Solutions Engineer (AI/ML, Pre-Sales)

What You’ll Work On

• Embed deeply with strategic customers to understand their data curation needs, business challenges, and technical requirements in detail.
• Lead end-to-end customer PoCs that connect data curation, training behavior, evaluation outcomes, including dataset analysis, training plan design, and results interpretation.
• Partner with customer ML teams to map data & curation strategy.
• Design and execute evaluation plans for base and post-trained models, selecting appropriate benchmarks/metrics, and running model evaluations.
• Produce customer-ready evaluation reports: methodology, metrics, baselines, ablations (e.g., curated vs raw), conclusions, and recommended next steps for productionization.
• Communicate technical results to both ML experts and exec stakeholders, including tradeoffs in compute, latency, and deployment cost.
• Collaborate closely with GTM, Engineering, and Research teams to ensure seamless customer experiences, deliver compelling demos, align on requirements, and bring customer insights into actionable model training and product strategies.
• Provide technical guidance, training, and clear documentation to ensure prospects can confidently assess the solution.

About You

• 4+ years of experience in software, ML platform, solutions, or customer engineering roles, with significant experience driving technical pre-sales engagements and PoCs.
• Strong practical expertise in ML model training, including how models are trained and improved across pre-training, domain-specific mid-training, and post-training, such as supervised fine-tuning and reinforcement learning.
• Demonstrated ability to design, run, and interpret model evaluations for base and post-trained models: choosing metrics/benchmarks, building or using evaluation harnesses, analyzing results, and presenting findings clearly with customers.
• Strong programming skills in Python (or equivalent); able to prototype quickly and iterate with customers.
• Experience with data processing / distributed systems (e.g., Spark, Ray, data lakes/warehouses) and comfort working with large-scale datasets.
• Familiarity with modern ML infrastructure: PyTorch/Hugging Face ecosystems, distributed training concepts, and deployment environments across cloud/on-prem/hybrid.
• Familiarity with cloud platforms (AWS/GCP/Azure) and containerization (Docker/Kubernetes).
• Strong communication skills, with the ability to translate complex ML and systems topics for diverse audiences.
• Required to travel to customer sites as needed to support pre-sales engagements.

Compensation

• Salary ranges from $230,000 to $300,000 OTE.
• 100% covered health benefits (medical, vision, and dental).
• 401(k) plan with a generous 4% company match.
• Unlimited PTO policy.
• Annual $2,000 wellness stipend.
• Annual $1,000 learning and development stipend.
• Daily lunches and snacks provided in office.
• Relocation assistance for employees moving to the Bay Area.