Senior Machine Learning Engineer

About the role

You will design, build, tune, and evaluate production-grade multi-agentic systems that guide people through complex, high-stakes conversations. These systems combine multi-step agent orchestration, retrieval, memory, rigorous evaluation, and safety layers. This is an applied-ML and LLM-systems role focused on agent behavior, model selection, retrieval quality, and evaluation.

Responsibilities

• Design and orchestrate multi-agentic workflows.
• Own context engineering for production agents, including system design, safety rules, context injection, and clarifying question strategies.
• Design tools and function-calling interfaces so agents take reliable, well-structured actions.
• Build and tune retrieval (RAG) pipelines—embeddings, vector search, filtering, query rewriting, and relevance tuning.
• Select and optimize models across providers, balancing quality, latency, determinism, and cost.
• Design agent memory and context management for coherent multi-turn behavior.
• Build safety and guardrail layers for input filtering, scope and safety checks, and graceful handling of edge cases.
• Own LLM evaluation, offline eval suites, graders/LLM-as-judge, test sets and personas, metrics, and quality gates.
• Collaborate with cross-functional stakeholders on requirements, project execution, and status tracking.
• Document high-fidelity technical designs and establish alignment on solutions within the broader engineering team.

Requirements

• Bachelor's or master's degree in data science, Machine Learning Engineering, or a related technical field, or equivalent practical experience.
• 5+ years of professional Data Science/ML engineering experience.
• Strong applied experience building LLM-powered agents in production—shipped, multi-turn agentic systems, not just prompt experiments.
• Hands-on expertise with agent orchestration frameworks—stateful graphs, tool use, and conditional routing.
• Deep understanding of context engineering and tool/function-calling design for reliable agent behavior.
• Practical RAG experience—embeddings, vector search, and retrieval-quality tuning.
• Fluency with LLM model selection and tuning across providers, including reasoning models and their trade-offs.
• Experience designing LLM evaluation—offline eval, graders, test sets, metrics, and quality gates.
• Comfort with agent observability and tracing to diagnose and improve behavior.
• Strong Python skills as applied to ML/agent work.

Nice to have

• Experience with agent memory systems.
• Experience with the LangChain suite.
• Experience building safety guardrails for high-stakes domains (clinical, financial, legal).
• Experience optimizing LLM latency, cost, and reliability at scale.
• Experience with building and working with MCPs and loop engineering.
• Prompt optimization techniques such as GEPA.
• Working with sensitive data in regulated environments.

About the company

Transcarent is the One Place for Health and Careᵀᴹ, bringing medical, pharmacy, and point solutions together with the WayFindingᵀᴹ experience, the first and only generative AI-powered health and care platform for health consumers. Our WayFinding experience, paired with transparent and consumer-driven pharmacy care, 2nd.MD expert medical opinions, and virtual primary care, works seamlessly with comprehensive Care Experiences—Cancer Care, Surgery Care, and Weight Health—to support people with all of their health needs, simple or serious. More than 1,700 employers and health plans rely on us to provide information, guidance, and care, empowering health consumers with more choice, an experience they love, access to higher-quality care, and lower costs for 21 million Members.

Company values

• People First: We prioritize our Members, clients, and each other in every decision.
• Care: Every decision starts with improving health and care for our Members.
• Resilience: We push boundaries and take the uncharted path to change an industry.
• Results: We take ownership, solve with speed, and deliver for our people and each other.
• Humble and Human: We lead with humility, bring fun to tough moments, and go further together.

Pay

Individual compensation packages are based on factors unique to each candidate, including primary work location and an evaluation of skills, experience, market demands, and internal equity.

Benefits

• Competitive medical, dental, and vision coverage.
• Competitive 401(k) Plan with a generous company match.
• Flexible Time Off/Paid Time Off, 13 paid holidays.
• Protection Plans including Life Insurance, Disability Insurance, and Supplemental Insurance.
• Mental Health and Wellness benefits.
• Corporate bonus program or sales incentive (target included in OTE).
• Stock options.