Interested in this AI/ML Engineer role at Sapient Bioanalytics?
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About This Role
Sapient is a fast-growing early-stage biotech company headquartered in San Diego, CA. Our focus is on delivering multi-omics services to accelerate drug development and transform personalized medicine. Combining state-of-the-art mass spectrometry technology with advanced biocomputational services and multifaceted human health data, we enable biomarker discovery at unprecedented speed and scale to provide a deeper understanding of human biology and effectively align biological targets, patients, and drug therapies. We are a highly collaborative team of passionate scientists, clinicians, and engineers with expertise in various disciplines, committed to making innovations with scientific rigor. Ultimately, our platform technology enhances the precision and efficacy of treatments, touching and enhancing countless lives. About the Role
We are seeking a motivated and detail-oriented AI Demand Generation & Outreach Specialist to join our growing commercial team. This position offers an excellent opportunity to develop diverse skills in marketing and sales development within the life sciences industry as part of an innovative company “going beyond the genome” to advance precision medicine and ultimately improve patients’ lives. As a rapidly growing start-up, we value team members who can function highly in a fast-paced environment and who think and work well independently. Exceptional opportunity to work closely with top commercial and scientific personnel with high opportunity for career development and growth. This position is remote.
Job Description
- Build and run AI-assisted outbound campaigns that generate qualified meetings for the CSO and business development team across Sapient's discovery proteomics, metabolomics, targeted protein assay and FFPE tumor proteomics services.
- Map each service line to the right scientific personas and develop clear, specific messaging that earns replies from translational medicine, biomarker, clinical development and target discovery leaders at pharma and biotech companies.
- Build targeted prospect lists for conferences and campaigns from speaker programs, exhibitor lists, attendee data and professional networks, screening each list against CRM history and do-not-contact lists.
- Write and edit outreach copy, web content and case studies, partnering with the science team to ensure every technical claim is accurate and supported.
- Quality-control every campaign before launch and manage sequences, schedules, sender mailboxes and test sends in our sending platform.
- Own campaign responses: qualify replies, answer straightforward questions, book meetings and follow through until each conversation lands.
- Improve how Sapient is found online through search optimization, structured and machine-readable content, and generative engine optimization so AI assistants surface and accurately describe our services.
- Set up tracking and attribution before launch, publish the monthly outreach performance dashboard and maintain the campaign calendar.
- Test subject lines, messaging angles, sequence length and send timing, and turn results into recommendations on who and how we target next.
- Support conference and trade show presence and event follow-up campaigns, keeping brand messaging consistent across email, decks and web.
- Maintain accurate records of contacts, responses and qualified leads within our CRM system (Dynamics).
- Contribute improvements to the outreach pipeline, including script, prompt and documentation updates, and document lessons learned as repeatable practices.
Requirements
- BS or BA in a life science field (biology, biochemistry, bioengineering or similar), or equivalent practical exposure to the life science industry.
- 1 to 3 years' experience in marketing, sales development, communications or another commercial role in life sciences.
- Fluent, daily use of AI assistants such as Claude or ChatGPT, with the discipline to verify output against the source.
- Comfortable with technology, including running scripts, editing CSV and configuration files, and using git and GitHub. Formal software engineering experience is not required.
- Working knowledge of how drug development is organized and what translational and biomarker teams care about.
- Strong marketing and commercial instinct: able to judge why a message lands and whether a prospect has a real need, budget and reason to engage.
- Clear, concise writing and confidence communicating with senior scientists by email and phone.
- Highly detail oriented with strong follow-through; checks rather than assumes.
- Resilient, self-motivated and comfortable with ambiguity in a fast-paced environment.
- Sound judgment handling confidential prospect data, CRM exports and executive sending accounts.
- Spreadsheet fluency (Excel or Google Sheets) and working knowledge of SEO fundamentals. Interest in generative engine optimization is a plus.
- Experience with campaign or prospecting platforms (e.g., HubSpot, Smartlead, Apollo, Clay, LinkedIn Sales Navigator), analytics tools, a CMS, a CRM or Python is preferred but not required.
Position Details
- Position Type: Full-time.
- Compensation: $70,000 to $95,000 base depending on experience, plus a $10,000 variable component tied to qualified meetings created.
Sapient is an equal opportunity employer. We value diversity, inclusivity and belonging of all people from different cultures and socioeconomic backgrounds. We do not discriminate based on race, ethnicity, gender, age, sex, disability or any other secondary biases.
The pay range for this role is:
70,000 - 95,000 USD per year(Remote (United States))
Salary Context
This $70K-$95K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Sapient Bioanalytics, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($82K) sits 61% below the category median. Disclosed range: $70K to $95K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Sapient Bioanalytics AI Hiring
Sapient Bioanalytics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $95K - $95K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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