Who we are
Transcarent is the One Place for Health and Careᵀᴹ, bringing medical, pharmacy, and point solutions together with the WayFinding ᵀᴹ 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. For more information, visit transcarent.com , and follow us on LinkedIn .
We are looking for teammates to join us in building our company, culture, and Member experience who:
- Put people first, and make decisions with the Member’s best interests in mind
- Are active learners, constantly looking to improve and grow
- Are driven by our mission to measurably improve health and care each day
- Bring the energy needed to transform health and care, and move and adapt rapidly
Are laser focused on delivering results for Members, and proactively problem solving to get there
About This Role
As an ML Engineer, you build production grade multi agentic systems that guide people through complex, high-stakes conversations. Our systems combine multi-step agent orchestration, retrieval, memory, and rigorous evaluation and safety layers.
We're looking for an ML Engineer to design, build, tune, and evaluate these agentic systems end to end: from contex t engineering and tool design, through retrieval and memory, to evaluation and safety guardrails. This is an applied-ML and LLM-systems role focused on agent behavior, model selection, retrieval of quality, and evaluation. Though understanding of AI/ML is crucial for this role, we kindly request that you refrain from using GenAI while going through the interview process to allow fair evaluation of your skillset.
What you’ll do
- Design and orchestrate multi-agentic workflows .
- Own context engineering for production agents, including system design, safety rules, context injection, and clarifying question strategies.
- Design tool s 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, balance 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.
- Meta technical responsibility: Document high-fidelity technical designs, establish alignment on solutions within broader engineering team.
What we’re looking for
- Bachelor's or master's degree in data science , Machine Learning Engineering, or a related technical field, or equivalent practical experience.
- 3+ 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 LangChain suite.
- Experience building safety guardrails for high-stakes domains (clinical, financial, legal).
- Experience optimizing LLM latency, cost, and reliability at scale.
- E xperience with building and working with MCPs and loop engineering .
- Prompt optimization techniques such as GEPA .
Individual compensation packages are based on a few different factors unique to each candidate, including primary work location and an evaluation of a candidate's skills, experience, market demands, and internal equity.
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- T otal Rewards
Salary is just one component of Transcarent's total package. All regular employees are also eligible for the corporate bonus program or a sales incentive (target included in OTE) as well as stock options.
Our benefits and perks programs include, but are not limited to:
- 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
Transcarent is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. If you are a person with a disability and require assistance during the application process, please don't hesitate to reach out!
Research shows that candidates from underrepresented backgrounds often don't apply unless they meet 100% of the job criteria. While we have worked to consolidate the minimum qualifications for each role, we aren't looking for someone who checks each box on a page; we're looking for active learners and people who care about disrupting the current health and care with their unique experiences.