Benjamin Reavill

Machine Learning Engineer (LLM) Salary Competitive DOE Boston, 2 days per week in-office We’re working a fast-growing AI company on a mission to automate complex workflows in the financial services sector, starting with insurance. Their technology leverages cutting-edge AI to simplify high-value processes, from multi-turn conversations to full workflow automation. As an ML Engineer within LLMs, you’ll be building and scaling advanced AI systems that power intelligent, multi-agent workflows. You’ll take ownership of designing, fine-tuning, and productionizing large language models, integrating them with backend systems, and optimizing their performance. You’ll collaborate closely with data science, DevOps, and leadership to shape the AI infrastructure that drives the company’s automation solutions. What You’ll Do:Build, fine-tune, and productionize large language model (LLM) pipelines, including PEFT, RLHF, and DPO workflows.Develop APIs, data pipelines, and orchestration systems for multi-agent, multi-turn AI conversations.Integrate models with backend services, including voice orchestration platforms and transcript generation.Optimize model usage and efficiency, transitioning from external APIs to in-house solutions.Collaborate cross-functionally with data scientists, DevOps, and leadership to deliver scalable machine learning solutions. What We’re Looking For:Essential Skills & Experience:Strong proficiency in Python and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).Hands-on experience fine-tuning LLMs (Hugging Face, PEFT).Familiarity with AWS (especially S3 for model management) and deploying ML models to production.Ability to reason deeply about ML principles, architectures, and design choices.Knowledge of multi-agent orchestration and conversational AI systems.Desirable Skills & Experience:Experience with RLHF or preference optimization.Background in voice AI, speech-to-text, or text-to-speech systems.Exposure to financial services or insurance applications.Familiarity with optimizing models for long-context scenarios. If you’d like to hear more, please apply or get in touch!

AI Evaluation Engineer $160,000 - $180,000 Remote (US-based)Are you passionate about shaping how AI is deployed safely, reliably, and at scale? This is a rare opportunity to join a mission-driven tech company as their first AI Evaluation Engineer, a foundational role where you’ll design, build, and own the evaluation systems that safeguard every AI-powered feature before it reaches the real world.This organization builds AI-enabled products that directly helps governments, nonprofits, and agencies deliver financial support to people who need it most. As AI capabilities race forward, ensuring these systems are safe, accurate, and resilient is critical. That’s where you come in.You won’t just be testing models, you’ll be creating the frameworks, pipelines, and guardrails that make advanced LLM features safe to ship. You’ll collaborate with engineers, PMs, and AI safety experts to stress test boundaries, uncover weaknesses, and design scalable evaluation systems that protect end users while enabling rapid innovation. What You’ll DoOwn the evaluation stack – design frameworks that define “good,” “risky,” and “catastrophic” outputs.Automate at scale – build data pipelines, LLM judges, and integrate with CI to block unsafe releases.Stress testing – red team AI systems with challenge prompts to expose brittleness, bias, or jailbreaks.Track and monitor – establish model/prompt versioning, build observability, and create incident response playbooks.Empower others – deliver tooling, APIs, and dashboards that put eval into every engineer’s workflow. Requirements:Strong software engineering background (TypeScript a plus)Deep experience with OpenAI API or similar LLM ecosystemsPractical knowledge of prompting, function calling, and eval techniques (e.g. LLM grading, moderation APIs)Familiarity with statistical analysis and validating data quality/performanceBonus: experience with observability, monitoring, or data science tooling

AI Solutions Engineer / AI Solutions Architect - Visual Generative AI $150,000 - $180,000 New York - 3x days WFH We’re partnered with a pioneering enterprise-grade Visual GenAI platform that has been shaping the responsible AI landscape for over five years. Backed by leading global investors and trusted by some of the world’s largest brands, this company is building the infrastructure for the next generation of safe, scalable, and brand-consistent AI-driven products and services. Now, they’re looking for a Technical AI Solutions Engineer / Solutions Architect to help enterprise customers unlock the full value of their platform. You’ll be the bridge between customers and internal teams, ensuring adoption, ROI, and long-term success.You’ll work hands-on with developers, product managers, and technical decision-makers, helping them integrate cutting-edge generative visual AI into their workflows. Why Join?Be part of a category-defining AI company that’s shaping the future of responsible GenAI.Work with globally recognized enterprise clients across multiple industries.Have a direct impact on product evolution through customer insights.Join a team at the forefront of AI innovation with deep expertise in generative visual technologies. What You’ll Be DoingOwn the post-sales relationship with a portfolio of technical enterprise customers.Guide onboarding, technical enablement, and adoption of the platform.Act as a trusted advisor, helping customers translate technical capabilities into business impact.Work directly with client engineering teams to integrate APIs and platform capabilities.Troubleshoot across the stack (APIs, model behaviour, latency, deployment).Lead workshops, demos, and training sessions for technical stakeholders.Gather and translate customer feedback into product improvements.Collaborate with Product, Engineering, Sales, and Support to advocate for customer needs. Requirements:Must-Haves3–6 years in a SaaS technical customer-facing role (Customer Success Engineer, Solutions Engineer, or Technical Account Manager).Proficiency with API integrations, scripting (Python or JavaScript), and cloud platforms (AWS, GCP, or Azure).Strong grasp of machine learning and generative AI fundamentals, especially computer vision.Ability to translate technical capabilities into business value.Proven success supporting technical customers at a B2B SaaS or AI/ML platform company.Excellent communication, relationship management, and organizational skills.Nice-to-HavesHands-on experience with generative models (diffusion, transformers) or visual AI systems.Familiarity with MLOps workflows, model deployment, or on-device inference.Exposure to creative industries (media, marketing, design).Background in data science, software engineering, or applied AI.

Senior Deep Learning Scientist $160,000 - $250,000 Onsite – New Haven, Connecticut A cutting-edge biotech startup is seeking a Senior Deep Learning Scientist to join their team. This innovative company is pioneering a first-of-its-kind platform to conduct preclinical studies, aiming to revolutionize the understanding and treatment of neurological diseases.As a Senior Deep Learning Scientist, you will play a pivotal role in designing and implementing AI models that integrate complex biological signals. You'll be at the forefront of pioneering work in areas such as generative graph representation learning, contributing to the development of novel AI architectures tailored to the intricacies of human brain biology.Key Responsibilities:Design, develop, and deploy state-of-the-art deep learning models for analyzing multi-modal biological data.Develop deep learning architectures incorporating biological inductive biases, and explore generative graph representation learning to uncover novel patterns in brain data.Work closely with bioinformatics, experimental biology, and engineering teams to integrate multi-modal datasets into cohesive AI frameworks.Optimize deep learning pipelines for petabyte-scale datasets and ensure models are scalable on high-performance computing infrastructures.Publish research findings and present at scientific conferences to contribute to the broader AI and biomedical communities. Requirements:PhD or Post-doc in Computer Science, Machine Learning, or a related STEM field with a strong demonstrated track record of applying deep learning to biological problems.The ability to translate conceptual research frameworks into deployable architectures.Comfort working across research and applied implementation.Proven experience with GNNs and an ability to tailor these methods to biological data. Experience with generative graph representation learning is a significant plus.Expertise in PyTorch with the ability to build and deploy scalable models.Familiarity with developing production-quality pipelines, cloud computing, and model deployment best practices.Demonstrated ability to research and implement novel deep learning architectures tailored to complex biological datasets.Experience with high-performance computing (HPC) environments or distributed training techniques for large-scale GNN models. Apply now or reach out to Ben at benjamin@deeprec.ai to learn more!