AI Engineer
17 Sep 2025
Washington, DC, USA
Verified by Turrior
Content + Source + Freshness • 14 Feb 2026 • 95% confidence
85 / 100
Offer value
This position offers high value due to its focus on cutting-edge AI applications, strong company growth, and competitive compensation.
- Competitive salary and equity opportunities.
- Work on innovative AI solutions that directly affect user experiences.
- Fully remote working arrangement, fostering flexibility.
Pros
- Focus on advanced AI techniques and production-grade applications.
- Opportunities to impact real-world data solutions.
- Remote work flexibility allows for a better work-life balance.
Cons
- Requires specialized skills with LLM applications.
- Intense project demands may lead to high workloads.
- Limited clarity on long-term company stability.
Who it's for
Mid-Senior • Fully Remote
Good fit
- Mid-career AI engineers with specialized knowledge in LLMs.
- Technical professionals eager to drive AI success in real applications.
- Those interested in fully remote roles with flexible hours.
Not recommended for
- Individuals lacking solid technical background in AI.
- Candidates who prefer structured, non-technical roles.
- Those not comfortable with remote work dynamics.
Motivation fit
Desire to solve complex AI challenges.Interest in working within data-driven environments.Willingness to iterate rapidly in a startup-like culture.
Key skills
AI/ML model tuningProduction code developmentExperiment design and evaluationSQL proficiency
Score: 85/100 AI verified analysis
About the job
About Bobsled
Bobsled is building AI-powered analytics experiences that turn natural language into accurate, production-grade insights. We’re looking for a hands-on AI Engineer to drive text-to-SQL accuracy and the systems that make our LLM-based application reliable in production.
What You’ll Do
- Own the text-to-SQL accuracy problem end-to-end: design evals, iterate prompts, and improve retrieval/routing
- Build and operate the experimentation and evaluation loop (automatic evals, regression suites, dataset curation)
- Design pragmatic LLM application architectures (RAG, agent routing, tool-use orchestration) optimized for accuracy and latency
- Ship production-grade code and support deployments; instrument, monitor, and troubleshoot model behavior in real customer environments
- Partner closely with engineering and customers to improve semantic models, SQL generation, and data alignment
- Create feedback loops from users to systematically capture issues and convert them into measurable improvements
- Contribute to automation of environment provisioning and dev workflows to enable fast iteration
What We’re Looking For
- 2+ years in ML/AI or data-focused engineering or data science roles building production systems data or AI systems
- Demonstrated experience tuning LLM applications: prompt engineering, evals, retrieval, agent design, or similar
- Strong hands-on coding in Python or TypeScript (TypeScript familiarity a plus; willingness to work across the stack required)
- ML engineering mindset beyond notebooks: testing, CI, observability, performance, and deployment in production
- Comfort with SQL and complex data modeling; familiarity with data warehouses and pipelines
- Pragmatic, product-oriented approach—optimize for impact over novelty; complement existing systems rather than rebuild from scratch
- Ability to design experiments, quantify improvements, and communicate trade-offs clearly
Nice to Have
- Experience with text-to-SQL systems, semantic layers, or BI/analytics workflows
- Exposure to RAG frameworks, knowledge graphs, vector stores, and evaluation tooling
- Prior work in analytics engineering or data engineering environments
Success Looks Like
- Measurable improvements in text-to-SQL accuracy across target datasets and partners
- Reliable eval pipeline and regression suite running in CI to catch degradations
- Clear architecture and documentation for context/agent systems that others can contribute to
- Short feedback cycles with partners leading to fast, meaningful product wins
Compensation
- Competitive salary and meaningful equity
- Comprehensive benefits
#LI-REMOTE
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