and identify opportunities to apply emerging techniques to client problems before they become mainstream. You will prototype, experiment, and build proof-of-concepts that inform our technical strategy and create differentiated solutions for clients.
A significant focus of this role is local and self-hosted model deployment. Many clients have requirements—privacy, latency, cost, compliance—that make cloud API models impractical. You will evaluate open-source models (Llama, Mistral, Mixtral, and others), optimize them for specific use cases through fine-tuning or quantization, and deploy them on appropriate infrastructure. Understanding the tradeoffs between model size, speed, and capability is essential.
You will also work on advanced retrieval and generation architectures. Standard RAG is becoming table stakes—you will explore techniques like hypothetical document embeddings, multi-vector retrieval, graph-based knowledge representations, and hybrid search approaches that improve retrieval quality for complex domains. You will build and evaluate these systems systematically, measuring improvements and documenting what works.
Multi-agent systems are another area of focus. As AI capabilities grow, orchestrating multiple specialized agents to solve complex problems becomes more viable. You will design and implement multi-agent architectures, experiment with different coordination patterns, and develop the evaluation frameworks to measure whether complex agent systems actually outperform simpler approaches for given use cases.
What You Will Build
- 1Local model deployment pipelines using Ollama, vLLM, and optimized inference servers
- 2Fine-tuned models for domain-specific applications using LoRA, QLoRA, and full fine-tuning approaches
- 3Advanced retrieval systems including multi-vector approaches, knowledge graphs, and hybrid search
- 4Multi-agent architectures with novel coordination patterns, memory systems, and tool use
- 5Evaluation frameworks for comparing model performance across dimensions relevant to client use cases
- 6Research prototypes that demonstrate emerging capabilities for potential client applications
- 7Internal tooling and frameworks that accelerate experimentation and research-to-production pipelines
Key Responsibilities
- Evaluate emerging AI models, techniques, and tools for potential application to client problems
- Design and implement local model deployment solutions using open-source models and inference optimization
- Fine-tune models for domain-specific applications, managing the full lifecycle from data preparation to deployment
- Build advanced retrieval architectures that go beyond standard RAG for complex information needs
- Develop multi-agent systems and experiment with coordination patterns, memory, and tool integration
- Create rigorous evaluation frameworks that measure AI system performance on relevant metrics
- Prototype novel approaches and build proof-of-concepts that inform technical strategy
- Translate research papers and techniques into practical implementations
- Collaborate with engineering teams to productionize research outputs
- Document research findings, experimental results, and recommendations for internal knowledge sharing
- Present technical deep-dives to team members and contribute to a culture of continuous learning
- Engage with the broader AI research community through reading groups, conferences, and publications
- Guide technical direction for AI projects and advise on approaches for complex client challenges
What We Are Looking For
- 5+ years of software engineering experience with at least 3 years focused on AI/ML
- Strong understanding of transformer architectures, attention mechanisms, and modern LLM fundamentals
- Experience with model fine-tuning including data preparation, training, and evaluation
- Hands-on experience with local model deployment: Ollama, vLLM, TGI, llama.cpp, or similar
- Deep knowledge of retrieval systems: embedding models, vector databases, and retrieval optimization
- Strong Python proficiency including PyTorch or similar deep learning frameworks
- Experience designing and running ML experiments with appropriate rigor and documentation
- Understanding of model optimization techniques: quantization, pruning, distillation
- Familiarity with the research landscape: ability to read papers and translate to implementation
- Strong analytical and problem-solving skills with ability to debug complex ML systems
- Excellent communication skills for explaining research findings to technical and non-technical audiences
- Self-directed research style with ability to identify promising directions and manage time effectively
Nice to Have
- Publications in ML/AI venues (NeurIPS, ICML, ACL, EMNLP, etc.)
- Experience with multi-agent systems and agent orchestration frameworks
- Background in specific ML domains: NLP, computer vision, speech, or reinforcement learning
- Experience with distributed training and large-scale ML infrastructure
- Knowledge of efficient inference: speculative decoding, continuous batching, KV cache optimization
- Familiarity with AI safety and alignment research
- Experience with knowledge graphs and graph neural networks
- Background in specific verticals: healthcare, legal, finance where domain expertise matters
- Contributions to open-source ML projects
- Experience mentoring junior researchers or leading research initiatives
- Active participation in ML research communities
- Prior experience at research labs, AI startups, or applied research roles
Tech Stack
Benefits & Perks
- Competitive salary: $150,000 - $210,000 depending on experience
- Equity participation with meaningful upside as we grow
- Fully remote work with flexible hours—work from anywhere in the US
- Comprehensive health, dental, and vision insurance (100% premium covered for employee)
- Unlimited PTO with encouraged minimum of 4 weeks—we mean it
- $3,000 annual learning and development budget for courses, books, and certifications
- Conference attendance budget including travel—attend or speak at ML research conferences
- Top-tier hardware: MacBook Pro with GPU access for experiments, external display, and peripherals
- All AI tools and subscriptions you need: GPT-4, Claude, compute credits, and more
- Quarterly team offsites in interesting locations
- 401(k) with company match
- Paid parental leave (12 weeks)
- Home office setup stipend ($1,000)
- Work on genuinely interesting problems at the frontier of AI
Ready to Apply?
Send us your resume and a brief introduction. Tell us about your experience with AI/ML systems and what excites you about this opportunity.